Abstract
Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service (QoS) becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony (ABC) optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications.
Highlights
Internet of ings (IoT) is reshaping the technological landscape of traditional systems. e concept of IoT-based smart system is making its way from dreams to reality [1]
We propose an Artificial Bee Colony (ABC) optimization technique that balances the workload over the edge server, provides the right resource for offloading device, and exploits low latency interconnections between IoT device and server. e proposed framework can be effectively utilized for IoT devices, where the task is executed under strict energy with the required latency to meet the high Quality of Service (QoS) requirements, which is very unlikely to get using the traditional cloud
We presented the ABC algorithm for computation offloading at IoT edge. e objective function measures the service time and energy consumption for the solution provided by the optimization algorithm. e main purpose is to minimize the objective function, which searches for minimum computational cost and job latency. ere are three decision variables: si, ti, and ξ. e input for the algorithm is a set of jobs and nodes
Summary
Internet of ings (IoT) is reshaping the technological landscape of traditional systems. e concept of IoT-based smart system is making its way from dreams to reality [1]. Storage, and communication services at the edge of a network, resulting in low latency, high bandwidth, and energy-efficiency [9] Both the edge and fog computing architectures have been used to handle resource-scarcity of IoT devices [10]. (i) We devised a classical three-tier framework by integrating edge and cloud to simulate computation offloading process following strict energy and latency constraint for delay-sensitive tasks. (ii) To effectively balance the workload over edge servers, we propose ABC optimization technique based on swarm intelligence, where the objective function is set to achieve the minimum computation cost and low latency for the offloaded tasks. It shows the computation offloading algorithm based on Artificial Bee Colony.
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