Multi-Objective Semi-Greedy Algorithm with Adaptive Alpha Selection for Task Scheduling in Fog Environments

Multi-Objective Semi-Greedy Algorithm with Adaptive Alpha Selection for Task Scheduling in Fog Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-Objective Semi-Greedy Algorithm with Adaptive Alpha Selection for Task Scheduling in Fog Environments Abdulrahman K. Al-Qadhi, Rukshan Athauda, Rohaya Latip, Masnida Hussin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9453464/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract With the growth of demand of Artificial Intelligence workloads, the increase in energy requirements by cloud data centres is gaining a lot of attention. Fog computing is an emerging technology that offloads computing to layers near the edge of the network, reducing the workload and thus energy consumption at cloud layers. Simultaneously, fog computing minimises the network’s bandwidth use resulting in lower latency, energy consumption and improving other quality of service (QoS) for client workloads. Task scheduling in fog computing is an NP-hard problem that directly impacts the system performance and QoS. Semi-greedy algorithms are known for their fast convergence and high probability of finding near-optimal solutions. This paper proposes a multi-objective semi-greedy algorithm, termed MOSG-AAS, to minimise energy consumption, response time, makespan, and violation time while taking into consideration the tasks’ deadlines. MOSG-AAS improves on the Multi-start Priority-aware Semi-greedy (PSG-M) algorithm using an Adaptive Alpha Selection method to explore other solutions in the search space and to exploit near optimal solutions in a smart directed way by utilising the performance history of each space (alpha) and applying a stagnation penalty for alpha search space that fall in local optima. To avoid leaving certain search spaces unexplored, a probability mechanism is used to give a chance to select least performing alphas using roulette wheel algorithm. Experimental results confirm that MOSG-AAS outperforms the state-of-the-art PSG-M algorithm and other semi-greedy algorithms in terms of energy consumption, makespan, deadline violation, and convergence. Deadline-aware energy consumption fog computing quality of service resource allocation response time multi-objective optimization semi-greedy task scheduling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 17 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9453464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636284963,"identity":"202a4213-af84-4d02-b71a-a886f089b78c","order_by":0,"name":"Abdulrahman K. 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Fog computing is an emerging technology that offloads computing to layers near the edge of the network, reducing the workload and thus energy consumption at cloud layers. Simultaneously, fog computing minimises the network\u0026rsquo;s bandwidth use resulting in lower latency, energy consumption and improving other quality of service (QoS) for client workloads. Task scheduling in fog computing is an NP-hard problem that directly impacts the system performance and QoS. Semi-greedy algorithms are known for their fast convergence and high probability of finding near-optimal solutions. This paper proposes a multi-objective semi-greedy algorithm, termed MOSG-AAS, to minimise energy consumption, response time, makespan, and violation time while taking into consideration the tasks\u0026rsquo; deadlines. MOSG-AAS improves on the Multi-start Priority-aware Semi-greedy (PSG-M) algorithm using an Adaptive Alpha Selection method to explore other solutions in the search space and to exploit near optimal solutions in a smart directed way by utilising the performance history of each space (alpha) and applying a stagnation penalty for alpha search space that fall in local optima. To avoid leaving certain search spaces unexplored, a probability mechanism is used to give a chance to select least performing alphas using roulette wheel algorithm. 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