Dynamic Vector Bin Packing for Online Resource Allocation in the Cloud
Aniket Murhekar, David Arbour, Tung Mai, Anup Rao

TL;DR
This paper introduces and analyzes the MinUsageTime Dynamic Vector Bin Packing problem for online resource allocation in cloud computing, providing bounds on algorithm performance and experimental validation.
Contribution
It studies the competitive ratios of various online packing algorithms for multi-dimensional resource demands, improving bounds and extending analysis to the vector case.
Findings
Move To Front algorithm has a competitive ratio at most (2μ+1)d+1.
First Fit and Next Fit have competitive ratios bounded by (μ+2)d+1 and 2μd+1.
Experimental results show Move To Front outperforms other algorithms.
Abstract
Several cloud-based applications, such as cloud gaming, rent servers to execute jobs which arrive in an online fashion. Each job has a resource demand and must be dispatched to a cloud server which has enough resources to execute the job, which departs after its completion. Under the `pay-as-you-go' billing model, the server rental cost is proportional to the total time that servers are actively running jobs. The problem of efficiently allocating a sequence of online jobs to servers without exceeding the resource capacity of any server while minimizing total server usage time can be modelled as a variant of the dynamic bin packing problem (DBP), called MinUsageTime DBP. In this work, we initiate the study of the problem with multi-dimensional resource demands (e.g. CPU/GPU usage, memory requirement, bandwidth usage, etc.), called MinUsageTime Dynamic Vector Bin Packing (DVBP). We…
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Taxonomy
TopicsOptimization and Search Problems · Optimization and Packing Problems · Advanced Manufacturing and Logistics Optimization
