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Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog- Cloud Computing Environment
Omer, Dalia Fatahelrahman
Omer, Dalia Fatahelrahman
Date
2019-05
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Type
Thesis
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Description
A Master of Science thesis in Computer Engineering by Dalia Fatahelrahman Omer entitled, “Minimizing Deadline Misses of Mobile IoT Requests in a Hybrid Fog-Cloud Computing Environment”, submitted in May 2019. Thesis advisor is Dr. Raafat Aburukba and thesis co-advisor is Dr. Taha Landolsi. Soft and hard copy available.
Abstract
The emergence of Internet of Things (IoT) has led to the rise of a variety of applications with different characteristics and Quality of Service (QoS) requirements. Those applications require computational power and have time sensitive requirements. Cloud computing paradigm provides illusion to consumers with unlimited computation resource power. However, cloud computing fails to deliver on the time-sensitive requirements of applications. The main challenge in the cloud computing paradigm is the associated delays from the edge IoT device to the cloud data center and from the cloud data center back to the edge device. Fog computing extends limited computational services closer to the edge device to achieve the time sensitive requirement of applications. The introduction of fog computing raises other challenges that are addressed in this thesis such as the mobility of edge devices, and the collaboration of multiple fog nodes and the cloud to achieve the QoS requirements of applications. The purpose of this work is to propose a scheduling solution which adopts the three-tier fog computing architecture in order to satisfy the maximum number of requests given their deadline requirements. This work takes into consideration the setting of distributed schedulers at the fog tier, and the heterogeneous IoT devices with varying degrees of mobility at the edge tier. In this thesis, an optimization model using mixed integer programming is introduced to minimize deadline misses. The proposed model is then validated with an exact solution technique. The scheduling problem is known to be an NP-hard, and hence, exact optimization solutions are inadequate for large size problems. Given the complex nature of the problem, a heuristic approach using Genetic Algorithm (GA) is presented with static and dynamic implementation. The performance of the proposed GA was evaluated and compared against round robin and priority scheduling. The results show that the deadline misses of the proposed approach is 20% to 55% better than the other techniques.