Elysium Technologies Private...

Elysium Technologies Private Limited Singapore | Madurai | Chennai | Trichy | Coimbatore | Ramnad

Pondicherry | Salem | Erode | Tirunelveli

http://www.elysiumtechnologies.com, [email protected]

http://www.elysiumtechnologies.com/


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ETPL NT-001 Answering “What-If” Deployment and Configuration Questions With WISE: Techniques and Deployment Experience

ETPL NT-002 Complexity Analysis and Algorithm Design for Advance Bandwidth Scheduling in Dedicated Networks

ETPL NT-003 Diffusion Dynamics of Network Technologies With Bounded Rational Users: Aspiration-Based Learning

ETPL NT-004 Delay-Based Network Utility Maximization

ETPL NT-005 A Distributed Control Law for Load Balancing in Content Delivery Networks

ETPL NT-006 Efficient Algorithms for Neighbor Discovery in Wireless Networks

ETPL NT-007 Stochastic Game for Wireless Network Virtualization

ETPL NT-008 ABC: Adaptive Binary Cuttings for Multidimensional Packet Classification,

ETPL NT-009 A Utility Maximization Framework for Fair and Efficient Multicasting in Multicarrier Wireless Cellular Networks

ETPL NT-010 Achieving Efficient Flooding by Utilizing Link Correlation in Wireless Sensor Networks,

ETPL NT-011 Random Walks and Green's Function on Digraphs: A Framework for Estimating Wireless Transmission Costs

ETPL NT-012 "A Flexible Platform for Hardware-Aware Network Experiments and a Case Study on Wireless Network Coding

ETPL NT-013 Exploring the Design Space of Multichannel Peer-to-Peer Live Video Streaming Systems

ETPL NT-014 Secondary Spectrum Trading—Auction-Based Framework for Spectrum Allocation and Profit Sharing

ETPL NT-015 Towards Practical Communication in Byzantine-Resistant DHTs

ETPL NT-016 Semi-Random Backoff: Towards Resource Reservation for Channel Access in Wireless LANs

ETPL NT-017 Entry and Spectrum Sharing Scheme Selection in Femtocell Communications Markets

ETPL NT-018 On Replication Algorithm in P2P VoD,

ETPL NT-019 Back-Pressure-Based Packet-by-Packet Adaptive Routing in Communication Networks

ETPL NT-020 Scheduling in a Random Environment: Stability and Asymptotic Optimality

ETPL NT-021 An Empirical Interference Modeling for Link Reliability Assessment in Wireless Networks

ETPL NT-022 On Downlink Capacity of Cellular Data Networks With WLAN/WPAN Relays

ETPL NT-023 Centralized and Distributed Protocols for Tracker-Based Dynamic Swarm Management

ETPL NT-024 Localization of Wireless Sensor Networks in the Wild: Pursuit of Ranging Quality

ETPL NT-025 Control of Wireless Networks With Secrecy

ETPL NT-026 ICTCP: Incast Congestion Control for TCP in Data-Center Networks

ETPL NT-027 Context-Aware Nanoscale Modeling of Multicast Multihop Cellular Networks

ETPL NT-028 Moment-Based Spectral Analysis of Large-Scale Networks Using Local Structural Information

ETPL NT-029 Internet-Scale IPv4 Alias Resolution With MIDAR

ETPL NT-030 Time-Bounded Essential Localization for Wireless Sensor Networks

ETPL NT-031 Stability of FIPP -Cycles Under Dynamic Traffic in WDM Networks

ETPL NT-032 Cooperative Carrier Signaling: Harmonizing Coexisting WPAN and WLAN Devices

ETPL NT-033 Mobility Increases the Connectivity of Wireless Networks

ETPL NT-034 Topology Control for Effective Interference Cancellation in Multiuser MIMO Networks

ETPL NT-035 Distortion-Aware Scalable Video Streaming to Multinetwork Clients

ETPL NT-036 Combined Optimal Control of Activation and Transmission in Delay-Tolerant Networks

ETPL NT-037 A Low-Complexity Congestion Control and Scheduling Algorithm for Multihop Wireless















Modern scientific data management and analysis usually rely on multiple scientists with diverse expertise. In

recent years, such a collaborative effort is often structured and automated by a data flow-oriented process

called scientific workflow. However, such workflows may have to be designed and revised among multiple

scientists over a long time period. Existing workbenches are single user-oriented and do not support scientific

workflow application development in a "collaborative fashion". In this paper, we report our research on the

enabling techniques in the aspects of collaboration provenance management and reproduciability. Based on a

scientific collaboration ontology, we propose a service-oriented collaboration model supported by a set of

composable collaboration primitives and patterns. The collaboration protocols are then applied to support

effective concurrency control in the process of collaborative workflow composition. We also report the design

and development of Confucius, a service-oriented collaborative scientific workflow composition tool that

extends an open-source, single-user development environment.

ETPL

SER - 001

Confucius: A Tool Supporting Collaborative Scientific Workflow Composition

Web service composition (WSC) is the task of combining a chain of connected single services together to

create a more complex and value-added composite service. Quality of service (QoS) has been mostly applied

to represent nonfunctional properties of web services and differentiate those with the same functionality. Many

research has been done on QoS-aware service composition, as it significantly affects the quality of a

composite service. However, existing methods are restricted to predefined workflows, which can incur a

couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the

completeness of finding a composite service solution. In this paper, instead of predefining a workflow model

for service composition, we propose a novel planning-based approach that can automatically convert a QoS-

aware composition task to a planning problem with temporal and numerical features. Furthermore, we use

state-of-the-art planners, including an existing one and a self-developed one, to handle complex temporal

planning problems with logical reasoning and numerical optimization. Our approach can find a composite

service graph with the optimal overall QoS value while satisfying multiple global QoS constraints. We

implement a prototype system and conduct extensive experiments on large web service repositories. The

experimental results show that our proposed approach largely outperforms existing ones in terms of solution

quality and is efficient enough for practical deployment.

ETPL

SER - 002

QoS-Aware Dynamic Composition of Web Services Using Numerical Temporal

Planning








Quality of service (QoS) is widely employed for describing nonfunctional characteristics of web services.

Although QoS of web services has been investigated intensively in the field of service computing, there is a

lack of real-world web service QoS data sets for validating various QoS-based techniques and models. To

investigate QoS of real-world web services and to provide reusable research data sets for future research, we

conduct several large-scale evaluations on real-world web services. First, addresses of 21,358 web services are

obtained from the Internet. Then, three large-scale real-world evaluations are conducted. In our evaluations,

more than 30 million real-world web service invocations are conducted on web services in more than 80

countries by users from more than 30 counties. Detailed evaluation results are presented in this paper and

comprehensive web service QoS data sets are publicly released online.

