Resource allocation and routing in parallel multi-server queues with abandonments for cloud profit maximization Articles uri icon

publication date

  • March 2019

start page

  • 221

end page

  • 236

volume

  • 103

international standard serial number (ISSN)

  • 0305-0548

electronic international standard serial number (EISSN)

  • 1873-765X

abstract

  • This paper considers a Markov decision model for profit maximization of a cloud computing service provider catering to customers submitting jobs with firm real-time random deadlines. Customers are charged on a per-job basis, receiving a full refund if deadlines are missed. The service provider leases computing resources from an infrastructure provider in a two-tier scheme: long-term leasing of basic infrastructure, consisting of heterogeneous parallel service nodes, each modeled as a multi-server queue, and short-term leasing of external servers. Given the intractability of computing an optimal dynamic resource allocation and job routing policy, maximizing the long-run average profit rate, the paper addresses the design, implementation and testing of low-complexity heuristics. The policies considered are a static policy given by an optimal Bernoulli splitting, and three dynamic index policies based on different index definitions: individually optimal (IO), policy improvement (PI) and restless bandit (RB) indices. The paper shows how to implement efficiently each such policy, and presents a comprehensive empirical comparison, drawing qualitative insights on their strengths and weaknesses, and benchmarking their performance in an extensive study. (C) 2018 Elsevier Ltd. All rights reserved.

keywords

  • parallel multi-server queues; abandonments; firm deadlines; resource allocation; routing; markov decision process; cloud computing; bernoulli splitting; index policies; load distribution; optimal repair; real-time; customers; optimality; admission; systems; service