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An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure
Fahmy, Ahmed
Fahmy, Ahmed
Date
2020-02
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Type
Thesis
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Description
A Master of Science thesis in Computer Engineering by Ahmed Fahmy entitled, “An Auction-Based Scheduling Approach for Minimizing Latency in Fog Computing Using 5G Infrastructure”, submitted in February 2020. Thesis advisor is Dr. Raafat Aburukba and thesis co-advisor is Dr. Taha Landolsi. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).
Abstract
The advent of the Internet of Things (IoT) has brought an unprecedented increase in the number of connected devices. Recently, IoT-based devices have been used in several applications including healthcare, data analytics, smart cities, and many others. Time-sensitive applications, such as Vehicle-to-Vehicle (V2V) communication, led to the need for an Ultra-High Reliable Low Latency Communication (URLLC). Consequently, 5G networks gained massive attention from the research community due to its ability to support enormous amounts of transfer rate. One of the main supporting computing paradigms for IoT is cloud computing, as it offers computing capabilities over the Internet. Nevertheless, cloud computing is unsuitable for time-critical applications. Hence, researchers proposed deploying fog computing as part of the 5G small cells to tackle the deficiencies of cloud computing. Many challenges arise while combining 5G technology and fog computing such as scheduling service requests across small cells to reduce the overall latency. In this work, the scheduling problem is modeled as an optimization problem with the objective of minimizing the overall latency. Furthermore, small cells are decentralized by nature. Therefore, a coordination framework is proposed to handle the interdependency between small cells. Accordingly, the decentralized scheduling problem is mapped to a combinatorial auction optimization problem. The proposed optimization model is validated using an optimization engine. The scheduling problem is known as an NP-hard problem. Thus, a decentralized heuristic solution is proposed to solve the scheduling problem in polynomial time. The proposed solution integrates a novel Simulated Annealing-Based Scheduling (SABS) and Auction-Based Winner Determination (ABWD) heuristic algorithms. To assess the performance and quality of the proposed heuristic solution, a centralized approach is used as a benchmark. Furthermore, sensitivity analysis is conducted in which the impact of each system parameter on the system behavior is investigated. The results prove the adequacy of the proposed solution as the execution time remained approximately constant, with an average of 726 μs, considering different problem sizes. Moreover, the proposed solution is found to be scalable and accommodates the exponential growth of IoT devices.