Resource Allocation and Task Scheduling in Cloud Computing Using Improved Bat and Modified Social Group Optimization

Nowadays, Cloud computing is growing quickly with a maximum number of users and clients that are reaching from single users to huge business organizations. The cloud is facing significant challenges in managing large volumes of data and several cloud resources due to its rapid growth. Several optimization algorithms faced a challenge in scalability when working with more complicated scheduling scenarios. A combination of Resource Allocation and Task Scheduling using Improved Bat Optimization Algorithm (IBOA) by using dynamic weights with the speed update formula and increasing the random searchability and Modified Social Group Optimization (MSGO) by modifying the acquiring phase is introduced in this research. The developed IB and MSGO methods are simulated using a various simulation setup and the obtained results like response time, utilization of resources and energy consumption are compared against existing methods to measure the efficiency. For 100 tasks, the proposed IB and MSGO method achieved 32.5 watts of energy. The obtained results exposed that IB and MSGO method has better performance compared with the existing method namely Multi-Objective Task Scheduling Grey Wolf Optimization (MOTSGWO) in terms of energy consumption.