Dynamic Resource Allocation Method for Load Balance Scheduling over Cloud Data Center Networks

TL;DR

This paper introduces DRALB, a dynamic resource allocation method for load balancing in cloud data centers that improves resource utilization, reduces SLA violations, and enhances throughput by analyzing resource demands and categorizing resources into four queues.

Contribution

The paper proposes a novel load balancing scheduling method that considers diverse client applications and optimizes resource allocation to minimize wastage and SLA violations.

Findings

Reduces network traffic by up to 58.49%.

Improves resource utilization and response time.

Lowers SLA violation rate.

Abstract

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of clients applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89 and 58.49 in the network.