ETPL

SER - 003

Investigating QoS of Real-World Web Services

Advancements in cloud computing enable the easy deployment of numerous services. However, the analysis

of cloud service access platforms from a client perspective shows that maintaining and managing clients

remain a challenge for end users. In this paper, we present the design, implementation, and evaluation of an

asymmetric virtual machine monitor (AVMM), which is an asymmetric partitioning-based bare-metal

approach that achieves near-native performance while supporting a new out-of-operating system mechanism

for value-added services. To achieve these goals, AVMM divides underlying platforms into two asymmetric

partitions: a user partition and a service partition. The user partition runs a commodity user OS, which is

assigned to most of the underlying resources, maintaining end-user experience. The service partition runs a

specialized OS, which consumes only the needed resources for its tasks and provides enhanced features to the

user OS. AVMM considerably reduces virtualization overhead through two approaches: 1) Peripheral devices,

such as graphics equipment, are assigned to be monopolized by a single user OS. 2) Efficient resource

management mechanisms are leveraged to alleviate complicated resource sharing in existing virtualization

technologies. We implement a prototype that supports Windows and Linux systems. Experimental results

show that AVMM is a feasible and efficient approach to client virtualization.

ETPL

SER - 004 A Bare-Metal and Asymmetric Partitioning Approach to Client Virtualization








This paper addresses automatic service composition (ASC) as a means to create new value-added services

dynamically and automatically from existing services in service-oriented architecture and cloud computing

environments. Manually composing services for relatively static applications has been successful, but

automatically composing services requires advances in the semantics of processes and an architectural

framework that can capture all stages of an application's lifecycle. A framework for ASC involves four stages:

planning an execution workflow, discovering services from a registry, selecting the best candidate services,

and executing the selected services. This four-stage architecture is the most widely used to describe ASC, but

it is still abstract and incomplete in terms of scalable goal composition, property transformation for seamless

automatic composition, and integration architecture. We present a workflow orchestration to enable nested

multilevel composition for achieving scalability. We add to the four-stage composition framework a

transformation method for abstract composition properties. A general model for the composition architecture

is described herein and a complete and detailed composition framework is introduced using our model. Our

ASC architecture achieves improved seamlessness and scalability in the integrated framework. The ASC

architecture is analyzed and evaluated to show its efficacy.

ETPL

SER - 006

A Scalable Architecture for Automatic Service Composition

Location-based services (LBS) are widely deployed. When the implementation of an LBS-enabled service has

evolved, regression testing can be employed to assure the previously established behaviors not having been

adversely affected. Proper test case prioritization helps reveal service anomalies efficiently so that fixes can be

scheduled earlier to minimize the nuisance to service consumers. A key observation is that locations captured

in the inputs and the expected outputs of test cases are physically correlated by the LBS-enabled service, and

these services heuristically use estimated and imprecise locations for their computations, making these

services tend to treat locations in close proximity homogenously. This paper exploits this observation. It

proposes a suite of metrics and initializes them to demonstrate input-guided techniques and point-of-interest

(POI) aware test case prioritization techniques, differing by whether the location information in the expected

outputs of test cases is used. It reports a case study on a stateful LBS-enabled service. The case study shows

that the POI-aware techniques can be more effective and more stable than the baseline, which reorders test

cases randomly, and the input-guided techniques. We also find that one of the POI-aware techniques, cdist, is

either the most effective or the second most effective technique among all the studied techniques in our

evaluated aspects, although no technique excels in all studied SOA fault classes.

A Technique for Deploying Robust Web Services

Developing robust web services is a difficult task. Field studies show that a large number of web services are

deployed with robustness problems (i.e., presenting unexpected behaviors in the presence of invalid inputs).

Although several techniques for the identification of robustness problems have been proposed in the past, there

is no practical approach to automatically fix those problems. This paper proposes a mechanism that

automatically fixes robustness problems in web services. The approach consists of using robustness testing to

detect robustness issues and then mitigate those issues by applying inputs verification based on well-defined

parameter domains, including domain dependencies between different parameters. This integrated and fully

automated methodology has been used to improve three different implementations of the TPC-App web

services and several services publicly available on the Internet. Results show that the proposed approach can

be easily used to improve the robustness of web services code.

ETPL

SER - 005

Prioritizing Test Cases for Regression Testing of Location-Based Services: Metrics,

Techniques, and Case Study








Managing virtualized services efficiently over the cloud is an open challenge. Traditional models of software

development are not appropriate for the cloud computing domain, where software (and other) services are

acquired on demand. In this paper, we describe a new integrated methodology for the life cycle of IT services

delivered on the cloud and demonstrate how it can be used to represent and reason about services and service

requirements and so automate service acquisition and consumption from the cloud. We have divided the IT

service life cycle into five phases of requirements, discovery, negotiation, composition, and consumption. We

detail each phase and describe the ontologies that we have developed to represent the concepts and

relationships for each phase. To show how this life cycle can automate the usage of cloud services, we

describe a cloud storage prototype that we have developed. This methodology complements previous work on

ontologies for service descriptions in that it is focused on supporting negotiation for the particulars of a service

and going beyond simple matchmaking.

ETPL

SER - 008

Automating Cloud Services Life Cycle through Semantic Technologies

We address the problem of usage license administration in federated settings. This problem arises whenever

organizations, such as educational or research groups or institutions, share resources for business and scientific

reasons. In such settings, each user's usage of a licensed resource is typically supported by the user's

organization. License administration involves satisfying legal requirements while applying organizational

strategies for effective resource usage, and carrying out suitable accounting and audit controls. We propose an

approach, Licit, wherein an agent represents each resource sharing site and administers licenses in

collaboration with other agents. We show how to represent a variety of usage licenses formally as executable

policies and provide a simple information model using which each party can specify both the attributes

involved in its licenses and how to resolve them. Our architecture naturally accommodates a variety of site-

specific (i.e., custom) strategies for license administration. Licit has been implemented in a popular open

source framework for virtual computing, and yields performance results indicating its practical feasibility.

ETPL

SER - 007

Licit: Administering Usage Licenses in Federated Environments








Remote data integrity checking is of crucial importance in cloud storage. It can make the clients verify

whether their outsourced data is kept intact without downloading the whole data. In some application

scenarios, the clients have to store their data on multi-cloud servers. At the same time, the integrity checking

protocol must be efficient in order to save the verifier’s cost. From the two points, we propose a novel remote

data integrity checking model: ID-DPDP (identity-based distributed provable data possession) in multi-cloud

storage. The formal system model and security model are given. Based on the bilinear pairings, a concrete ID-

DPDP protocol is designed. The proposed ID-DPDP protocol is provably secure under the hardness

assumption of the standard CDH (computational Diffie- Hellman) problem. In addition to the structural

advantage of elimination of certificate management, our ID-DPDP protocol is also efficient and flexible.

Based on the client’s authorization, the proposed ID-DPDP protocol can realize private verification, delegated

verification and public verification.

