Optimizing resource allocation and delay in fog computing using game theory

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Optimizing resource allocation and delay in fog computing using game theory

ارائه دهنده: Provider: Seyed poorya ahmadi

اساتید راهنما: Supervisors: Dr. Mahdi Abbasi

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Dr. mehdi sakhaienia and Dr mehdi mohamadi

زمان و تاریخ ارائه: Time and date of presentation: 2022/12/3

مکان ارائه: Place of presentation: Virtual room

چکیده: Abstract: With the increasing development of smart devices, the Internet of Things (IoT) is also developed. Subsequently, the amount of produced data and the computational loads extremely increased. That's why cloud computing, is used as a solution to handle the huge volumes of workloads. However, the considerable delay in processing loads in the cloud is still considered as the main issue in the field of distributed computing networks. Processing workloads at the edge of the network can reduce the response time, but on the other hand, it results in energy constraints by bringing load processing from data centers, which are supplied by electrical energy sources, to the edges of the network which are only supported by limited energies of batteries. Therefore, workloads need to be distributed evenly between the clouds and the edges of the network. In this research, we present a load balancing and scheduling method for fog computing environments based on game theory and ant colony algorithm. The proposed algorithm not only balances the load, but also monitors the priority of tasks removed from the loaded virtual machines. Tasks removed from these virtual machines act fairly as they update information globally. This algorithm also considers the priorities of the tasks. Load balance improves overall processing power, and priority load balancing focuses on reducing duty waiting time in the virtual machine queue; Therefore, the response time of virtual machines is reduced. We have compared our proposed algorithm with other available techniques. The results show that our algorithm is good without increasing additional costs. This load balancing method works for heterogeneous fog computing systems and is non-preventive for balancing independent tasks. The simulation results show that the proposed method performs better than the previous methods due to the equitable load distribution and reduces energy consumption by 9% and reduces the delay at the grid edge by 7%

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