ENERGY EFFICIENT RESOURCE ALLOCATION IN DATA CENTERS

PhD Thesis Proposal Defence


Title: "ENERGY EFFICIENT RESOURCE ALLOCATION IN DATA CENTERS"

by

Miss Xiangming DAI


Abstract:

Recent years have seen a tremendous increase in the popularity of cloud 
computing services in supporting business, communication, online customer 
service and helping make life more productive and efficient. Naturally, this 
has been accompanied by a constant expansion of data centers in scale and in 
geographical outreach worldwide, resulting in a dramatic growth of the energy 
consumed to power such data centers. Several studies indicate that the energy 
consumed today by data centers is equivalent to the annual output of 34 large 
(500-megawatt) coal-red power plants in the US alone. This number is forecast 
to reach double in less than 10 years. The huge amount of energy consumption 
not only costs data center providers billions of dollars, but also generate 
hundreds of millions of tonnes of carbon pollution per year. Energy consumption 
in data centers comes from several aspects: i) computing and networking 
equipments, ii) cooling equipments, and iii) power draw and other ancillary 
equipments. Any reduction of such consumption is seen as such a boon that some 
data center providers such as Facebook and Google have built data centers in as 
far ung area as the Arctic circle, while others like Microsoft are considering 
undersea data centers, to cut cooling costs. In this thesis, we consider 
several important problems of resource allocation in data center while 
optimizing the energy consumed by computing and networking equipments.

The thesis is structured in three parts. The rst part falls within the area of 
the so-called platform-as-a-service (PaaS) cloud service model and deals with 
job scheduling in the MapReduce massive-data parallel-processing framework. In 
this part we consider energy efficiency as a byproduct of minimizing the jobs . 
More specically, we first propose a new scheduling algorithm called Multiple 
Queue Scheduler (MQS) to improve the data locality rate of tasks as a means to 
curbing the costly data migration delays. Then, to take into account the 
intricate details of MapReduce framework such as the early shuffle problem, we 
propose the Dynamic Priority Multiple Queue Scheduler (DPMQS) to further 
improve MQS. DPMQS dynamically increases the priority of jobs that are close to 
completing their Map phase to speed up the start of the reduce phase, thus 
reducing further the expected job holding time and thus the makespan. We 
implemented both algorithms in Hadoop and compared their performance to other 
existing algorithms. The second part falls within the realm of 
infrastructure-as-a-service (IaaS) and deals with energy efficient virtual 
machine (VM) scheduling in data centers. In this part we formulate the minimum 
energy VM scheduling as a non-convex optimization model, prove its NP-hardness, 
then explore two greedy approximation algorithms, minimum energy VM scheduling 
algorithm (MinES) and minimum communication VM scheduling algorithm (MinCS), to 
reduce the energy while satisfying the data center tenants service level 
agreements. Current IaaS service providers only support rudimentary network 
topologies that simply consist of a super-sized virtual switch interconnecting 
a tenant's VMs. To increase the business potential of such platforms by 
supporting more intricate network topologies as specied by the tenants, we 
consider in the third part of this thesis the problem of embedding virtual 
clusters into a data center in a energy efficient manner. We carefully provide 
a mathematical optimization model of this problem, then given its NP-hardness, 
we propose an approximate algorithm minimum energy virtual cluster embedding 
(MinE-VCE) to solve the problem. We tested all proposed algorithms using real 
data traces as well as synthetic ones to demonstrate their performance.


Date:			Friday, 26 February 2016

Time:                  	2:00pm - 4:00pm

Venue:                  CYTG001
                         CYT Building

Committee Members:	Dr. Brahim Bensaou (Supervisor)
  			Dr. Jogesh Muppala (Chairperson)
 			Dr. Kai Chen
  			Prof. Danny Tsang (ECE)


**** ALL are Welcome ****