ETPL

SER - 009

Identity-Based Distributed Provable Data Possession in Multi-Cloud Storage

In an uncertain and changing environment, a composite service needs to continuously optimize its business

process and service selection through runtime adaptation. To achieve the overall satisfaction of stakeholder

requirements, quality tradeoffs are needed to adapt the composite service in response to the changing

environments. Existing approaches on service selection and composition, however, are mostly based on

quality preferences and business processes decisions made statically at the design time. In this paper, we

propose a requirements-driven self-optimization approach for composite services. It measures the quality of

services (QoS), estimates the earned business value, and tunes the preference ranks through a feedback loop.

The detection of unexpected earned business value triggers the proposed self-optimization process

systematically. At the process level, a preference-based reasoner configures a requirements goal model

according to the tuned preference ranks of QoS requirements, reconfiguring the business process according to

its mappings from the goal configurations. At the service level, selection decisions are optimized by utilizing

the tuned weights of QoS criteria. We used an experimental study to evaluate the proposed approach. Results

indicate that the new approach outperforms both fixed-weighted and floating-weighted service selection

approaches with respect to earned business value and adaptation flexibility.

ETPL

SER - 010

Requirements-Driven Self-Optimization of Composite Services using Feedback

Control








We investigate the problem of minimizing the sum of the queue lengths of all the nodes in a wireless network

with a forest topology. Each packet is destined to one of the roots (sinks) of the forest. We consider a time-

slotted system and a primary (or one-hop) interference model. We characterize the existence of causal sample-

path optimal scheduling policies for this network topology under this interference model. A causal sample-

path optimal scheduling policy is one for which at each time-slot, and for any sample-path traffic arrival

pattern, the sum of the queue lengths of all the nodes in the network is minimum among all policies. We show

that such policies exist in restricted forest structures, and that for any other forest structure, there exists a

traffic arrival pattern for which no causal sample-path optimal policy can exist. Surprisingly, we show that

many forest structures for which such policies exist can be scheduled by converting the structure into an

equivalent linear network and scheduling the equivalent linear network according to the one-hop interference

model. The nonexistence of such policies in many forest structures underscores the inherent limitation of

using sample-path optimality as a performance metric and necessitates the need to study other (relatively)

weaker metrics of delay performance.

ETPL

NW - 011

On Sample-Path Optimal Dynamic Scheduling for Sum-Queue Minimization in

Forest

Selecting a vulnerability detection tool is a key problem that is frequently faced by developers of security-

critical web services. Research and practice shows that state-of-the-art tools present low effectiveness both in

terms of vulnerability coverage and false positive rates. The main problem is that such tools are typically

limited in the detection approaches implemented, and are designed for being applied in very concrete

scenarios. Thus, using the wrong tool may lead to the deployment of services with undetected vulnerabilities.

This paper proposes a benchmarking approach to assess and compare the effectiveness of vulnerability

detection tools in web services environments. This approach was used to define two concrete benchmarks for

SQL Injection vulnerability detection tools. The first is based on a predefined set of web services, and the

second allows the benchmark user to specify the workload that best portrays the specific characteristics of his

environment. The two benchmarks are used to assess and compare several widely used tools, including four

penetration testers, three static code analyzers, and one anomaly detector. Results show that the benchmarks

accurately portray the effectiveness of vulnerability detection tools (in a relative manner) and suggest that the

proposed benchmarking approach can be applied in the field.

ETPL

SER - 012

Assessing and Comparing Vulnerability Detection Tools for Web Services:

Benchmarking Approach and Examples








The widespread use of web services in forming complex online applications requires service composition to

cope with highly dynamic and heterogeneous environments. Traditional centralized service composition

techniques are not sufficient to address the needs of applications in decentralized environments. In this paper,

a stigmergic-based approach is proposed to model the decentralized service interactions and handle service

composition in highly dynamic open environments. In the proposed approach, web services and resources are

modeled as multiple agents. Stigmergic-based self-organization mechanisms among agents are deployed to

facilitate adapting service composition. In addition, to overcome the limitations of traditional QoS-based

approaches, trust measurements are deployed as a criterion for service selection. To improve the performance

of the proposed stigmergic-based approach under dynamic scale-free environments, we investigate the

hybridization with local search operators to consolidate adaptation, and diversity schemes are introduced to

facilitate continual service adaptation. Extensive experiments show the efficiency of the proposed approach in

dealing with incomplete information and dynamic factors in composing and adapting web services in open

environments. The experiment results also show that the proposed approach achieves a better performance

than other traditional approaches.

ETPL

SER - 013

Trustworthy Stigmergic Service Composition and Adaptation in Decentralized

Environments

Social network platforms have rapidly changed the way that people communicate and interact. They have

enabled the establishment of, and participation in, digital communities as well as the representation,

documentation and exploration of social relationships. We believe that as ‘apps’ become more sophisticated, it

will become easier for users to share their own services, resources and data via social networks. To

substantiate this, we present a Social Compute Cloud where the provisioning of Cloud infrastructure occurs

through “friend” relationships. In a Social Compute Cloud, resource owners offer virtualized containers on

their personal computer(s) or smart device(s) to their social network. However, as users may have complex

preference structures concerning with whom they do or do not wish to share their resources, we investigate,

via simulation, how resources can be effectively allocated within a social community offering resources on a

best effort basis. In the assessment of social resource allocation, we consider welfare, allocation fairness, and

algorithmic runtime. The key findings of this work illustrate how social networks can be leveraged in the

construction of cloud computing infrastructures and how resources can be allocated in the presence of user

sharing preferences.

ETPL

SER - 014

A Social Compute Cloud: Allocating and Sharing Infrastructure Resources via Social

Networks








Service Level Agreements (SLAs) are typically used to specify rules regarding the consumption of services

that are agreed between the providers of the Service-Based Applications (SBAs) and their consumers. An SLA

includes a list of terms that contain the guarantees that must be fulfilled during the provisioning and

consumption of the services. Since the violation of such guarantees may lead to the application of potential

penalties, it is important to assure that the SBA behaves as expected. In this article, we propose a proactive

approach to test SLA-aware SBAs by means of identifying test requirements, which represent situations that

are relevant to be tested. To address this issue, we define a four-valued logic that allows evaluating both the

individual guarantee terms and their logical relationships. Grounded in this logic, we devise a test criterion

based on the Modified Condition Decision Coverage (MCDC) in order to obtain a cost-effective set of test

requirements from the structure of the SLA. Furthermore by analyzing the syntax and semantics of the

agreement, we define specific rules to avoid non-feasible test requirements. The whole approach has been

automated and applied over an eHealth case study.

ETPL

SER - 015

Coverage-based testing for Service Level Agreements

Recently, there is a trend on developing mobile applications based on service-oriented architecture in

numerous application domains, such as telematics and smart home. Although efforts have been made on

developing composite SOAP services, little emphasis has been put on invoking and composing a combination

of SOAP, non-SOAP, and non-web services into a composite process to execute complex tasks on various

mobile devices. Main challenges are two-fold: one is how to invoke and compose heterogeneous web services

with various protocols and content types, including SOAP, RESTful, and OSGi services; and the other is how

to integrate non-web services, like Web contents and mobile applications, into a composite service process. In

this work, we propose an approach to invoking and composing SOAP, non-SOAP, and non-web services with

two key features: an extended BPEL engine bundled with adapters to enable direct invocation and composition

of SOAP, RESTful and OSGi services based on Adapter pattern; and two transformation mechanisms devised

to enable conversion of Web contents and Android activities into OSGi services. In the experimental

evaluations, we demonstrate network traffic and turnaround time of our approach are better than those of the

traditional ones.

