A Systematic Review on Various Task Scheduling Algorithms in Cloud Computing
DOI:
https://doi.org/10.4108/eetiot.4548Keywords:
Task Schedduling, Machine Learning, Cloud Computing, Nature-inspired algorithmsAbstract
Task scheduling in cloud computing involves allocating tasks to virtual machines based on factors such as node availability, processing power, memory, and network connectivity. In task scheduling, we have various scheduling algorithms that are nature-inspired, bio-inspired, and metaheuristic, but we still have latency issues because it is an NP-hard problem. This paper reviews the existing task scheduling algorithms modelled by metaheuristics, nature-inspired algorithms, and machine learning, which address various scheduling parameters like cost, response time, energy consumption, quality of services, execution time, resource utilization, makespan, and throughput, but do not address parameters like trust or fault tolerance. Trust and fault tolerance have an impact on task scheduling; trust is necessary for tasks and assigning responsibility to systems, while fault tolerance ensures that the system can continue to operate even when failures occur. A balance of trust and fault tolerance gives a quality of service and efficient task scheduling; therefore, this paper has analysed parameters like trust and fault tolerance and given research directions.
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