Open AccessOn the Execution Time of Programs in Stochastic Scheduling
Author(s) -
Adriano K. Suzuki,
Matheus Henrique Junqueira Saldanha
Publication year - 2020
Publication title -
revista eletrônica de iniciação científica
Language(s) - English
Resource type - Journals
ISSN - 1519-8219
DOI - 10.5753/reic.2020.1746
Subject(s) - computer science , scheduling (production processes) , a priori and a posteriori , inference , estimator , execution time , cloud computing , distributed computing , mathematical optimization , artificial intelligence , mathematics , statistics , operating system , philosophy , epistemology
Scheduling appears frequently in distributed, cloud and high-performance computing, as well as in embedded systems. Here, the execution time of subtasks is the major factor influencing decision-making, and despite being random variables they are majorly treated in the literature as being deterministic. Our project intends to shed more light on the underlying distribution of execution times, attempting to verify: 1) if the usual assumption of normal distribution is reasonable; 2) if there exist more suitable distribution families; and 3) if anything can be inferred a priori by analyzing general aspects of the program. We have modeled the problem and experimentally assessed distributions, showing that they are often not normal. We suggest alternative distributions, which we released as R packages, and propose estimators that ease parameter inference. With this we hope to promote usage of stochastic scheduling by making it easier for users to define distributions when requested.