TETRA: Time- and Energy-Aware TOPSIS-based Resource Allocation

Abstract

With the exponential growth of IoT devices, there has been an increasing demand for distributed computing paradigms such as edge computing and fog computing to address the limitations of cloud computing. Resource scheduling is a critical aspect across the different layers, as it ensures that the available resources are efficiently utilized and allocated to different tasks. Most of the existing resource scheduling algorithms for fog computing environments focus primarily on performance metrics such as makespan, resource utilization, and cost separately. However, there is a need for dynamic multi-objective optimization techniques that can be energy-aware while not compromising on makespan. In this thesis, we introduce a novel resource scheduling algorithm for fog computing environments that optimizes time and energy consumption, which ensures higher performance and lower data center costs. The algorithm considers all the available Virtual Machines (VM) in the fog computing environment. Then, it uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is a multi-criteria decision analysis (MCDA) method, to identify the optimal resources. Our algorithm considers multiple computational parameters such as Million Instructions Per Second (MIPS), the number of processing cores, and thermal design power (TDP) to rank available resources. We conducted a series of experiments, and our algorithm achieves a multi-objective optimization for scheduling IoT tasks on higher-ranked resources resulting in a 7%, 19% and 25% optimization rates in makespan over Best-Fit, Greedy and First-Fit algorithms respectively. In addition, the optimizations in energy consumption over the Best-Fit, Greedy and First-Fit algorithms from our experiments were 1%, 41% and 27%, respectively.