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

A Survey on Live Virtual Machine Migrations and its Techniques

Today’s world is internet world. Almost all the people uses internet for accessing different services. In Cloud Computing various cloud consumers demand variety of services as per their dynamically changing needs over the internet. So it is the job of cloud computing to avail all the demanded services to the cloud consumers. But due to the availability of finite resources it is very difficult for cloud providers to provide all the demanded services in time. From the cloud providers’ perspective cloud resources must be allocated in a fair manner. So, it’s a vital issue to meet cloud consumers’ QoS requirements and satisfaction. Virtualization mainly abstracts the resources like CPU and Memory through Virtual Machine for efficient resource utilization. Virtual Machine Migration is one of the key technique for dynamic resource management in cloud computing. This paper mainly addresses key performance issues, challenges and techniques for live virtual machine migration in cloud computing. It also focuses on the key issues related to these existing live virtual machine migration techniques and summarizes them. Keywords: Cloud Computing, Migration, Virtualization, Virtual Machine, Physical Machine, Resource Management, Live Virtual Machine Migration

Memory Management and Reuse Mechanism for Virtual Machine in Cloud Computing to Minimize Energy Consumption : A Review Paper

Cloud computing is an emerging computing technology for large data centers that maintains computational resources through the internet, rather than on local computers. VM migration provides the capability to balance the load, system maintenance and fault tolerance, etc. However, existing migration techniques, used to migrate virtual machines keeping memory images of VMs in host and skipping transfer of unchanged memory pages to reduce the amount of transfer data during migration, if number of migrations increases, number of memory images stored on host are also increased, this causes memory starvation.  In this paper, a propose technique that reduces the size of memory image stored on source host before migration.  When a VM migrates to other host, memory images of VM is kept in the source host after removing unwanted data according to the Probability factor. When the VM migrates back to the original host later, the kept memory image will be “reused”, i.e. data which are identical to the kept data will not be transferred and comparative to existing system the size of memory image is small. To validate this approach, evaluate the results using different threshold levels and probability factor of change in data. Proposed system required less memory to store the memory image and allow more VMs to be hosted. Specifically, proposed work is used to improve resource efficiency throughout by reducing the size of memory image that is stored on source host. Keywords: Cloud computing, Migration, Virtualization, Virtual Machine, Physical Machine, Live Virtual Machine Migration

Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201