English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a new scheduler applying the concept of non-uniformlaxity to Earliest deadline first (EDF) approach for aperiodic tasks. Thisscheduler improves task utilisation (Execution time / deadline) and alsoincreases the number of tasks that are being scheduled. Laxity is a measure ofthe spare time permitted for the task before it misses its deadline, and iscomputed using the expression (deadline - (current time + execution time)).Weight decides the priority of the task and is defined by the expression(quantum slice time / allocated time)*total core time for the task. Quantumslice time is the time actually used, allocated time is the time allocated bythe scheduler, and total core time is the time actually reserved by the corefor execution of one quantum of the task. Non-uniform laxity enables schedulingof tasks that have higher priority before the normal execution of other tasksand is computed by multiplying the weight of the task with its laxity. Thealgorithm presented in the paper has been simulated on Cheddar, a real timescheduling tool and also on SESC, an architectural simulator for multicoreplatforms, for upto 5000 random task sets, and upto 5000 cores. This schedulerimproves task utilisation by 35% and the number of tasks being scheduled by36%, compared to conventional EDF.

Application of non-uniform laxity to EDF for aperiodic tasks to improve task utilisation on multicore platforms