A Genetic Algorithm Scheduling Approach for Virtual Machine Resources in a Cloud Computing Environment

In the present cloud computing environment, the scheduling approaches for VM (Virtual Machine) resources only focus on the current state of the entire system. Most often they fail to consider the system variation and historical behavioral data which causes system load imbalance. To present a better approach for solving the problem of VM resource scheduling in a cloud computing environment, this project demonstrates a genetic algorithm based VM resource scheduling strategy that focuses on system load balancing. The genetic algorithm approach computes the impact in advance, that it will have on the system after the new VM resource is deployed in the system, by utilizing historical data and current state of the system. It then picks up the solution, which will have the least effect on the system. By doing this it ensures the better load balancing and reduces the number of dynamic VM migrations. The approach presented in this project solves the problem of load imbalance and high migration costs. Usually load imbalance and high number of VM migrations occur if the scheduling is performed using the traditional algorithms

Analysis and Development of Efficient Task Scheduling Strategies in Heterogeneous Cloud Environment

In recent years, Cloud computing has become the integral part of information technology. Lots of research is being done from academic level to industry level. Cloud computing provides service to the users through internet and other distributed network environment on pay as you use basis and user demand basis. It provides an virtual environment of computing resources which can be utilized by cloud users and cloud applications. Scheduling in cloud systems is one of the biggest challenge. An efficient task scheduler is that which is flexible according to the changing environment of clouds and complexity of the submitted tasks. Efficient use of system and getting highest performance of the system is the primary goal of any task scheduling algorithm. Cloud service providers always struggles with problems such as load balancing, Task completion time and wastage of resources. This thesis basically focuses on Task completion time of tasks submitted to the virtual Machines (VMs). Multiple experiments has been performed in CloudSim 3.0.3 simulation toolkit. All the experimental results have been obtained from CloudSim by using base classes and libraries provided in toolkit. Without using any single physical machine CloudSim library gives an full environment for development and research the different techniques for simulation and modelling. Few most generic task scheduling strategies have been studied for this thesis. Based on the study a new strategy has been proposed. This new strategy is named as SCHFMC algorithm, it’s description and study has been provided in chapter 4. SCHFMC algorithm helps in allocating the tasks to the virtual machines (VMs) with varying processing capacity. It has an efficient way to utilise the full processing power of machine so that system can be active and alive without any failure. This algorithm reduces the total completion time of all tasks submitted to the virtual machines. This algorithm has performed better than generic task scheduling meth

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Load Balancing and Virtual Machine Allocation in Cloud-based Data Centers

As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of Virtual Machines has been simulated on CloudSim – a Java based cloud simulator