Results 81 to 90 of about 5,171 (123)

Analytic approximations of fork-join queues

2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015
Fork-join queues characterize a network of parallel servers where an arriving job splits into subtasks, and are serviced in parallel. Exact analytic results are known for the mean response time of a two server system. For more than two parallel servers, approximations for the mean response time of both homogeneous and heterogeneous servers have been ...
P. Fiorini
exaly   +3 more sources

Approximate analysis of fork/join synchronization in parallel queues

IEEE Transactions on Computers, 1988
An approximation technique, called scaling approximation, is introduced and applied to the analysis of homogeneous fork/join queuing systems consisting of K>or=2 servers. The development of the scaling approximation technique is guided by both experimental and theoretical considerations.
A N Tantawi
exaly   +3 more sources

Generalized parallel-server fork-join queues with dynamic task scheduling

Annals of Operations Research, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mark S Squillante, Yanyong Zhang, Anand Sivasubramaniam   +2 more
exaly   +3 more sources

Power control in saturated fork-join queueing systems

Performance Evaluation, 2017
Abstract The analysis of fork-join queueing systems has played an important role for the performance evaluation of distributed systems where parallel computations associated with the same job are carried out and a job is considered served only when all the parallel tasks it consists of are served and then joined.
Andrea Marin, Sabina Rossi
exaly   +3 more sources

A hybrid solution of fork/join synchronization in parallel queues

IEEE Transactions on Parallel and Distributed Systems, 2001
A new analysis technique, dynamic-bubblesort analysis, is introduced for general K-queue first-in-first-out HFJ (homogenous fork/join queuing) systems of K/spl ges/2 . The dynamic-bubblesort model dynamically sorts the branches of the queues based on the number of the tasks waiting for synchronization in each branch.
R. Chen
exaly   +3 more sources

Speed scaling in fork-join queues

Proceedings of the 13th EAI International Conference on Performance Evaluation Methodologies and Tools, 2020
Frequency scaling plays an important power-saving role in computer systems. In fork-join systems, dynamic adaptation of the server speeds can significantly reduce system power consumption while maintaining high throughput. In previous work, we studied a rate adaptation policy that dynamically chooses server speeds based on the difference in join-queue ...
Andrea Marin, Sabina Rossi, Carey Williamson   +2 more
openaire   +2 more sources

Acyclic fork-join queuing networks

Journal of the ACM, 1989
In this paper the class of acyclic fork-join queuing networks that arise in various applications, including parallel processing and flexible manufacturing are studied. In such queuing networks, a fork describes the simultaneous creation of several new customers, which are sent to different queues.
François Baccelli, William A. Massey, Donald F. Towsley   +2 more
openaire   +3 more sources

An optimal scheduling policy for fork/join queues

Proceedings of 32nd IEEE Conference on Decision and Control, 2002
We consider the problem of optimally scheduling an arriving job on a set of homogeneous single-server queues: (i) arriving jobs consist of a random number of tasks which can be executed independently of each other; (ii) a job is completed only after all of its component tasks have finished execution; and (iii) a central dispatcher schedules the tasks ...
A.M. Makowski, R. Nelson
openaire   +2 more sources

Stochastic comparisons for fork-join queues with exponential processing times

Journal of Applied Probability, 1997
Consider a fork-join queue, where each job upon arrival splits into k tasks and each joins a separate queue that is attended by a single server. Service times are independent, exponentially distributed random variables. Server i works at rate , where μ is constant.
Frostig, Esther, Lehtonen, Tapani
openaire   +2 more sources

Introduction to Fork-Join Queues

2022
exaly   +2 more sources