A Resources Allocation Algorithm based on Media Task QoS in Cloud Computing

Bohai Hong College 0/ information Science and Engineering

Ocean University 0/ China, Qingdao, China hong_ bohai@126.com

YiliZhai Qingdao Haier Electronic Co.Ltd

Qingdao China zhaiyili@haier.com

Abstract-Consider service satisfaction of multitask in cloud

media, a resources allocation algorithm based on Media Task QoS (RAMTQ) is put forward. Firstly, according to media task features, QoS weight vector and expected resources vector are obtained; then we work out resources similarity vector between alternative resources and expected resources by linear normalization, further to obtain the service satisfaction by

Euclidean distance; finally, the resources corresponding to the maximum service satisfaction are allocated. The simulation illustrates the effectiveness of the algorithm.

Keywords-service satisfaction; media task features; resources allocation

I. INTRODUCTION

Media technology is the unity of a variety of techniques, it has a wide range of application areas. However, due to the characteristics of its timeliness, interactivity, high-capacity, consumption of resources, resources allocation study is more important in the application.

Resources allocation in media service is mainly considered from priority and QoS currently. A kind of algorithms are based on different priority strategies. The IRAI allocation algorithm proposed in [1] used fuzzy inference system to set priority smartly for improving resources utilization. Reference [2] proposed a priority-based resources allocation algorithm to solve the problem of resources sharing among tasks, which reach nearly optimal resources utilization; Kwang-Chun Go et al proposed a priority assignment algorithm based on the type of service with CAC access rule, it can reduce the real-time access time of WiMedia service [3]. Above are resources allocation algorithms based on priority, they give sufficient resources to high-priority tasks, which will lead to polarization of resources allocation and decrease of resources utilization, therefore, overall service satisfaction is not high.

In order to improve the service satisfaction, another kind of resources allocation strategies are based on task QoS. Reference [4] selected the appropriate resources according to the dynamic changes of the network, so that the resources are fully utilized to ensure service satisfaction. Reference [5] studied bandwidth resources dynamic allocation algorithm with QoS distortion rate as a parameter and, this paper proposed MSTFP protocol to meet user demand for services. According to media QoS, resources dynamic allocation algorithm based

978-1-4673-5000-6/13/$31.00 ©2013 IEEE

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Ruichun Tang College o/information Science and Engineering

Ocean University o/China State Key Laboratory 0/ Digital appliances, Qingdao, China

tangruichun@126.com YuqingFeng

College o/information Science and Engineering Ocean University o/China

on assessment is proposed in [6], it met the target packet loss probability to ensure the media application QoS service satisfaction. These QoS-based algorithms above improve service satisfaction through adapting the resources passively instead of allocating resources appropriate to the task.

This paper presents a resources allocation algorithm based on media task QoS features to improve overall service satisfaction in cloud computing, which also considers the resources utilization. First, the greatest component of QoS weight vector is taken as the task's QoS Preference; then we work out resources similarity vector between the alternative resources and expected resources by resources linear normalization, and further to obtain the resources allocation vector corresponding to the maximum service satisfaction; finally a virtual machine (VM) is created to perform the task. Simulation results illustrate the effectiveness of the proposed method.

This paper is organized as follows: section II introduces the related work; section III describes resources allocation algorithm based on media task QoS features in cloud environment; section IV includes simulation results and algorithm analysis; section V gives the conclusion.

II. RELATED WORK

A. problem description Resources allocation strategies based on priority give

priority to performance; they set reasonable order of priority to maximize to meet users' need. In the case of small number of tasks, the strategy has better service; In the case of large number, high-priority tasks still get better service, but low­priority tasks have no adequate resources, thus resulting in a waste of resources. The cost is considered firstly in traditional QoS-based resources allocation strategies; according to available resources, it takes certain algorithm to optimize quality of service, which saves resources, but is limited to improve service satisfaction.

With the combination of cloud computing and media service[71, these situations above can be mitigated with virtualization technology. Cloud computing has the characteristics of heterogeneous and virtualization, and manage service nodes in the form of resources pool. Virtualization is a

key technology in cloud computing, it can make a physical service node into one or more isolated VM completely, also the configuration speed is very fast. Therefore, we can dynamically allocate resources required by the task and establish a VM to perform the task.

This paper derives QoS weight vector and expected resources vector from media task features, and select the appropriate resources to set up a VM. The allocation of resources required by the task, considering the media task features, can improve the utilization of the resources and service satisfaction.

B. Research Environment

We set Media Services Manager (MSM) in common cloud environment, when a user proposes a media service request to cloud environment, MSM is responsible for resources allocation scheduling. Fig 1 shows the work process of MSM as an interface connecting users to cloud. r-----------j

;

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VoD !;lIocate VM

n :retum YM (.,D) !

Video Session :

&i File Transfer :

n ! request

{.!.ll ! I Media Streaming: , , , , , , , , , , , , t ____________ :

.

