ERAM2 - ENERGY BASED RESOURCE ALLOCATION WITH MINIMUM RECKON AND MAXIMUM RECKON
DOI:
https://doi.org/10.24297/jac.v12i23.30Keywords:
Cloud Computing, Virtual Machine, Resource Allocation, Minimum Reckon and Maximum Reckon, Ant Colony - Maximum Reckon and Minimum Reckon (AC-MRMR), Task Migration.Abstract
The emerging field of cloud computing has flexibility and dominant computational architecture that offers ubiquitous services to users. It is different from traditional architecture because it accommodates resources in a unified way. Due to rapid growth in demands for providing the resources and computation in cloud environments, Resource allocation is considered as primary issues in performance, efficiency, and cost. For the provisioning of resource, Virtual Machine (VMs) is employed to reduce the response time and executing the tasks according to the available resources. The users utilize the VMs based on the characteristics of the tasks for effective usage of resources. This helps in load balancing and avoids VMs being in an idle state. Several resource allocation techniques are proposed to maximize the utility of physical resource and minimize the consuming cost of Virtual Machines (VMs). This paper proposes an Energy-Based Resource Allocation with Minimum Reckon and Maximum Reckon (ERAM2); which achieves an efficient scheduling by matching the user tasks on Resource parameters like Accessibility, Availability, Cost, Reliability, Reputation, Response time, Scalability and Throughput in the terms of Maximum Reckon and Minimum Reckon. This paper proposes an Ant Colony - Maximum Reckon and Minimum Reckon (AC-MRMR) method to consolidate all the available resource based on the pheromone value; the score is calculated for each pheromone value. When the score value exceeds Threshold limit then task migration process is carried out for optimized resource allocation of tasks.
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