ETPL

SER - 016

A Framework for Composing SOAP, Non-SOAP and Non-Web Services








Web service recommendation systems can help service users to locate the right service from the large number

of available Web services. Avoiding recommending dishonest or unsatisfactory services is a fundamental

research problem in the design of Web service recommendation systems. Reputation of Web services is a

widely-employed metric that determines whether the service should be recommended to a user. The service

reputation score is usually calculated using feedback ratings provided by users. Although the reputation

measurement of Web service has been studied in the recent literature, existing malicious and subjective user

feedback ratings often lead to a bias that degrades the performance of the service recommendation system. In

this paper, we propose a novel reputation measurement approach for Web service recommendations. We first

detect malicious feedback ratings by adopting the Cumulative Sum Control Chart, and then we reduce the

effect of subjective user feedback preferences employing the Pearson Correlation Coefficient. Moreover, in

order to defend malicious feedback ratings, we propose a malicious feedback rating prevention scheme

employing Bloom filtering to enhance the recommendation performance. Extensive experiments are conducted

by employing a real feedback rating dataset with 1.5 million Web service invocation records. The

experimental results show that our proposed measurement approach can reduce the deviation of the reputation

measurement and enhance the success ratio of the Web service recommendation.

ETPL

SER - 017

Reputation Measurement and Malicious Feedback Rating Prevention in Web Service

Recommendation Systems








Many web computing systems are running real time database services where their information change

continuously and expand incrementally. In this context, web data services have a major role and draw

significant improvements in monitoring and controlling the information truthfulness and data propagation.

Currently, web telemedicine database services are of central importance to distributed systems. However, the

increasing complexity and the rapid growth of the real world healthcare challenging applications make it hard

to induce the database administrative staff. In this paper, we build an integrated web data services that satisfy

fast response time for large scale Tele-health database management systems. Our focus will be on database

management with application scenarios in dynamic telemedicine systems to increase care admissions and

decrease care difficulties such as distance, travel, and time limitations. We propose three-fold approach based

on data fragmentation, database web sites clustering and intelligent data distribution. This approach reduces

the amount of data migrated between web sites during applications’ execution; achieves cost-effective

communications during applications’ processing and improves applications’ response time and throughput.

The proposed approach is validated internally by measuring the impact of using our computing services’

techniques on various performance features like communications cost, response time, and throughput. The

external validation is achieved by comparing the performance of our approach to that of other techniques in

the literature. The results show that our integrated approach significantly improves the performance of web

database systems and outperforms its counterparts.

ETPL

SER - 018

Designing High Performance Web-Based Computing Services to Promote Telemedicine

Database Management System

this paper introduces a testing strategy that is suitable for testing service-based applications. We describe an

architecture that responds to changes of service operation, operation arguments and service composition

changes. Our proof-of-concept test system performs runtime testing on our model atomic and composite web

services using a random testing technique. A novel change identification method was developed to capture

changes at the service interface. The test system is able to identify changes that occur in service operations and

operational arguments in a service description of a test candidate. Our approach uses a new method to detect

changes in a service inventory. Automated reconfiguration is used to support the continuous operation of the

testing systems during a test candidate change

ETPL

SER - 019

Dynamic Test Reconfiguration for Composite Web Services








Software cohesion concerns the degree to which the elements of a module belong together. Cohesive software

is easier to understand, test and maintain. In the context of service-oriented development, cohesion refers to

the degree to which the operations of a service interface belong together. In the state of the art, software

cohesion is improved based on refactoring methods that rely on information, extracted from the software

implementation. This is a main limitation towards using these methods in the case of Web services: Web

services do not expose their implementation; instead all that they export is the Web service interface

specification. To deal with this problem, we propose an approach that enables the cohesiondriven

decomposition of service interfaces, without information on how the services are implemented. Our approach

progressive decomposes a given service interface into more cohesive interfaces; the backbone of the approach

is a suite of cohesion metrics that rely on information, extracted solely from the specification of the service

interface. We validate the approach in 22 real-world services, provided by Amazon and Yahoo. We assess the

effectiveness of the proposed approach, concerning the cohesion improvement, and the number of interfaces

that result from the decomposition of the examined interfaces. Moreover, we show the usefulness of the

approach in a user study, where developers assessed the quality of the produced interfaces.

ETPL

SER - 020

Cohesion-Driven Decomposition of Service Interfaces Without Access to Source Code

Composite services are widely popular for solving complex problems where the required QoS levels are often

demanding. The composite service that provides the best utility while meeting the QoS requirements has to be

found. This paper proposes a network model where many complementary candidates could be selected for

each service class to improve the benefits, while the conventional model limits the selection to a single service

candidate or service level per service class. The selection of services step is NP-hard because it can be reduced

to a multi-constraint knapsack problem. Yet, the decision has to be reached rapidly so that it does not increase

the overall workflow time. Large-size networks and problems with high restriction levels (strong QoS

requirements) are the most problematic. Traditional Multiple-Constrained-Shortest-Path (MCSP) heuristics are

improved in this paper using the novel concept “potential feasibility”. When our modified MCSP heuristic

algorithms are compared to the CPLEX solver, one of them demonstrates a significantly smaller average

runtime. Further, it provides solutions within a 2.6% optimality gap on average for small networks, and a 10%

optimality gap on average for large networks, regardless of the restriction level. Our algorithm uses a general

utility function, not derived from the QoS parameters.

ETPL

SER - 021

Network and QoS-based Selection of Complementary Services








The convergence of Services Computing and Web 2.0 gains a large space of opportunities to compose

$“situational”$ web applications from Web-delivered services. However, the large number of services and the

complexity of composition constraints make manual composition difficult to application developers, who

might be non-professional programmers or even end-users. This paper presents a systematic data-driven

approach to assisting situational application development. We first propose a technique to extract useful

information from multiple sources to abstract service capabilities with a set tags. This supports intuitive

expression of user’s desired composition goals by simple queries, without having to know underlying

technical details. A planning technique then exploits composition solutions which can constitute the desired

goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates

visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A

series of experiments demonstrate the efficiency and effectiveness of our approach.

ETPL

SER - 022

Data-Driven Composition for Service-Oriented Situational Applications

This paper presents a novel monitoring architecture addressed to the cloud provider and the cloud consumers.

This architecture offers a Monitoring Platform-as-a-Service to each cloud consumer that allows to customize

the monitoring metrics. The cloud provider sees a complete overview of the infrastructure whereas the cloud

consumer sees automatically her cloud resources and can define other resources or services to be monitored.

