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Fog computing (FC) is a cloud computing layer which extends cloud services to the edge layer or to the end user computing with the intension of optimal service provisioning and faster processing capabilities among the end users, it is not the intention of FC to replace cloud computing capability, but is to provide faster accessibility of the cloud services which includes; storage, processing and computation to the end users (Pradeep & Sharma, 2019).
With the increase in demand of cloud resources, task scheduling is one of the greatest challenge and area of interest among researchers in the field of cloud computing, it is generally known that the main function of cloud computing is resources provisioning, therefore many strategies of resource scheduling and optimization methods were used by many researchers in the area (Aliyu, Murali, Gital, & Boukari, 2020).
According to Aliyu, et al., (2021) Task scheduling can be classified and categorised based on real time, cloud services, workflow, or can be static or dynamic scheduling. Many dynamic scheduling techniques have been envesaged based on metaheuristics and deterministic to resolve scheduling prolems. Related research can be observed in (Li, Liu, Wu, & Li, 2019; Matrouk & Alatoun, 2021) and etc. Relavant cited works Verma, Bhardwaj, & Yadav, (2016) discused task allocation and scheduling techniques in Fog computing.
Amancio da Silva, Asamooning, Orrillo, Sofia, & Mendes, (2020) presented data placement algorithms in fog environment which further stated virtual machine selection and virtual machine allocation can be combined to optimise task scheduling that will be assigned to Cloud, therby efficient allocation of resource modules in the fog networks. Deteministic method is a technique of problem solving which follows a trigent sequence of defined procedure in solving a solution to a task which sometime are categorised as inconclusive/inaccurate, as a result of traping in to the local minium (Aliyu, Murali, Gital, & Boukari, 2020).
The concept of Meta-heuristics algorithms are set of problem solving method in which are desinged to find, select, or generate a heuristic that can produce a significantly better result to an optimization problem with few iterations. This algorithm provide better result through exploration and exploitation specifically with very limited computational effort or non complete information which is applicable to wide number of problems for task scheduling in fog computing. The following are examples of meta-heuristics algorithms; Particle Swarm Optimization (PSO), Cuckoo Search (CS), Cat Swarm Optimization (CSO), genetic algorithm (GA) and so on. These algorithms have been used and reasonably performed better in optimizing task scheduling and cloud service providers throughput in the area of minimizing makespan, balancing load and scalability by providing shortest optimal results within shortest period (Aliyu, et al., 2021).
The main purpose of this study is to propose a framework for an energy efficient task scheduling algorithm based on hybrid meta-heuristics optimization in fog computing environment with the intention of reducing energy consumption, improving response time and resource utilization in fog computing environment.
The paper is organized based on the following sections; section I presented the introduction of the paper which includes general terms and background about fog computing, section II presented the layers of fog computing which includes the general architecture of fog computing, section III presented the literatures that are related to fog computing with regards to task scheduling, section IV presented the methodology of the proposed system design which include the system architecture and the various parameters required for the development of the proposed system and finally section V presented the conclusion and future directions of the research.