A Novel Architecture with Dynamic Queues Based on Fuzzy Logic and Particle Swarm Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing is an emerging high performance computing paradigm for managing and delivering services using a large collection of heterogeneous autonomous systems with flexible computational architecture. Task scheduling is one of the most challenging aspects to improve the overall performance of the cloud computing such as response time, cost, makespan, throughput etc. Task scheduling is also essential to reduce power consumption, processing time and improve the profit of service providers by decreasing operating costs and improving the system reliability. This paper focuses on Task Scheduling using a novel architecture with Dynamic Queues based on hybrid algorithm using Fuzzy Logic and Particle Swarm Optimization algorithm (TSDQ-FLPSO) to optimize makespan and waiting time. The experimental result based on an open source simulator (CloudSim) show that the proposed TSDQ-FLPSO provides an optimal balance results, minimizing the waiting time, reducing the makespan and improving the resource utilization compared to existing scheduling algorithms.