An Adaptive Task scheduling Techniques for Cloud Computing using Nature inspired Optimization Theory
Cloud computing is a significant technology trend that provides computing resources as a revolutionary service for academic researchers and the IT industry. The efficiency of emerging cloud computing technology is mainly compatible with essential resources such as resource management. A crucial part of resource management strategies in the cloud environment depends on the cloud scheduling strategy. The cloud scheduling strategy generates and draws much interest in using resources, thereby increasing private cloud performance. Fair use of resources to overload servers is sometimes a power consumption in a private cloud. Cloud scheduling is the best solution for the efficient use of resources. It is a critical way to reduce energy consumption. The main objective of this research work is to investigate various cloud decision technologies and propose an energy-conscious cloud resolution strategy to optimize resources in the cloud computing environment. Specific functionality balances workloads between virtual machines. It reduces energy consumption through the cloud scheduling algorithm we proposed. First, the energy-conscious cloud scheduling algorithm implemented using a metaphoric approach called the Adaptive Fruit-Fly Optimization (AFO) technique. It relies on outcome-tracking behavior to balance the workload and optimize results for all tasks using the parameters the Estimated Turnaround Time (ETAT), the Response Time of Resource (RTR), energy consumption, and private cloud costs. Adaptive fruit-fly optimization (AFO-CS) for cloud scheduling improves the overall flow of the process, optimizing full time, and RTRs. This algorithm used to optimize integration speed and resources using hybrid technology. This model works best by increasing consumption rates and reducing costs. The simulation result reflects the fact that the utilization rate is 15% higher than the current model, and the performance increases.
Keywords: Cloud Computing, Cloud Scheduling, Adaptive Fruit-Fly Optimization, Queuing Model, Energy Efficiency.