Resource allocation algorithms for virtualized service hosting platforms

Commodity clusters are used routinely for deploying service hosting platforms. Due to hardware and operation costs, clusters need to be shared among multiple services. Crucial for enabling such shared hosting platforms is virtual machine (VM) technology, which allows consolidation of hardware resour...

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Veröffentlicht in:Journal of parallel and distributed computing Jg. 70; H. 9; S. 962 - 974
Hauptverfasser: Stillwell, Mark, Schanzenbach, David, Vivien, Frédéric, Casanova, Henri
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Inc 01.09.2010
Elsevier
Schlagworte:
Algorithms
Applied sciences
Cluster
Clusters
Computer Science
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Distributed, Parallel, and Cluster Computing
Exact sciences and technology
Formulations
Hardware
Mathematical analysis
Optimization
Platforms
Resource allocation
Searching
Software
Virtual machine
Cluster
Virtual machine
Resource allocation
Consolidation
Optimal allocation
Decision making
Equity
Objective function
Service quality
Workload
ISSN:0743-7315, 1096-0848
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Zusammenfassung:Commodity clusters are used routinely for deploying service hosting platforms. Due to hardware and operation costs, clusters need to be shared among multiple services. Crucial for enabling such shared hosting platforms is virtual machine (VM) technology, which allows consolidation of hardware resources. A key challenge, however, is to make appropriate decisions when allocating hardware resources to service instances. In this work we propose a formulation of the resource allocation problem in shared hosting platforms for static workloads with servers that provide multiple types of resources. Our formulation supports a mix of best-effort and QoS scenarios, and, via a precisely defined objective function, promotes performance, fairness, and cluster utilization. Further, this formulation makes it possible to compute a bound on the optimal resource allocation. We propose several classes of resource allocation algorithms, which we evaluate in simulation. We are able to identify an algorithm that achieves average performance close to the optimal across many experimental scenarios. Furthermore, this algorithm runs in only a few seconds for large platforms and thus is usable in practice. ► Resource allocation for static workloads on virtualized cluster platforms can be formulated as the optimization of a novel metric called the “yield”. ► Yield optimization can be framed as a mixed-integer linear program with a computable bound on the optimal.► Using a binary search on the optimum yield, solving a vector packing problem at each iteration of the search, can lead to results close to the optimal in practice.
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ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2010.05.006