Agent-based Resource Allocation Research Articles

An Energy-Aware Agent-Based Resource Allocation Using Targeted Load Balancer for Improving Quality of Service in Cloud Environment

In order to manage the load on dispersed data centers and cut down on energy established on time usage, agent-based resource allocation is given attention. Using a targeted load balancer (TLB), we suggest an energy-aware agent-based resource allocation in this research to enhance quality of service in a cloud setting. This agent is first set up to keep track of the resource load resulting from the request that has been assigned a job. Cloud watch also keeps an eye on energy levels to determine the typical payload size of resource execution. The TLB establishes new instance state to assign the resource based on the payload weight. To shorten the execution time, the dynamic hyper switching model develops a balancing mechanism. The suggested system achieves high performance in resource management by creating load balancer that is efficiently targeted to cut down on computation time and cost depending on energy levels. In comparison to existing techniques, the suggested parallelized homogeneous job in the cloud environment produces greater performance up to 95.5% while maintaining the time execution utilizing switching state of execution. This maintains the reduced CPU consumption, which dependent on the lowering of temporal complexity.

Agent based Resource Allocation Mechanism Focusing Cost Optimization in Cloud Computing

A cloud computing environment offers a simplified, centralized platform or resources for use when needed on low cost. One of the key functionality of this type of computing is to allocate the resources on an individual demand. However, with the expanding requirements of cloud user, the need of efficient resource computing is also emerging. The main role of service provider is to effectively distribute and share the resources which otherwise would result into resource wastage. In addition to the user getting the appropriate service according to request, the cost of respective resource is also optimized. In order to surmount the mentioned shortcomings, this paper proposes a new agent based optimized resource assignment algorithm which is not only responsible for searching comprehensive services but also considers reducing the cost of virtual machines which are consumed by on-demand services only.

An Agent-based Resource Allocation Model for computational grids

The paper presents an Agent-based Resource Allocation Model (ARAM) for grid computing. Three types of agents, namely Job Agents (JAs), Resource Brokering Agents (RBAs), and Resource Monitoring Agents (RMAs), are used. The JAs are mobile and they repr