This is accomplished by means of an adaptive distributed monitoring architecture automatically deployed in

the cloud infrastructure. This architecture has been implemented and released under GPL license to the

community as “MonPaaS”, open source software for integrating Nagios and OpenStack. An intensive

empirical evaluation of performance and scalability have been done using a real deployment of a cloud

computing infrastructure in which more than 3700 VMs have been executed..

ETPL

SER - 023

MonPaaS: An Adaptive Monitoring Platform as a Service for Cloud Computing

Infrastructures and Services








QoS-aware Web service composition intends to maximize the global QoS of a composite service with local

and global QoS constraints while selecting the independent candidate services from different providers. With

the increasing number of candidate services emerging from the Internet, the network delays often greatly

affect the performance of the composite service, which are usually difficult to be collected beforehand. One

remedy is to predict them for the composition. However, there are some new issues in network delay

predictions for the composition, including prediction accuracy, on-demand measures to new services and

runtime overhead. In this paper, we try to tackle these critical challenges by taking advantage of the

geolocations of candidate services. We firstly describe a network-aware service composition problem. Then,

we present a novel geolocation-based NQoS prediction and reprediction approach for service composition.

Furthermore, a geolocation-based service selection algorithm is presented to make use of our NQoS prediction

approach for the composition. We have conducted extensive experiments on the real-world dataset collected

from PlanetLab. Comparative experimental results demonstrate that our approach improves the prediction

accuracy and predictability of the NQoS and reduces the runtime overheads in predicting the composition.

ETPL

SER - 024

Network-aware QoS prediction for Service Composition Using Geolocation

With explosive growth of social media, social computing becomes a new IT feature. A core functionality of

social computing is social network analysis, which studies dynamics of social connectivity among people,

including how people influence one another and how fast information diffuses in a social network and what

factors stimulate influence diffusion. One of the models for information diffusion is the heat diffusion model.

Although it is simple in capturing the basic principle of social influence, there are several limitations. First, the

uniform heat diffusion is no longer hold in social networks. Second, high degree nodes are most influential in

all contexts is not realistic. In this paper we propose a probabilistic approach of social influence diffusion

model with incentives. Our approach has three features. First we define an influence diffusion probability for

each node instead of uniform probability. Second, we categorize nodes into two classes: active and inactive.

Active nodes have chances to influence inactive nodes but not vice versa. Third, we utilize a system defined

diffusion threshold to control how influence is propagated. We study how incentives can be utilized to boost

the influence diffusion. Our experiments show the reward-powered model is more effective in influence

diffusion.

ETPL

SER - 025

Probabilistic Diffusion of Social Influence with Incentives








An external web service may evolve without prior notification. In the course of the regression testing of a

workflow-based web service, existing test case prioritization techniques may only verify the latest service

composition using the not-yet-executed test cases, overlooking high-priority test cases that have already been

applied to the service composition before the evolution. In this paper, we propose Preemptive Regression

Testing (PRT), an adaptive testing approach to addressing this challenge. Whenever a change in the coverage

of any service artifact is detected, PRT recursively preempts the current session of regression test and creates a

sub-session of the current test session to assure such lately identified changes in coverage by adjusting the

execution priority of the test cases in the test suite. Then, the sub-session will resume the execution from the

suspended position. PRT terminates only when each test case in the test suite has been executed at least once

without any preemption activated in between any test case executions. The experimental result confirms that

testing workflow-based web service in the face of such changes is very challenging; and one of the PRT-

enriched techniques shows its potential to overcome the challenge.

ETPL

SER - 026

Preemptive Regression Testing of Workflow-based Web Services

Modern data centers use virtualization as a means to increase utilization of increasingly powerful multi-core

servers. Applications often require only a fraction of the resources provided by modern hardware. Multiple

concurrent workloads are therefore required to achieve adequate utilization levels. Current virtualization

solutions allow hardware to be partitioned into Virtual Machines with appropriate isolation on most levels.

However, unmanaged consolidation of resource intensive workloads can still lead to unexpected performance

variance. Measures are required to avoid or reduce performance interference and provide predictable service

levels for all applications. In this paper, we identify and reduce network-related interference effects using

performance models based on the runtime characteristics of virtualized workloads. We increase the

applicability of existing training data by adding network-related performance metrics and benchmarks. Using

the extended set of training data, we predict performance degradation with existing modeling techniques as

well as combinations thereof. Application clustering is used to identify several new network-related

application types with clearly defined performance profiles. Finally, we validate the added value of the

improved models by introducing new scheduling techniques and comparing them to previous efforts. We

demonstrate how the inclusion of network-related parameters in performance models can significantly increase

the performance of consolidated workloads.

ETPL

SER - 027

Network Aware Scheduling for Virtual Machine Workloads with Interference Models








Service-Oriented Computing enables the composition of loosely coupled services provided with varying

Quality of Service (QoS) levels. Selecting a near-optimal set of services for a composition in terms of QoS is

crucial when many functionally equivalent services are available. As the number of distributedservices,

especially in the cloud, is rising rapidly, the impact of the network on the QoS keeps increasing. Despite this,

current approaches do not differentiate between the QoS of services themselves and the network. Therefore,

the computed latency differs from the actual latency, resulting in suboptimal QoS. Thus, we propose

a network-aware approach that handles the QoS of services and the QoS of the network independently. First,

we build a network model in order to estimate the network latency between arbitrary services and potential

users. Our selection algorithm then leverages this model to find compositions with a low latency for a given

execution policy. We employ a self-adaptive genetic algorithm which balances the optimization of latency and

other QoS as needed and improves the convergence speed. In our evaluation, we show that

our approach works under realistic network conditions, efficiently computing compositions with much lower

latency and otherwise equivalent QoS compared to current approaches.

ETPL

SER - 028

SanGA: A Self-Adaptive Network-Aware Approach to Service Composition

Data as a Service (DaaS) builds on service-oriented technologies to enable fast access to data resources on

the Web. However, this paradigm raises several new privacy concerns that traditional privacy models do not

handle. In addition, DaaS composition may reveal privacy-sensitive information. In this paper, we propose a

formal privacy model in order to extend DaaS descriptions with privacy capabilities. The privacy model

allows a service to define a privacy policy and a set of privacy requirements. We also propose a privacy-

preserving DaaS composition approach allowing to verify the compatibility between privacy requirements and

policies in DaaS composition. We propose a negotiation mechanism that makes it possible to dynamically

reconcile the privacy capabilities ofservices when incompatibilities arise in a composition. We validate the

applicability of our proposal through a prototype implementation and a set of experiments.

ETPL

SER - 029

Privacy-Enhanced Web Service Composition








With the proliferation of Web services, service engineers demand automatic service composition algorithms

that not only synthesize the correct service compositions from thousands of services but also satisfy the quality

requirements of users. This is known as QoS-aware automatic service composition problem. Our observation

is that current research of only finding the optimal service composition result has several shortcomings. Users

have to utilize the optimal one, which will make it rigid, and consequently brings about problems, such as

overload of "hot services" and lack of choices for users. To cope with these problems,

a top k query mechanism is introduced in this paper, a progressive and incremental Key-Path-Based Loose

(KPL) algorithm with 100% accuracy is proposed. Our QSynth which won the performance championship of

Web Service Challenge 2009 and 2010 is extended to support top k query based on KPL algorithm.

