Integrated pricing and capacity decision for a telecommunication service provider

Hong, Ki-sung; Lee, Chulung

doi:10.1007/s11042-012-1030-3

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…There is no waiting room for jobs in the data center, which can be viewed as a loss queue. The assumption of loss queue model is reasonable for data centers and it is also widely used in telephone systems, computer networks, cloud computing, and so on [18,21,45]. In what follows we provide a detailed problem description for the data center.…”

Section: Problem Descriptionmentioning

confidence: 99%

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Xia

2019

Discrete Event Dyn Syst

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In this paper, we propose a novel dynamic decision method by applying the sensitivity-based optimization theory to find the optimal energy-efficient policy of a data center with two groups of heterogeneous servers. Servers in Group 1 always work at high energy consumption, while servers in Group 2 may either work at high energy consumption or sleep at low energy consumption. An energy-efficient control policy determines the switch between work and sleep states of servers in Group 2 in a dynamic way. Since servers in Group 1 are always working with high priority to jobs, a transfer rule is proposed to migrate the jobs in Group 2 to idle servers in Group 1. To find the optimal energy-efficient policy, we set up a policy-based Poisson equation, and provide explicit expressions for its unique solution of performance potentials by means of the RG-factorization. Based on this, we characterize monotonicity and optimality of the long-run average profit with respect to the policies under different service prices. We prove that the bang-bang control is always optimal for this optimization problem, i.e., we should either keep all servers sleep or turn on the servers such that the number of working servers equals that of waiting jobs in Group 2. As an easy adoption of policy forms, we further study the threshold-type policy and obtain a necessary condition of the optimal threshold policy. We hope the methodology and results derived in this paper can shed light to the study of more general energy-efficient data centers.

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Section: Problem Descriptionmentioning

confidence: 99%

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Xia

2019

Discrete Event Dyn Syst

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“…The tenth paper entitled "Integrated pricing and capacity decision for a telecommunication service provider," by Lee et al [5] studies a price and capacity decision for a telecommunication service provider that differentiates its products based on price and quality of service to maximize revenue. They first develop a mathematical model to determine the optimal product prices and capacities necessary for maximizing total profit and then consider a case where a service provider can increase or decrease the capacity marginally, and compute the optimal price and optimal capacity.…”

Section: Semantic and Social Multimedia Systemsmentioning

confidence: 99%

Editorial: advanced semantic and social multimedia technologies for future computing environment

Rho

Sauveron

Markantonakis

2012

Multimed Tools Appl

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“…The manufacturer's objective is to minimize holding costs subject to a service‐level constraint. Similarly, Hong and Lee consider the profit maximizing way of splitting a given capacity between two servers subject to linear demand function with substitution effects, waiting costs, and lateness penalties imposed when exogenously given delay guarantees are violated. de Kok et al considers a packaging facility whose capacity must be allocated among different package sizes.…”

Section: Introductionmentioning

confidence: 99%

Optimal service‐capacity allocation in a loss system

Hassin

Shaki

Yovel

2015

Naval Research Logistics

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We consider a loss system with a fixed budget for servers. The system owner's problem is choosing the price, and selecting the number and quality of the servers, in order to maximize profits, subject to a budget constraint. We solve the problem with identical and different service rates as well as with preemptive and non-preemptive policies. In addition, when the policy is preemptive we prove the following conservation law: the distribution of the total service time for a customer entering the slowest server is hyper-exponential with expectation equal to the average service rate independent of the allocation of the capacity.

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Integrated pricing and capacity decision for a telecommunication service provider

Cited by 8 publications

References 16 publications

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Optimal energy-efficient policies for data centers through sensitivity-based optimization

Editorial: advanced semantic and social multimedia technologies for future computing environment

Optimal service‐capacity allocation in a loss system

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