An Efficient Load Distribution Approach for Optimizing Resources in SDN‐Based Edge Computing Environment DOI

Ajay Nain,

Sophiya Sheikh, Mohammad Shahid

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(12-14)

Опубликована: Май 14, 2025

ABSTRACT In the rapidly evolving networking and communication technology era, emergence of novel edge computing paradigms helps reduce latency improve efficiency. The advancements bring data processing closer to its source, reducing distance. Moreover, integrating Software‐Defined Networking (SDN) in enhances network management by decoupling control plane from plane, enabling more flexible efficient resource allocation distributed environments. However, scheduling, allocation, load balancing are significant obstacles enhancing resources' performance. Besides, help use all resources optimize system's performance effectively. To address these issues, this paper proposed an Average‐Based Resource Allocation Load Balancing (ABRL) algorithm for task balancing, which aims minimize task's completion time enhance utilization. A three‐layer SDN‐based architecture is designed implement that improves simulation studies have been conducted using OpenDaylight (ODL) controller implemented Python. Experimental results demonstrate strategy optimizes makespan, average utilization, level under consideration exhibits better than existing state‐of‐the‐art techniques.

Язык: Английский

Multi‐Criteria Optimization of Scientific Workflow Schedules for Improved Energy Efficiency in Cloud Infrastructures DOI
Nadia Dahmani, Hatem Aziza, Hajer Ben-Romdhane

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(9-11)

Опубликована: Апрель 9, 2025

ABSTRACT Rising global dependence on cloud services has become crucial for enterprises, aiming to guarantee continuous data accessibility while pursuing enhanced energy efficiency and minimized carbon emissions from centers. However, the persistent challenge of high‐energy consumption in these facilities necessitates a concentrated approach toward reduction. This paper introduces an innovative multi‐objective scheduling strategy scientific workflows, tailored heterogeneous computing environments. Our method employs hybrid genetic algorithm, incorporating Hill Climbing generate initial population chromosomes. Subsequently, algorithm optimizes task assignments most suitable virtual machines, utilizing meticulously designed fitness function evaluate each chromosome's suitability solving problem. Through extensive experimentation, we demonstrate that our proposed outperforms other techniques terms solution quality, contributing reduced consumption, processing duration, cost. We contend this holds substantial potential mitigating footprint associated with centers, offering sustainable environmentally conscious workflow scheduling.

Язык: Английский

Процитировано

0
The mapping trick: leveraging RoboSoccer obstacle avoidance and navigation for advanced task scheduling solutions in foggy IoE ecosystems DOI
Seyed Omid Azarkasb, Seyed Hossein Khasteh

The Journal of Supercomputing, Год журнала: 2025, Номер 81(5)

Опубликована: Апрель 15, 2025

Язык: Английский

Процитировано

0
An Efficient Load Distribution Approach for Optimizing Resources in SDN‐Based Edge Computing Environment DOI

Ajay Nain,

Sophiya Sheikh, Mohammad Shahid

и другие.

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(12-14)

Опубликована: Май 14, 2025

ABSTRACT In the rapidly evolving networking and communication technology era, emergence of novel edge computing paradigms helps reduce latency improve efficiency. The advancements bring data processing closer to its source, reducing distance. Moreover, integrating Software‐Defined Networking (SDN) in enhances network management by decoupling control plane from plane, enabling more flexible efficient resource allocation distributed environments. However, scheduling, allocation, load balancing are significant obstacles enhancing resources' performance. Besides, help use all resources optimize system's performance effectively. To address these issues, this paper proposed an Average‐Based Resource Allocation Load Balancing (ABRL) algorithm for task balancing, which aims minimize task's completion time enhance utilization. A three‐layer SDN‐based architecture is designed implement that improves simulation studies have been conducted using OpenDaylight (ODL) controller implemented Python. Experimental results demonstrate strategy optimizes makespan, average utilization, level under consideration exhibits better than existing state‐of‐the‐art techniques.

Язык: Английский

Процитировано

0