,-------VM model

'-------,-------

task schedule model

'-------

, MSM , , , , , , ,------- ,

node , optimal , resources information :

futum A&S f.-resources .. ! resources model

param·ters

task delivery task analysis

model '-------

Figure 1: Work process of MSM

Cloud

Media task can access to the cloud environment via MSM transparently. As shown the task pool reflects the diversity of media tasks [81.MSM provides the functions of task scheduling, task analysis, resources allocation, as well as the establishment of VM. Task analysis module obtains the QoS weight vector and QoS Preference according to the characteristics of the task. In order to ensure the components of the QoS weight vector are met, they must be given an appropriate amount of physical resources, so the task analysis module should also have the function of forming an expected resources vector. As heterogeneity in the cloud environment, the allocation and scheduling (A&S) module references expected resources vector to select the appropriate resources in the resources pool, then send the selected resources information to the VM module. The VM module creates a VM with allocated resources for task, and releases it after the task is finished, and the resources are returned to the resources pool.

III. RESOURCES ALLOCATlON ALGORITHM BASED ON

MEOlA TASK QoS

A. Model Establishment

J) Task QoS Features Model Cloud computing provide services for all types of media

tasks with different task features, we can select demand

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weight of the response time, computing capacity, delay, bandwidth, and service reliability as the main indicators describing media QoS diversity.

Definition 1: Let wI' w2' w3' w4' Ws respectively represents task demand weight with response time, computing capacity, delay, bandwidth, and service reliability, then QoS weight vector can be expressed as we wI' w2' w3' w4' ws) ,

5

and I wi=l ,i E [1,5]. i=l When a media task is scheduled by MSM, task analysis

module can obtain W of the task. W is assigned for quantification research with task tolerance method[91; we select .i\MX(wp w2, w3, w4, ws) as the task QoS preference meaning on which tasks have higher service requirements.

Each component of W requires a certain amount of physical resources, we introduce the task expected physical resources vector as the reference of resources allocation.

Definition 2: Task expected physical resources vector is represented as ERC'i, r2, r3, r4, rs) , where r, is a combination

of one or more physical resources to satisfy Wi of the

corresponding W ,i E [1,5].

2) Service Node Classification Model Since the mass and heterogeneous nodes m cloud

computing, MSM classifies nodes to shorten the searching time; this paper introduces the resources weight vector as classification criteria.

Definition 3: Suppose there are n service nodes in the resources pool, the resources vector of node i can be expressed

as R/'ip'i2,'i3,'i4''is) , set RmaxCrmprm2,'in3,'in4,'ins) as the vector of the maximum value of various resources in all

nodes, then resources weight vector WR; of service node i can be calculated as:

WR, = R, x diag(rmp rm2, rm3, rm4, rmS r' , i E [0, n-I] (1)

Each service node's WR; is obtained by (1), according to

its maximum component; nodes are classified into the appropriate queue of nodes, so that the resources allocation can search in the corresponding queue to shorten time. Equation (1) cannot only be used for the initialization of the nodes in the resources pool, but also act as a basis of service node re-estimate after resources allocation and recovery.

B. Resources Similarity Vector Establishment

When a media task is scheduled, MSM fmds the corresponding node queue (set the queue length is k) via task QoS preference, and searches the combination of resources to provide service for the task. In order to express similarity between the task's ER and resources provided by cloud environments, we introduce the concept of resources similarity vector.

Definition 4: Set S;CS,pS,2,S;3,S,4'S;S) as resources

similarity vector, the component Si/ (j E [1,5] ) is calculated

by linear normalization between ER andR;, si/ as follows:

error r .. jr. > a lj }

S .. = 1 1 � r .. jr. � a iE[O, k-l] (2) lj lj }

rujrj r .. jr. <1 lj }

Where a is the threshold, selecting the appropriate a can avoid allocating too much resources. This will not only meet the needs of the tasks, but also restrict a waste of resources.

C. Se�ice Satisfaction Solution

Resources similarity vector cannot directly expresses S

satisfaction, even when I Si/ is epual, corresponding si/ may j=l

be different; so we compare Wand S; with the vector W to evaluate the service satisfaction. Euclidean distance d is typically used to measure the degree of similarity of two vectors, when Euclidean distance is zero, these two vectors are equal. Because there are anti-relationship between satisfaction and Euclidean distance, the service satisfaction of the i-th kind of resources combination is expressed as: C; = 1- d, . The resources vector corresponding to the maximum servIce satisfaction is used for allocating resources.

Tbeorem 1: Given a task's WandS" the service 5

satisfaction can be expressed as: C, = 1- L ( Wj -S'j x Wj r ' j=1 then the higher the S;, the better the service satisfaction.

Proof: The QoS weight vector Wi corresponding to R; IS

derived from S, and W:

W; = S, xdiag(w" w2' w3' w4' ws), i E [0, k -1] ;

where diag( WI' w2' w3' w4' ws) is a diagonal matrix s

transformed from W .Since I Wj = 1 , and si/ � 1 , then rl

s s 2 >i/Wj �1.When ISijwj =1, it IS indicated that the j=l )=1

resources provided by the compliance with the

s

cloud environment are in full user's service expectations.

