Scheduled System Maintenance:
On Wednesday, July 29th, IEEE Xplore will undergo scheduled maintenance from 7:00-9:00 AM ET (11:00-13:00 UTC). During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Performance analysis of distributed deadlock detection algorithms

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)
Soojung Lee ; Dept. of Comput. Educ., Inchon Nat. Univ. of Educ., South Korea ; Kim, J.L.

The paper presents a probabilistic performance analysis of a deadlock detection algorithm in distributed systems. Although there has been extensive study on deadlock detection algorithms in distributed systems, little attention has been paid to the study of the performance of these algorithms. Most work on performance study has been achieved through simulation but not through an analytic model. Min (1990), to the best of our knowledge, made the sole attempt to evaluate the performance of distributed deadlock detection algorithms analytically. Being different from Min's, our analytic approach takes the time-dependent behavior of each process into consideration rather than simply taking the mean-value estimation. Furthermore, the relation among the times when deadlocked processes become blocked is studied, which enhances the accuracy of the analysis. We measure performance metrics such as duration of deadlock, the number of algorithm invocations, and the mean waiting time of a blocked process. It is shown that the analytic estimates are nearly consistent with simulation results

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:13 ,  Issue: 4 )