Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization

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...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) Vol. 2021; no. 1
Main Authors: Babar, Mohammad, Khan, Muhammad Sohail, Din, Ahmad, Ali, Farman, Habib, Usman, Kwak, Kyung Sup
Format: Journal Article
Language:English
Published: Hoboken Hindawi 2021
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Ant colony optimization
Bandwidths
Bees
Cloud computing
Communication
Computation offloading
Edge computing
Energy consumption
File servers
Heuristic methods
Internet of Things
Management
Management techniques
Mathematical optimization
Network latency
Optimization techniques
Particle swarm optimization
Resource management
Response time
Response time (computers)
Scheduling
Servers
Workload
Workloads
Pakistan
ISSN:1076-2787, 1099-0526
Online Access:Get full text
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Summary: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.
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ISSN:1076-2787
1099-0526
DOI:10.1155/2021/5563531