Evaluations show that, compared to the state of the art, KPL algorithm achieves superior scalability and

accuracy with respect to a large variety of composition scenarios. Moreover, we generalize a new graph

problem: top k DAGs (Directed Acyclic Graphs) problem based on the above work. Applications of this new

graph problem contain API recommender, supply chain and so on.

ETPL

SER - 031

Top K Query for QoS-Aware Automatic Service Composition

With data storage and sharing services in the cloud, users can easily modify and share data as a group. To

ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the

blocks in shared data. Different blocks in shared data are generally signed by different users due

to data modifications performed by different users. For security reasons, once a user is revoked from the

group, the blocks which were previously signed by this revoked user must be re-signed by an existing user.

The straightforward method, which allows an existing user to download the corresponding part

of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in

the cloud. In this paper, we propose a novel public auditing mechanism for the integrity

of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow

the cloud to re-sign blocks on behalf of existing users during user revocation, so that existingusers do not need

to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the

integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has

been re-signed by the cloud. Moreover, our mechanism is able to support batchauditing by verifying

multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly

improve the efficiency of user revocation

ETPL

SER - 030

Panda: Public Auditing for Shared Data with Efficient User Revocation in the

Cloud








Storage clouds use economies of scale to host data for diverse enterprises. However, enterprises differ in the

requirements for their data. In this work, we investigate the problem of resiliency or disaster recovery

(DR) planning in a storage cloud. The resiliency requirements vary greatly between different enterprises and

also between different datasets for the same enterprise. We present in this paper Resilient Storage Cloud Map

(RSCMap), a generic cost-minimizing optimization framework for disaster recovery planning, where the cost

function may be tailored to meet diverse objectives. We present fast algorithms that come up with a minimum

cost DR plan, while meeting all the DR requirements associated with all the datasets hosted on

the storage cloud. Our algorithms have strong theoretical properties: 2 factor approximation for bandwidth

minimization and fixed parameter constant approximation for the general cost minimization problem. We

perform a comprehensive experimental evaluation of RSCMap using models for a wide variety of replication

solutions and show that RSCMap outperforms existing resiliency planning approaches.

ETPL

SER - 032

Integrated Resiliency Planning in Storage Clouds

This paper presents a Learning Automata (LA)-based QoS (LAQ) framework capable of addressing some of

the challenges and demands of various cloud applications. The proposed LAQ framework ensures that the

computing resources are used in an efficient manner and are not over- or under-utilized by the consumer

applications. Service provisioning can only be guaranteed by continuously monitoring the resource and

quantifying various QoS metrics, so that services can be delivered in an on-demand basis with certain levels of

guarantee. The proposed framework helps in ensuring guarantees with these metrics in order to provide QoS-

enabled cloud services. The performance of the proposed system is evaluated with and without LA, and it is

shown that the LA-based solution improves the performance of the system in terms of response time and speed

up.

ETPL

SER - 033

Learning Automata-Based QoS Framework for Cloud IaaS








Cloud storage services have become commercially popular due to their overwhelming advantages. To provide

ubiquitous always-on access, a cloud service provider (CSP) maintains multiple replicas for each piece of data

on geographically distributed servers. A key problem of using the replication technique in clouds is that it is

very expensive to achieve strong consistency on a worldwide scale. In this paper, we first present a

novel consistency as a service (CaaS) model, which consists of a large data cloud and multiple

small audit clouds. In the CaaS model, a data cloud is maintained by a CSP, and a group of users that

constitute an audit cloud can verify whether the data cloud provides the promised level of consistency or not.

We propose a two-level auditing architecture, which only requires a loosely synchronized clock in

the audit cloud. Then, we design algorithms to quantify the severity of violations with two metrics: the

commonality of violations, and the staleness of the value of a read. Finally, we devise a

heuristic auditing strategy (HAS) to reveal as many violations as possible. Extensive experiments were

performed using a combination of simulations and real cloud deployments to validate HAS.

ETPL

SER - 034

Consistency as a Service: Auditing Cloud Consistency

As the size of DRAM memory grows in clusters, memory errors are common. Current memoryavailability

strategies mostly focus on memory backup and error recovery. Hardware solutions likemirror memory needs

costly peripheral equipments while existing software approaches reduce the expense but are limited by the

high overhead in practical usage. Moreover, in cloud environments, containers such as LXC now can be used

as process and application-level virtualization to run multiple isolated systems on a single host. In this paper,

we present a novel system called Memvisor to provide high availability memory mirroring. It is a software

approach achieving flexible multi-granularity memorymirroring based on virtualization and binary translation.

We can flexibly set memory areas to bemirrored or not from process level to the whole user mode

applications. Then, all memory write instructions are duplicated. Data written to memory are synchronized to

backup space in the instruction level. If memory failures happen, Memvisor will recover the data from the

backup space. Compared with traditional software approaches, the instruction level synchronization lowers the

probability of data loss and reduces the backup overhead. The results show that Memvisor outperforms the

state-of-the-art software approaches even in the worst case.

ETPL

SER - 035

Multi-Granularity Memory Mirroring via Binary Translation in Cloud

Environments








This paper presents the design, implementation, and evaluation of TransCom,

a virtual disk (Vdisk)based cloud computing platform that supports heterogeneous services of operating

systems (OSes) and their applications in enterprise environments. In TransCom, clients store all data and

software, including OS and application software, on Vdisks that correspond to disk images located on

centralized servers, while computing tasks are carried out by the clients. Users can choose to boot any client

for using the desired OS, including Windows, and access software and data services from Vdisks as usual

without consideration of any other tasks, such as installation, maintenance, and management. By centralizing

storage yet distributing computing tasks, TransCom can greatly reduce the potential system maintenance and

management costs. We have implemented a multi-platform TransComprototype that supports both Windows

and Linux services. The extensive evaluation based on both test-bed experiments and real-usage experiments

has demonstrated that TransCom is a feasible, scalable, and efficient solution for successful real-world use.

ETPL

SER - 036

TransCom: A Virtual Disk-Based Cloud Computing Platform for

Heterogeneous Services

Cloud computing is becoming increasingly important for provision of services and storage of data in the

Internet. However there are several significant challenges in securing cloud infrastructures from different types

of attacks. The focus of this paper is on the security services that a cloud provider can offer as part of its

infrastructure to its customers (tenants) to counteract these attacks. Our main contribution is

a security architecture that provides a flexible security as a service model that a cloudprovider can offer to its

tenants and customers of its tenants. Our security as a service model while offering a baseline security to the

provider to protect its own cloud infrastructure also provides flexibility to tenants to have

additional security functionalities that suit their security requirements. The paper describes the design of

the security architecture and discusses how different types of attacks are counteracted by the proposed

architecture. We have implemented the security architecture and the paper discusses analysis and performance

evaluation results.

