Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain Internet of Things based framework

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-12-31 DOI:10.1016/j.future.2024.107696

Osama Almurshed , Ashish Kaushal , Souham Meshoul , Asmail Muftah , Osama Almoghamis , Ioan Petri , Nitin Auluck , Omer Rana

{"title":"Enhancing performance of machine learning tasks on edge-cloud infrastructures: A cross-domain Internet of Things based framework","authors":"Osama Almurshed , Ashish Kaushal , Souham Meshoul , Asmail Muftah , Osama Almoghamis , Ioan Petri , Nitin Auluck , Omer Rana","doi":"10.1016/j.future.2024.107696","DOIUrl":null,"url":null,"abstract":"<div><div>The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for sectors such as smart agriculture, industry, and healthcare, EO-GNH integrates asynchronous MapReduce and parallel meta-heuristics to effectively manage AI applications, focusing on execution performance, resource utilization, and infrastructure resilience. The framework carefully addresses the distribution challenges of AI applications, especially Service Function Chains (SFCs), in edge-cloud infrastructures. It contains Data Flow Management, which covers aspects of data storage and data privacy, and also considers factors like regional adaptations, mobile access, and AI model refinement. EO-GNH ensures high availability for forecasting, prediction, and training AI models, operating efficiently within a geo-distributed infrastructure. The proposed strategies within EO-GNH emphasize concurrent multi-node execution, enhancing AI application placement by improving execution time, dependability, and cost-effectiveness. The efficiency of EO-GNH is demonstrated through its impact on QoS in real-time resource management across three application domains, highlighting its adaptability and potential in diverse cross-domain IoT-based environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"166 ","pages":"Article 107696"},"PeriodicalIF":6.2000,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24006605","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

The Internet of Things (IoT) and Edge-Cloud Computing have been trending technologies over the past few years. In this work, we introduce the Enhanced Optimized-Greedy Nominator Heuristic (EO-GNH), a framework designed to optimize machine learning (ML) and artificial intelligence (AI) application placement in edge environments, aiming to improve Quality of Service (QoS). Developed specifically for sectors such as smart agriculture, industry, and healthcare, EO-GNH integrates asynchronous MapReduce and parallel meta-heuristics to effectively manage AI applications, focusing on execution performance, resource utilization, and infrastructure resilience. The framework carefully addresses the distribution challenges of AI applications, especially Service Function Chains (SFCs), in edge-cloud infrastructures. It contains Data Flow Management, which covers aspects of data storage and data privacy, and also considers factors like regional adaptations, mobile access, and AI model refinement. EO-GNH ensures high availability for forecasting, prediction, and training AI models, operating efficiently within a geo-distributed infrastructure. The proposed strategies within EO-GNH emphasize concurrent multi-node execution, enhancing AI application placement by improving execution time, dependability, and cost-effectiveness. The efficiency of EO-GNH is demonstrated through its impact on QoS in real-time resource management across three application domains, highlighting its adaptability and potential in diverse cross-domain IoT-based environments.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.