Paper Info
Title
Topology-aware resource allocation for data-intensive workloads
Abstract
This paper proposes an architecture for optimized resource allocation in Infrastructure-as-a-Service (IaaS)-based cloud systems. Current IaaS systems are usually unaware of the hosted application's requirements and therefore allocate resources independently of its needs, which can significantly impact performance for distributed data-intensive applications. To address this resource allocation problem, we propose an architecture that adopts a "what if" methodology to guide allocation decisions taken by the IaaS. The architecture uses a prediction engine with a lightweight simulator to estimate the performance of a given resource allocation and a genetic algorithm to find an optimized solution in the large search space. We have built a prototype for Topology-Aware Resource Allocation (TARA) and evaluated it on a 80 server cluster with two representative MapReduce-based benchmarks. Our results show that TARA reduces the job completion time of these applications by up to 59% when compared to application-independent allocation policies.
Year
DOI
Venue
2010
10.1145/1925861.1925881
ACM SIGCOMM Computer Communication Review
Keywords
DocType
Volume
resource allocation,allocation policy,resource allocation problem,topology-aware resource allocation,virtualization,optimized resource allocation,allocation decision,current iaas system,optimized solution,topology awareness,mapreduce,hadoop,cloud system,impact performance,performance,modeling,infrastructure-as-a-service,data-intensive workloads,iaas,infrastructure as a service,search space,genetic algorithm
Conference
41
Issue
ISSN
Citations 
1
0146-4833
36
PageRank 
References 
Authors
1.58
15
4
Name
Order
Citations
PageRank
Gunho Lee1191784.03
Niraj Tolia288666.35
Parthasarathy Ranganathan33316230.61
Randy H. Katz4168193018.89