ETPL

SER - 037

Security as a Service Model for Cloud Environment








Security concerns are widely seen as an obstacle to the adoption

of cloud computing solutions.Information Flow Control (IFC) is a well understood Mandatory

Access Control methodology. The earliest IFC models targeted security in a centralised environment, but

decentralised forms of IFC have been designed and implemented, often within academic research projects. As

a result, there is potential for decentralised IFC to achieve better cloud security than is available today. In this

paper we describe the properties of cloud computing—Platform-as-a-Service clouds in particular—and review

a range of IFC models and implementations to identify opportunities for using IFC within

a cloud computingcontext. Since IFC security is linked to the data that it protects, both tenants and providers

of cloudservices can agree on security policy, in a manner that does not require them to understand and rely on

the particulars of the cloud software stack in order to effect enforcement.

ETPL

SER - 038

Information Flow Control for Secure Cloud Computing

The hindrances to the adoption of public cloud computing services include service reliability, data security and

privacy, regulation compliant requirements, and so on. To address those concerns, we propose

a hybrid cloud computing model which users may adopt as a viable and cost-saving methodology to make the

best use of public cloud services along with their privately-owned (legacy) data centers. As the core of

this hybrid cloud computing model, an intelligent workload factoring service is designed

for proactive workload management. It enables federation between on- and off-premise infrastructures for

hosting Internet-based applications, and the intelligence lies in the explicit segregation of base workload and

flash crowd workload, the two naturally different components composing the application workload. The core

technology of the intelligent workload factoring service is a fast frequent data item detection algorithm, which

enables factoring incoming requests not only on volume but also on data content, upon a changing application

data popularity. Through analysis and extensive evaluation with real-trace driven simulations and experiments

on a hybrid testbed consisting of local computingplatform and Amazon Cloud service platform, we showed

that the proactive workload managementtechnology can enable reliable workload prediction in the

base workload zone (with simple statistical methods), achieve resource efficiency (e.g., 78% higher server

capacity than that in base workloadzone) and reduce data cache/replication overhead (up to two orders of

magnitude) in the flash crowdworkload zone, and react fast (with an X^2 speed-up factor) to the changing

application data popularity upon the arrival of load spikes.

ETPL

SER - 039

Proactive Workload Management in Hybrid Cloud Computing








Recently, Cloud Computing is attracting great attention due to its provision of configurable computing

resources. MapReduce (MR) is a popular framework for data-intensive distributed computing of batch

jobs. MapReduce suffers from the following drawbacks: 1. It is sequential in its processing of Map and

Reduce Phases 2. Being cluster based, its scalability is relatively limited. 3. It does not support flexible

pricing. 4. It does not support stream data processing. We describe Cloud MapReduce (CMR), which

overcomes these limitations. Our results show that CMR is more efficient and runs faster than other

implementations of the MR framework. In addition to this, we showcase how CMR can be further enhanced

to: 1. Support stream data processing in addition to batch data by parallelizing the Map and Reduce phases

through a pipelining model. 2. Support flexible pricing using Amazon Cloud's spot instances and to deal with

massive machine terminations caused by spot price fluctuations. 3. Improve throughput and speed-up

processing over traditional MR by more than 30% for large data sets. 4. Provide added flexibility and

scalability by leveraging features of the cloud computing model. Click-stream analysis, real-time multimedia

processing, time-sensitive analysis and other stream processingapplications can also be supported.

ETPL

SER - 040

An Advanced MapReduce: Cloud MapReduce, Enhancements and Applications

Cloud computing is a style of computing where different capabilities are provided as a service to customers

using Internet technologies. The most common offered services are Infrastructure (IasS), Software (SaaS) and

Platform (PaaS). This work integrates the service management into the cloudcomputing concept and shows

how management can be provided as a service in the cloud. Nowadays,services need to adapt their

functionalities across heterogeneous environments with different technological and administrative domains.

The implied complexity of this situation can be simplified by aservice management architecture in the cloud.

This paper focuses on this architecture, taking into account specific service management functionalities, like

incident management or KPI/SLAmanagement, and provides a complete solution. The proposed architecture is

based on a distributed set of agents, using semantic-based techniques: a Shared Knowledge Plane, instantiated

in the cloud, has been introduced to ensure communication between agents.

ETPL

SER - 041

A Flexible Architecture for Service Management in the Cloud








Nowadays, the rapid growth of Cloud computing services is stressing the network communication

infrastructure in terms of resiliency and programmability. This evolution reveals missing blocks of the current

Internet Protocol architecture, in particular in terms of virtual machine mobility management for addressing

and locator-identifier mapping. In this paper, we propose some changes to the Locator/Identifier Separation

Protocol (LISP) to cope with this gap. We define novel control-plane functions and evaluate them exhaustively

in the worldwide public LISP testbed, involving five LISP sites distant from a few hundred kilometers to many

thousands kilometers. Our results show that we can guarantee service downtime upon

live virtual machine migration lower than a second across American, Asian and European LISP sites, and

down to 300 ms within Europe, outperforming standard LISP and legacy triangular routing approaches in

terms of service downtime, as a function of datacenter-datacenter and client-datacenter distances.

ETPL

SER - 042

Achieving Sub-Second Downtimes in Large-Scale Virtual Machine Migrations

with LISP

Backup paths are usually pre-installed by network operators to protect against single link failures in backbone

networks that use multi-protocol label switching. This paper introduces a new scheme called

Green Backup Paths (GBP) that intelligently exploits these existing backup paths to perform energy-

aware traffic engineering without adversely impacting the primary role of these backup paths of

preventing traffic loss upon single link failures. This is in sharp contrast to most existing schemes that

tackle energy efficiency and link failure protection separately, resulting in substantially high operational costs.

GBP works in an online and distributed fashion, where each router periodically monitors its

localtraffic conditions and cooperatively determines how to reroute traffic so that the highest number of

physical links can go to sleep for energy saving. Furthermore, our approach maintains quality-of-service by

restricting the use of long backup paths for failure protection only, and therefore, GBP avoids substantially

increased packet delays. GBP was evaluated on the point-of-presence representation of two publicly available

network topologies, namely, GÉANT and Abilene, and their real traffic matrices. GBP was able to achieve

significant energy saving gains, which are always within 15% of the theoretical upper bound.

