Dynamic Load Balancing

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.

The inherent dynamical in grid computing has made it extremely difficult to come up with near-optimal solutions to efficiently schedule tasks in grids. Task Scheduling plays crucial role in Grid computing. It is a challenge-able issue among scientists to achieve better results especially in makespan based on various AI methods. Nowadays, non deterministic algorithms provide better results for these tasks. In this study the task scheduling problem in Grid computing environments has been addressed. In this paper, Queen Bee Algorithm is used for resolving scheduling problem and the obtained results are compared with several Meta–heuristic Algorithms which are developed to solve the problem. As it illustrated, queen bee algorithm is declined considerably makespan and execution time parameters rather than others in different states.

A new emerging state-of-the-art challenging research area has been found in cloud computing. Cloud Computing is an idea, rely on service and delivery, it is distributed over the Internet and governed by appropriate set of protocol. In last few decades, Internet is growing rapidly as a result cloud computing and also expanded exponentially. Cloud computing is said to provide resoruces such as Software, Platform, and Infrastructure as services, namely, Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Cloud profaned the infrastructure resources like CPU, bandwidth, and memory to its end users as a part of its IaaS service. To meet the end users’ heterogeneous needs for resources it profaned and unprofane the resources dynamically, with minimal management effort of the service providers over the Internet. Thus, eliminating the need to manage the expensive hardware resources by companies and institutes. However, to satisfy the need for resources of the users on time, Cloud Service Provider (CSP) must have to maintain the Quality of Service (QoS). Service Level Agreement (SLA) is done between the Datacenters and its end users. Minimization of the violation of the SLA ensures better QoS. Research fraternity has proposed that one of the main reasons for violation of SLA is inefficient load balancing approaches in hosts that fail to ensure QoS, without missing the deadline by the distribution of dynamic workload evenly. In this paper, we propose to extend our previous work of simulated annealing-based optimized load balancing [1] by adding VM migration policy from one host to another on the basis of linear regression-based prediction policy for futuristic resource utilization. In our approach, we are going to predict short-time future resource utilization using linear regression based on the history of the previous utilization of resources by each host. We further use it in migration process to predict the overloaded hosts to underloaded ones. Experiments were simulated in CloudAnalyst and the results are quite encouraging and outperform some previous existing strategies of load balancing for ensuring QoS.

Cloud computing is a new technology that brings new challenges to all organizations around the world. Improving response time for user requests on cloud computing is a critical issue to combat bottlenecks. As for cloud computing, bandwidth to from cloud service providers is a bottleneck. With the rapid development of the scale and number of applications, this access is often threatened by overload. Therefore, this paper our proposed Throttled Modified Algorithm(TMA) for improving the response time of VMs on cloud computing to improve performance for end-user. We have simulated the proposed algorithm with the CloudAnalyts simulation tool and this algorithm has improved response times and processing time of the cloud data center.