Aug 2026· Indian Journal of Computer Science and Technology· pp. 964-971· 0 citations
TL;DR
An in-depth review of computation offloading algorithms within replicated fog computing environments, a critical area for modern distributed systems, with significant focus on the complex interplay between offloading and replication.
Abstract
This paper provides an in-depth review of computation offloading algorithms within replicated fog computing environments, a critical area for modern distributed systems. It delineates the foundational concepts of fog computing and data replication, exploring their architectures, characteristics, and inherent challenges. The paper then systematically examines various offloading mechanisms and objectives, categorizing state-of-the-art algorithms, including optimization-based, machine learning-driven, and dynamic resource management approaches. A significant focus is placed on the complex interplay between offloading and replication, particularly concerning data consistency, fault tolerance, resource heterogeneity, and security. Through an analysis of real-world applications and identified research gaps, this review aims to serve as a foundational resource for PhD researchers, guiding future investigations into more robust, efficient, and intelligent offloading strategies in highly dynamic and resource-constrained fog environments.
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements, yet real deployments often involve a mix of devices with different processing abilities, communication characteristics, and power constraints. These differences make it difficult to decide when and where tasks should be offloaded.
This study introduces a task-offloading approach that adapts to changing conditions in a heterogeneous fog environment. The method continuously observes factors such as processor utilization, task size, communication delay, and the remaining energy of participating devices. Using this information, the system determines whether a task should run on the originating device, a nearby fog node, or the cloud. The approach aims to limit unnecessary transfers while striking a balance between energy use and execution delay.
Simulation experiments conducted in iFogSim indicate that the proposed strategy consistently improves performance over conventional static or energy-unaware schemes. The results show notable reductions in overall energy usage and significant improvements in task-completion success under varying network loads. These findings suggest that integrating real-time monitoring with adaptive decision-making can strengthen the efficiency and responsiveness of fog-based IoT systems.
Ashish Bagla, Deepak Dagar, Pratik Srivastava· International Journal For Mu...· 0 citations
This research proposes a cost-aware, genetic-based task scheduling algorithm tailored for fog-cloud environments, which seeks to improve cost efficiency for real-time applications with strict deadlines, and demonstrates that the proposed algorithm surpasses existing techniques like Round-Robin and Trade-off algorithms.
Youssef Oukissou, Hamza Elhaou, Driss Ait Omar et al.· 1 citation
A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.
S. Alanazi· Journal of Advances in Infor...· 0 citations
This analysis identifies critical VM scheduling trade-offs, provides optimization guidelines, and validates the efficacy of hybrid adaptive methods via a new proposed heuristic-machine learning model for dynamic cloud environments.
Chaimae Bahij, Mohamed El Ghmary, Hassan Echoukairi· EPJ Web of Conferences· 0 citations
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
The paper argues for a shift from proof of concept scheduling studies toward reproducible, transparent, and deployable fog systems, and identifies several priorities for future work: standardized benchmark workloads, cloud native scheduling that accounts for container lifecycle and microservice dependencies, resilience aware scheduling that treats failures and migration as first class concerns, and carbon aware orchestration that extends beyond energy minimization.
Albahlool Abood· Asian Journal of Research in...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
