By Topic

Load sharing with consideration of future task arrivals in heterogeneous distributed real-time systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chao-Ju Hou ; Dept. of Electr. & Comput. Eng., Wisconsin Univ., Madison, WI, USA ; Shin, K.G.

In a heterogeneous distributed real-time system, transferring an unguaranteed task at a node to another node currently with the most abundant resources is not necessarily the best decision. We propose a new load sharing (LS) algorithm for real-time applications which takes into account the effect of future task arrivals on locating the best receiver for each unguaranteed task. Upon arrival of a task at a node, the node first checks whether it can complete the task in time using the minimum-laxity-first-served discipline. If the node cannot guarantee the arrived task, or if some of existing guarantees were to be invalidated as a result of inserting the task into its queue, then the node must locate a remote node to which each unguaranteed task is to be transferred. The LS algorithm minimizes not only the probability of transferring an unguaranteed task T to an incapable node with Bayesian analysis, but also the probability that a remote node fails to guarantee T because of future arrivals of tighter-laxity tasks with queueing analysis. All parameters needed for a node's LS decision are collected/estimated online using time-stamped region-change broadcasts (TSRCBs) and Bayesian estimation. By using TSRCBs, the collected state information can be used to estimate other nodes' states. Use of Bayesian estimation makes the LS algorithm adaptive to dynamically varying workloads with little computational overhead. Simulation results show that the proposed LS algorithm outperforms other LS algorithms in minimizing the probability of dynamic failure, task collisions and excessive task transfers

Published in:

Computers, IEEE Transactions on  (Volume:43 ,  Issue: 9 )