Abstract

Efficient task scheduling, aimed at maximizing resource utilization (such as CPU, memory, and bandwidth) while executing a maximum number of tasks, is crucial in large-scale cloud computing and associated architectures (e.g., Fog/Edge/IoT). These architectures are employed to support new business models and ensure uninterrupted services, even with intermittent connections to cloud servers. Resource optimization plays a vital role in determining the quality of service (QoS) provided to customers. Furthermore, task scheduling for parallel processing is fundamental for comprehending resource utilization, inter-process communication, network latency, load balancing, job migration, and fault tolerance. This research paper endeavours to explore, analyse, design, and implement scheduling algorithms that optimize the utilization of computing resources. The newly developed algorithm exhibits improved performance compared to existing ones. The results are interpreted and substantiated based on various QoS indicators. General terms Algorithms

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