Incentives for Shared Services: Multi-Server Queueing Systems with Priorities

2021 ◽  
Author(s):  
Hanlin Liu ◽  
Yimin Yu
Keyword(s):  
Queueing Systems ◽  
Shared Services ◽  
Multi Server

scholarly journals Zero Queueing for Multi-Server Jobs

2021 ◽  
Vol 5 (1) ◽  
pp. 1-25
Author(s):  
Weina Wang ◽  
Qiaomin Xie ◽  
Mor Harchol-Balter
Keyword(s):  
Cloud Computing ◽  
Stability Region ◽  
Transient Analysis ◽  
Queueing Systems ◽  
Sufficient Conditions ◽  
Hard Problem ◽  
Queueing Model ◽  
Multiple Servers ◽  
First Results ◽  
Multi Server

Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of complexity to the traditional one-server-per-job model: an arrival might not "fit'' into the available servers and might have to queue, blocking later arrivals and leaving servers idle. From a queueing perspective, almost nothing is understood about multi-server job queueing systems; even understanding the exact stability region is a very hard problem. In this paper, we investigate a multi-server job queueing model under scaling regimes where the number of servers in the system grows. Specifically, we consider a system with multiple classes of jobs, where jobs from different classes can request different numbers of servers and have different service time distributions, and jobs are served in first-come-first-served order. The multi-server job model opens up new scaling regimes where both the number of servers that a job needs and the system load scale with the total number of servers. Within these scaling regimes, we derive the first results on stability, queueing probability, and the transient analysis of the number of jobs in the system for each class. In particular we derive sufficient conditions for zero queueing. Our analysis introduces a novel way of extracting information from the Lyapunov drift, which can be applicable to a broader scope of problems in queueing systems.

Impact of Behavioral Factors on Performance of Multi-Server Queueing Systems

2017 ◽  
Author(s):  
Hung Do ◽  
Masha Shunko ◽  
Marilyn T. Lucas ◽  
David A. Novak
Keyword(s):  
Queueing Systems ◽  
Behavioral Factors ◽  
Multi Server

Incentives for Shared Services: Multiserver Queueing Systems with Priorities

2021 ◽  
Author(s):  
Hanlin Liu ◽  
Yimin Yu
Keyword(s):  
Cooperative Game ◽  
Cost Allocation ◽  
Service Providers ◽  
Queueing Systems ◽  
Service Level ◽  
Problem Definition ◽  
Shared Services ◽  
Scheduling Policy ◽  
Shared Service ◽  
Managerial Implications

Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.

Comparing multi-server queues with finite waiting rooms, II: Different numbers of servers

1979 ◽  
Vol 11 (02) ◽  
pp. 448-455 ◽  
Author(s):  
David Sonderman
Keyword(s):  
Probability Space ◽  
Queueing Systems ◽  
Stochastic Order ◽  
Sample Path ◽  
System Size ◽  
Waiting Room ◽  
Virtual Waiting Time ◽  
Number Of Customers ◽  
Multi Server ◽  
Quantities Of Interest

We compare two queueing systems with identical general arrival streams, but different numbers of servers, different waiting room capacities, and stochastically ordered service time distributions. Under appropriate conditions, it is possible to construct two new systems on the same probability space so that the new systems are probabilistically equivalent to the original systems and each sample path of the stochastic process representing system size in one system lies entirely below the corresponding sample path in the other system. This construction implies stochastic order for these processes and many associated quantities of interest, such as a busy period, the number of customers lost in any interval, and the virtual waiting time.

Comparing multi-server queues with finite waiting rooms, I: Same number of servers

1979 ◽  
Vol 11 (2) ◽  
pp. 439-447 ◽  
Author(s):  
David Sonderman
Keyword(s):  
Queueing Systems ◽  
Stochastic Comparisons ◽  
Waiting Room ◽  
Service Times ◽  
Interarrival Times ◽  
Multi Server

We compare two queueing systems with the same number of servers that differ by having stochastically ordered service times and/or interarrival times as well as different waiting room capacities. We establish comparisons for the sequences of actual-arrival and departure epochs, and demonstrate by counterexample that many stochastic comparisons possible with infinite waiting rooms no longer hold with finite waiting rooms.

scholarly journals Optimal server assignment in multi-server queueing systems with random connectivities

2019 ◽  
Vol 21 (4) ◽  
pp. 405-415
Author(s):  
Hassan Halabian ◽  
Ioannis Lambadaris ◽  
Yannis Viniotis
Keyword(s):  
Queueing Systems ◽  
Multi Server
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