2014
DOI: 10.1007/s11134-014-9414-x
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Asymptotic optimality of a greedy randomized algorithm in a large-scale service system with general packing constraints

Abstract: We consider a service system model primarily motivated by the problem of efficient assignment of virtual machines to physical host machines in a network cloud, so that the number of occupied hosts is minimized.There are multiple types of arriving customers, where a customer's mean service time depends on its type. There is an infinite number of servers. Multiple customers can be placed for service into one server, subject to general "packing" constraints. Service times of different customers are independent, e… Show more

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Cited by 24 publications
(91 citation statements)
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“…We therefore leave this question open. Note that several works consider bin-packing with item departures (see, e.g., Stolyar and Zhong (2015) and the references therein). In this work, the authors design a simple greedy algorithm for general packing constraints and show that it can be asymptotically optimal.…”
Section: Bin Packing Problemmentioning
confidence: 99%
“…We therefore leave this question open. Note that several works consider bin-packing with item departures (see, e.g., Stolyar and Zhong (2015) and the references therein). In this work, the authors design a simple greedy algorithm for general packing constraints and show that it can be asymptotically optimal.…”
Section: Bin Packing Problemmentioning
confidence: 99%
“…[3,24,31,36,42]). The VM placement in an infinite server system has been studied in [13,[33][34][35]. Four closely related papers are [23], [21], [22], [11] where a finite model of the cloud is studied and preemptive [23] and non-preemptive [11,21,22] scheduling algorithms to stabilize the system are proposed.…”
Section: Related Workmentioning
confidence: 99%
“…The infinite-server variant of our model is a generalization of the homogeneous (one server type) model studied in [13][14][15], which focused on the problem of minimizing the number of occupied servers in steadystate. In particular, GRAND algorithm was proposed and shown to be asymptotically optimal in [15].…”
Section: Related Previous Workmentioning
confidence: 99%
“…Note that the setting of the algorithm parameters a s , that achieves asymptotic optimality, depends only on the weights γ s , which are the parameters of the objective (as opposed to system parameters). One of the key qualitative insights of [15] was the surprising fact that an algorithm as simple as GRAND can be asymptotically optimal. The fact that an appropriately generalized, but still extremely simple, version of GRAND is optimal for in a heterogeneous system, is still more surprising.…”
Section: Introductionmentioning
confidence: 99%