In EDS ansehen

Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems.

Gespeichert in:
Bibliographische Detailangaben
Titel: Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems.
Autoren: Alam, Mahfooz, Haidri, Raza Abbas, Shahid, Mohammad
Quelle: International Journal of Pervasive Computing & Communications; 2020, Vol. 16 Issue 2, p113-141, 29p
Abstract: Purpose: Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks. Design/methodology/approach: To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration. Findings: The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment. Originality/value: Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Pervasive Computing & Communications is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:Purpose: Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks. Design/methodology/approach: To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration. Findings: The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment. Originality/value: Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0. [ABSTRACT FROM AUTHOR]
ISSN:17427371
DOI:10.1108/IJPCC-10-2019-0081