ETPL

SER - 043

Leveraging MPLS Backup Paths for Distributed Energy-Aware Traffic

Engineering








The need to autonomically optimize end-user service experience in near real time has been identified in the

literature in recent years. Management systems that monitor end-user service session context exist

but approaches that estimate end-user service experience from session context do not analyze the compliance

of that experience with user expectations. Approaches that optimize end-user servicedelivery are not

applicable to arbitrary services; they either optimize specific service types or use general mechanisms that do

not consider service experience. The lack of a holistic model for end-userservice management is a barrier to

autonomic end-user service optimization. This paper presentsAesop, an approach addressing

autonomic optimization of end-user service delivery using semantic-based techniques. Its knowledge base uses

the End-User Service Analysis and Optimization ontology, which models the end-user service management

domain and partitions knowledge that varies over time for efficient access. The Aesop Engine executes an

autonomic loop in near real time, which runssemantic algorithms to monitor sessions, analyze their

compliance with expectations, and plan and execute optimizations on service delivery networks. The

algorithms are efficient because they operate on small partitioned subsets of the Knowledge Base held as

separate self-contained models at run time. An Aesop implementation was evaluated on a home area network

test bed where compliance ofservice sessions with expectations when optimization was active was compared

with compliance of an identical set of sessions when optimization was inactive. Significant improvements

were observed on compliance levels of high priority sessions in all experimental scenarios, with compliance

levels more than doubled in some cases.

ETPL

SER - 044

The Aesop Approach for Semantic-Based End-User Service Optimization








Network visibility is a critical part of traffic engineering, network management, and security. The most

popular current solutions - Deep Packet Inspection (DPI) and statistical classification, deeply rely on the

availability of a training set. Besides the cumbersome need to regularly update the signatures, their visibility is

limited to classes the classifier has been trained for. Unsupervised algorithms have been envisioned as a viable

alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to

use them for traffic classification in practical scenario. To address the above issues, we propose SeLeCT,

a Self-Learning Classifier for Internet Traffic. It uses unsupervised algorithms along with an adaptive seeding

approach to automatically let classes of traffic emerge, being identified and labeled. Unlike

traditional classifiers, it requires neither a-priori knowledge of signatures nor a training set to extract the

signatures. Instead, SeLeCT automatically groups flows into pure (or homogeneous) clusters using simple

statistical features. SeLeCT simplifies label assignment (which is still based on some manual intervention) so

that proper class labels can be easily discovered. Furthermore, SeLeCT uses an iterative seeding approach to

boost its ability to cope with new protocols and applications. We evaluate the performance

of SeLeCT using traffic traces collected in different years from various ISPs located in 3 different continents.

Our experiments show that SeLeCT achieves excellent precision and recall, with overall accuracy close to

98%. Unlike state-of-art classifiers, the biggest advantage of SeLeCT is its ability to discover new protocols

and applications in an almost automated fashion.

ETPL

SER - 045

SeLeCT: Self-Learning Classifier for Internet Traffic

Testing network devices in a live environment is desirable due to its reality. However, the defects are not

reproducible, and the network connectivity will be broken if the device is down. For effective defect

reproduction from real traffic, we design a new mechanism, which allows the device under test (DUT) to be

automatically online/offline, and supports multi-port replay for multi-port network devices with an OpenFlow

switch. The defect traces are captured when the DUT is online. When a DUT failure is detected, the DUT will

be offline, and the defect-triggering traces will be replayed to identify the defect. For efficient replay, we keep

only partial payloads in a reduced number of packets in the defect traces that are sufficient to trigger the

defects. For defect identification, reduction based on a binary search algorithm is presented to deal with the

defects caused by payload anomalies and by overloading. The downsizing ratios in the cases of payload

anomalies and overloading are up to 98.8% and 96%, respectively. The minimum outage time of the failover

during the DUT failure is obtained when the check interval is 1 second and the number of tolerable

consecutive failures is 2.

ETPL

SER - 046

On-the-Fly Capture and Replay Mechanisms for Multi-Port Network Devices in

Operational Networks








We consider wireless mesh networks with cognitive ability of the wireless routers' radios. The cognitiveability

is a cost efficient manner to increase available bandwidth but requires an adaptive bandwidth management

mechanism to deal with dynamics of primary users' activities. In this paper, we investigate the

joint channel allocation and routing in cognitive wireless mesh networks including thechannel reuse

opportunities in order to improve the network performance. In particular we propose

aneconomic framework for adaptation and control of the network resources with the goal of network profit

maximization. The economic framework is based on the notion of state dependent node shadow price that is

derived from Markov decision theory. The node shadow prices are used as routing metrics while their average

values are used to allocate the channels among the different nodes. Simulation results illustrate

the network profit maximization and effectiveness of the proposed channel allocation scheme that is integrated

with a channel reuse algorithm.

ETPL

SER - 047

An Economic Framework for Routing and Channel Allocation in Cognitive

Wireless Mesh Networks

In wireless sensor networks the issue of preserving energy requires utmost attention. One primary way of

conserving energy is judicious deployment of sensor nodes within the network area so that theenergy flow

remains balanced throughout the network and prevents the problem of occurrence ofenergy holes. Firstly, we

have analyzed network lifetime, found node density as the parameter which has significant influence

on network lifetime and derived the desired parameter values for balancedenergy consumption. Then to meet

the requirement of energy balancing, we have proposed a probabilitydensity function (PDF), derived the PDF's

intrinsic characteristics and shown its suitability to model thenetwork architecture considered for the work.

A node deployment algorithm is also developed based on this PDF. Performance of the deployment scheme is

evaluated in terms of coverage-connectivity,energy balance and network lifetime. In qualitative analysis, we

have shown the extent to which our proposed PDF has been able to provide desired node density derived from

the analysis on networklifetime. Finally, the scheme is compared with three existing deployment schemes

based on various distributions. Simulation results confirm our scheme's supremacy over all the existing

schemes in terms of all the three performance metrics.

ETPL

SER - 048

Design of a Probability Density Function Targeting Energy-Efficient Node

Deployment in Wireless Sensor Networks








Carrier Ethernet is rapidly being deployed in the metropolitan and core segments of the transportnetwork. One

of the emerging flavors of Carrier Ethernet is the IEEE 802.1Qay PBB-TE or ProviderBackbone Bridging—

Traffic Engineering standard. PBB-TE relies on the assignment of a network-specific Virtual Local

Area Network (VLAN) tag, called the Backbone VLAN ID or BVID that is used in conjunction with

a backbone Media Access Control (MAC) address for forwarding. The 12-bit BVIDalong with 48-

bit Backbone MAC address are used to forward an Ethernet frame. The assignment of BVIDs in a network is

critical, given that there are only 4094 possible assignments, especially for those paths that are overlapping in

the network graph and incident at the same destination. While the only way to scale is to reuse BVIDs, this

method can lead to a complication if the same BVID is allocated to an overlapping path. To the best of our

knowledge, this is the first instance of isolating this problem of limited BVID availability which rises only due

to graphical overlap between services. We formulate and solve this as a constrained optimization problem. We

present optimal and heuristic algorithms to solve the BVID problem. The optimal approach solves the `static'

case, while the heuristic can solve both the `static' and the `dynamic' cases of the BVID allocation problem.

Results show that the developed heuristics perform close to the optimal and can be used in commercial

settings for both the static and dynamic cases.

ETPL

SER - 049

On the Backbone VLAN Identifier (BVID) Allocation in 802.1Qay Provider

Backbone Bridged — Traffic Engineered Networks

Thank You !





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