Cooperative Scheduling for Cloud Computing

Bernabé Dorronsoro · Juan J. Durillo

Bernabé Dorronsoro• Computer Science and PhD, University of Málaga, Spain

- Title: Design and Implementation of Cellular Genetic Algorithms for Complex Problems, 2007. !

• Supervised by: Prof. Enrique Alba !

• Post-DocUniversity of Luxembourg (Sept. 2007 - Aug. 2010) !

• Post-Doc SnT (Sept. 2010 - Aug. 2012) !

• AFR Post-Doc Univ. of Lille 1 (Sept. 2010 - Now) ���2

www.bernabe.dorronsoro.es

FNR-FWF Bilateral Projects

• From 2012 • All fields of research • Details

- Submitted to the organization where the main research will be done ‣ If Luxembourg -> Through CORE

‣ If Austria

‣ No deadline

‣ ~90K€ per year

‣ Success rate ~30%

!- Evaluated only in one country

- Funded by FWF and FNR

- Maximum 3 years

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Current Picture

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Cooperative Scheduling

for

Green Cloud Computing

Recent years have witnessed a change in the way computing is done by scientists and institutions. This

change has brought about a switching from local IT infrastructures to data centers built by companies

like Google, Amazon, etc. The number of these data centers is only expected to increase in the near

future due to the growing demand of computing services.

A capital problem for data centers is the enormous amount of power required for operating them which,

besides environmental issues, make up an important percentage of their operation cots. In such a

situation, different techniques are adopted in order to reduce the energy consumption. Example of these

techniques are: dynamic server configuration—powering off non-used nodes–, CPU frequency Scaling

—scaling down the frequency of CPU when no peak performance required –, or price and location

diversity– which takes advantage of fluctuating price of energy in different countries, locations to

migrate workload. In the context of Cloud computing, an example of dynamic server configuration is

consolidation: a well-known strategy that tries to group virtual machines (VM) into a reduced set of

physical machines (PM). To achieve this, the provider schedules VM into PM; in the following we refer

to this as provider schedule.

Nowadays, workflows have become a popular paradigm for building parallel applications in the

scientific domain. A workflow consists of a graph, where each node represents a computational task

that needs to be executed and arcs represents dependences between these tasks (e.g., data

transferences). One research challenge consists in mapping the workflow's tasks onto computational

resources in such a way that one or several criteria gets optimized. Examples of these criteria are

completion time, execution costs, energy consumption, or reliability, to mention a few. In the following

we will refer to this as workflow schedule.

The main problem in a real scenario is that both, the provider and the workflow schedules, are done

independently and their decisions might collide with each other's interests. An example of this may

may occur when both the user and provider tries to optimize for energy consumption. The workflow

schedule may determine a given number of VM of a given type is the solution minimizing the energy

consumption. This decision may however difficult the consolidation of VM onto PM, thus resulting on

user's goals provider's goals● Minimize make-span● Minimize cost● Minimize energy● Minimize risk

● Minimize operation costs

● Maximize revenue● Customers satisfaction

● Minimize energy● ...

scheduling

set of VM

makespan

cost

SLA(Service LevelAgreement)

virtual machinemigration

United Kingdomdata center

Austriandata center

Inet

The Idea

• Current approach - Not optimal

- User’s and provider’s interests are in conflict !

• What do we look for? - Best performance and price for users

- Best efficiency for provider !

• How? - Address the whole problem

- Coupling user and provider decisions ‣ Priorities

‣ Relaxed SLA

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