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Abstract
We consider a parallelserver system with homogeneous servers where incoming tasks, arriving at rate λ, are dispatched by n dispatchers, each of them balancing a fraction 1/n of the load to K/n servers. Servers are firstcomefirstserved (FCFS) queues and dispatchers implement size interval task assignment policy with equal load (SITAE), a sizebased policy such that the servers are equally loaded. We compare the performance of a system with n>1 dispatchers and a single dispatcher. We show that the performance of a system with n dispatchers, K servers, and arrival rate λ coincides with that of a system with one dispatcher, K/n servers, and arrival rate λ/n. We define the degradation factor as the ratio between the performance of a system with K servers and arrival rate λ and the performance of a system with K/n servers and arrival rate λ/n. We establish a partial monotonicity on n for the degradation factor and, therefore, the degradation factor is lower bounded by one. We then investigate the upper bound of the degradation factor for particular distributions. We consider two continuous service time distributions: uniform and bounded Pareto and a discrete distribution with two values, which is the distribution that maximizes the variance for a given mean. We show that the performance degradation is small for uniformly distributed job sizes but that for Bounded Pareto and two points distributions it can be unbounded. We have investigated the degradation using the distribution obtained from real traces.
Original language  English 

Article number  8672485 
Pages (fromto)  875  888 
Number of pages  14 
Journal  IEEE/ACM Transactions on Networking 
Volume  27 
Issue number  2 
Early online date  2019 
DOIs  
Publication status  Published  1 Apr 2019 
MoE publication type  A1 Journal articlerefereed 
Keywords
 Degradation
 Servers
 Routing
 Task analysis
 Dispatching
 IEEE transactions
 Time factors
 Parallelserver routing
 performance degradation
 economies of scale.
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 1 Finished

Faster Queues for Big Data  Nopeammat jonot suurten tietomassojen käsittelyyn
Hyytiä, E., Aalto, S., Viitasaari, L., Osti, P., Wu, X. & Bilenne, O.
01/01/2016 → 31/12/2017
Project: Academy of Finland: Other research funding