TASK OFFLOADING IN EDGECLOUD RESOURCE SCHEDULING USING DEPENDANT COMPUTATION TASK OFFLOAD USING ENHANCED FIREFLY (DCTO-EFF)

In order to efficiently distribute computing jobs between edge devices and cloud resources, task offloading is a crucial strategy in edge-cloud resource scheduling. Numerous optimization strategies have been put forth to maximize system performance and resource utilization. Among the cutting-edge optimization algorithms that are carefully investigated in this study are the Dependant Computation Task Offload Using Enhanced Bacterial Foraging Optimization (DCTO-EBFO), Firefly Algorithm (FA), Elephant Herding Optimization Algorithm (EHOA), Social Spider Optimization (SSO), Bee Colony Optimization (BCO), and Hybrid Grey Wolf Lion Optimization (HGWLO). However, most existing optimization strategies fail to effectively handle dependent computational tasks and often suffer from slow convergence and reduced efficiency under dynamic edge-cloud conditions. Furthermore, Proposed Dependant Computation Task Offload utilizing Enhanced Firefly (DCTO-EFF), a novel optimization method, is presented and assessed. At the beginning of the paper, the importance of optimization strategies in enhancing offloading alternatives is discussed, along with an overview of job offloading in edge-cloud resource scheduling. Next, a detailed explanation of each optimization algorithm's benefits and core concepts is provided. The suggested DCTO-EFF algorithm for work offloading is then explained, along with its features, capabilities, and advantages. Comprehensive simulations using EdgeCloudSim are used to compare the efficacy of the optimization strategies. Performance measurements include things like makespan, reaction time, energy consumption, resource utilization, latency, convergence speed, execution time, and delay. All things considered, this study provides useful details regarding the advantages and disadvantages of various optimization algorithms for task offloading in edge-cloud resource scheduling, assisting practitioners and researchers in selecting the optimal algorithm based on specific deployment scenarios and application needs.