Multi-Objective Optimization for Virtual Machine Allocation and Replica Placement in Virtualized Hadoop

Resource management is a key factor in the performance and efficient utilization of cloud systems, and many research works have proposed efficient policies to optimize such systems. However, these policies have traditionally managed the resources individually, neglecting the complexity of cloud syst...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 29; číslo 11; s. 2568 - 2581
Hlavní autoři: Guerrero, Carlos, Lera, Isaac, Bermejo, Belen, Juiz, Carlos
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Classification
Cloud computing
evolutionary computing and genetic algorithms
file replica placement
Genetic algorithms
hadoop
Multiple objective analysis
Optimization
Placement
Policies
Power consumption
Power demand
Resource management
Sorting
Sorting algorithms
Virtual environments
Virtual machine allocation
Virtual machining
ISSN:1045-9219, 1558-2183
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Shrnutí:Resource management is a key factor in the performance and efficient utilization of cloud systems, and many research works have proposed efficient policies to optimize such systems. However, these policies have traditionally managed the resources individually, neglecting the complexity of cloud systems and the interrelation between their elements. To illustrate this situation, we present an approach focused on virtualized Hadoop for a simultaneous and coordinated management of virtual machines and file replicas. Specifically, we propose determining the virtual machine allocation, virtual machine template selection, and file replica placement with the objective of minimizing the power consumption, physical resource waste, and file unavailability. We implemented our solution using the non-dominated sorting genetic algorithm-II, which is a multi-objective optimization algorithm. Our approach obtained important benefits in terms of file unavailability and resource waste, with overall improvements of approximately 400 and 170 percent compared to three other optimization strategies. The benefits for the power consumption were smaller, with an improvement of approximately 1.9 percent.
Bibliografie:ObjectType-Article-1
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2018.2837743