Usually, I Si/ w) < 1 , we pick the resources combination )=1

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closest to the ideal state of the resources allocation according

to the Euclidean distance of W, and W . 5

Euclidean distanced, = �)Wj-SijXW)2, i E [O,k -1]. j=1 Then service satisfaction C; :

5 Since C: = w�(l-Sij ) / L(Wj-SijxwS :2:0,slj E[O, 1], j=i SO C, is an increasing function of sij , that is, the higher

the S; , the better the service satisfaction. The proof is completed.

D. Resources Allocation Algorithm based on Media Task QoS (RAMTQ)

After a task is scheduled in task queue T, MSM can search the resources in the appropriate queue of resources pool through the QoS preference of task, then, calculate the S; between Ri and ER to get service satisfaction; at last, allocate resources which have the maximum service satisfaction. According to the selected resources information, YM module establishes a VM to carry out task and releases it after the task is finished. Resources A&S module will return the resources to the corresponding nodes. When the resources are assigned or recycled, MSM use (1) to estimate which resources queue the node should put in. Specific procedure is shown in Fig 2.

TM� {" ""'om W tl @ allocate VM

CD sch dule

t2 f---

t3 f---

t4 f---

·

·

·

(1) parameter

V M module

@ @l @

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module

� I

Gl Resources Pool get optimal "sou".s I I I · . .

high computing node queue

I I I · . .

high bandwidth node queue ·

·

·

QJ nodes I information .. I I · . .

high reliability node queue

(2) resources

Figure 2: Resources allocation scheduling procedure

RAMTQ is as follows: Step 1: Set the number of service nodes as n in resources pool;

For i = 1 to n Obtain WR; of node i by (1), and classify node i into

queue; Step2: if (status==allocation), go to step 3; else go to step 4; Step3: Get Wand ER of the task;

Let k equals to the length of the queue corresponding to task QoS preference; For i = 1 to k -1

Get S, by (2) and get C; associated with S, by (3);

Select Clllax in C; ; Allocate resources corresponding to Clllax ; Go to step 5;

Step4: Resources are returned to service node; Go to step5;

Step5: Use (1) to reclassify service nodes into the queue.

IV. ALGORITHM ANALYSES

In order to compare RAMTQ with Priority based Job Scheduling (PJSC)[2Ialgorithms, CloudSim is used as simulation platform. It can achieve RAMTQ algorithm by overloading BindCloudletToVM (int Cloudletld, int VMld) of Datacenter- Broker class. The paper set 12 different service nodes and 7 groups of different number of tasks.

Firstly, service satisfaction is a measure standard of algorithm performance. Because the comparison of single task cannot reflect the algorithm performance overall, so we take the overall service satisfaction as measure indicators, denoted

1 n by C = - Ie, where n is the number of tasks in each

n ;=1

group (size of cloudlets), C is service satisfaction of task i in the group. As the task number increased from 10 to 40, the overall service satisfaction of RAMTQ and PJSC is shown in Fig 3.

1 G 0.8 � c 0.6 '" 15 c: 0.4 j!! c 'RAMTQ' -+---0 0.2 v 'PJSC' -

0 10 15 20 25 30 35 40

size of cloudlets Figure 3: Overall service satisfaction comparison

Fig 3 shows that when n � 20 , overall service satisfaction of both algorithms is almost the same due to the sufficient resources; but when n > 20, with the increasing number of media tasks, the starvation emerges in PJSe. While RAMTQ algorithm takes into account media task QoS and allocates resources according to the need. Therefore, with the increasing number of tasks, the overall service satisfaction of RAMTQ is better than PJSC algorithm.

Secondly, when a task obtain the resources allocated and is executed, the overall execution time of the task can be used to evaluate the performance of these two algorithms. Let time is the execution time of single task: time=finish time-start time,

1 n then the overall execution time Time = - I time, , where n

n ;=1

is the number of tasks in each group, time; is execution time of task i. The simulation result is shown in Fig 4.

Fig 4 shows that the overall execution time of RAMTQ algorithm is shorter than PJSC in the different groups with

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500 400

]:300 E 200

i= 100 o

10 15 20 25 30 35 40 size of cloudlets

P JS C

• RAMTQ

Figure 4: Overall execution time comparison

different number of tasks, which is due to RAMTQ algorithm allocates resources based on the characteristics of the tasks, so that resources allocated can fully meet the needs of corresponding task. Hence, RAMTQ algorithm is better than PJSC algorithm on the overall execution time.

V. CONCLUSION

Depending on the media task QoS features and virtualization of cloud computing, this paper has proposed a resources allocation algorithm based on media tasks QoS features. The resources corresponding to the maximum service satisfaction are allocated according to task's needs. RAMTQ algorithm has improved resources utilization and user service satisfaction. Simulation results have showed the effectiveness of the algorithm.

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