Heavy Traffic Limits for Queues with Many Deterministic Servers
Queueing Systems: Theory and Applications
Dynamic Routing in Large-Scale Service Systems with Heterogeneous Servers
Queueing Systems: Theory and Applications
Optimal Control of a High-Volume Assemble-to-Order System
Mathematics of Operations Research
Contact Centers with a Call-Back Option and Real-Time Delay Information
Operations Research
Manufacturing & Service Operations Management
Multiserver Loss Systems with Subscribers
Mathematics of Operations Research
Pricing and Dimensioning Competing Large-Scale Service Providers
Manufacturing & Service Operations Management
Exploiting Market Size in Service Systems
Manufacturing & Service Operations Management
Asymmetric Information and Economies of Scale in Service Contracting
Manufacturing & Service Operations Management
State Space Collapse in Many-Server Diffusion Limits of Parallel Server Systems
Mathematics of Operations Research
Exploiting network effects in the provisioning of large scale systems
ACM SIGMETRICS Performance Evaluation Review - Special Issue on IFIP PERFORMANCE 2011- 29th International Symposium on Computer Performance, Modeling, Measurement and Evaluation
Estimating the loss probability under heavy traffic conditions
Computers & Mathematics with Applications
Queueing Systems: Theory and Applications
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This paper considers pricing and capacity sizing decisions, in a single-class Markovian model motivated by communication and information services. The service provider is assumed to operate a finite set of processing resources that can beshared among users; however, this shared mode of operation results in a service-rate degradation. Users, in turn, are sensitive to the delay implied by the potential degradation in service rate, and to the usage fee charged for accessing the system. We study the equilibrium behavior of such systems in the specific context of pricing and capacity sizing under revenue and social optimization objectives. Exact solutions to these problems can only be obtained via exhaustive simulations. In contrast, we pursueapproximate solutions that exploitlarge-capacity asymptotics. Economic considerations and natural scaling relations demonstrate that the optimal operational mode for the system is close to "heavy traffic." This, in turn, supports the derivation of simple approximate solutions to economic optimization problems, via asymptotic methods that completely alleviate the need for simulation. These approximations seem to be extremely accurate. The main insights that are gleaned in the analysis follow: congestion costs are "small," the optimal price admits a two-part decomposition, and the joint capacity sizing and pricing problem decouples and admits simple analytical solutions that are asymptotically optimal. All of the above phenomena are intimately related to statistical economies of scale that are an intrinsic part of these systems.