Implementing Virtual Machines for Dynamic Resource Allocation in Cloud Computing Environment (original) (raw)
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Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment
Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness" to measure the unevenness in the multi-dimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
"Survey on Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment"
The emergence of cloud computing infrastructures brings new ways to build and manage computing system with the flexibility offer by virtualization technologies. In this context, this focuses on two principal objective First leveraging virtualization and cloud computing infrastructures to build distributed large scale computing platforms from multiple cloud providers allowed to run software requiring large amounts of computation power. Secondly developing mechanisms to make these infrastructures more dynamic. This mechanism provides inter cloud live migration offing new ways to exploit the inherent dynamic nature of distributed clouds. Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the gains in the cloud model come from resource multiplexing through virtualization technology. In this paper we proposed system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness" to measure the unevenness in the multi-dimensional resource utilization of a server. By minimizing skewness, we can add different types of workloads nicely and improve the overall utilization of server resource. We present a set of heuristics that prevent overload in the system effectively while saved energy used. Trace driven simulation and experimental results demonstrate that our algorithm achieves good performance.
Overload Avoidance for Dynamic Virtual Machine Resource Allocation Environment
Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
DYNAMIC RESOURCE ALLOCATION FOR CLOUD COMPUTING ENVIRONMENT USING VIRTUAL MACHINES
Abstract: Cloud computing allows business customers to scale up and scale down their resource usage based on their needs. Many of the gains in the cloud come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We develop a set of heuristics that prevent overload in the system effectively while saving the energy. Keywords: Cloud Computing, Resource Management, Virtualization, Green Computing. Title: DYNAMIC RESOURCE ALLOCATION FOR CLOUD COMPUTING ENVIRONMENT USING VIRTUAL MACHINES Author: B. SIREESHA, E. VENKATA RAMANA International Journal of Computer Science and Information Technology Research, ISSN 2348-120X (online) Research Publish Journals
Dynamic Resource Allocation using Virtualization Technology in Cloud Computing
Cloud computing is ubiquitous and promise a cost-effective realization of the utility computing principle, allowing users and providers easy access to resources in a self-service, pay-as-you-go fashion, thus decreasing cost for system administration and improving resource utilization and accounting. This system present the virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. The concept of skewness to measure the unevenness in the multi-dimensional resource utilization of server. In this paper we give the dynamic resource allocation strategies and green computing technology to improve the performance.
Heuristic Based Resource Allocation for Cloud Using Virtual Machines
2015
Cloud computing allows to estimate the scale of resources for business customers .We achieve this through Virtualization Technology. Virtualization can be provided significant benefits in data centers by enabling virtual machine to eliminate hotspot. Virtualization used in scenarios-load balancing, online maintenance and proactive fault, power management. In Existing System VM Monitors like Xen provide a mechanism for mapping VM to physical resources. This mapping hidden from Cloud Users.VM live migration technology makes it possible to change the mapping between VM and PM while applications are running. Proposed system presents the implementation of an automatic resource management system that achieves a balance between the two goals-Avoidance Overload, Green Computing. By this we avoid Overload and introduce the concept of skewness to measure the uneven utilization of a Server.
Cloud computing is on demand service as it offers dynamic, flexible and efficient resource allocation for reliable and guaranteed services in pay-as-you-use manner to the customers. In Cloud computing multiple cloud users can request number of cloud services simultaneously, so there must be a facility provided such that all resources are obtainable to requesting user in efficient, well organised and proper manner to satisfy their need without compromising on the performance of the resources. Cloud computing has its era and become a new age technology that has got huge importance and potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere, anytime. One of the major challenges in cloud computing is related to optimizing the resources being allocated. The other challenges of resource allocation are meeting customer demands, data center management and application requirements. Here the design, implementation, and evaluation of a resource management system for cloud computing services are presented. The system multiplexes virtual to physical resources adaptively based on the changing demand. The skewness metric is used to combine Virtual Machines (VMs) with different resource characteristics appropriately so that the capacities of servers are well utilized. The algorithm helps to achieve both overload avoidance and green computing for systems with multi resource constraints.
Automated resource management in cloud computing environment
Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multi-dimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance
A Review- Dynamic Resource Allocation using Virtual Machines for Cloud Computing Environment
2014
A B S T R A C T A cloud computing infrastructure is a complex system with a large number of shared resources. Cloud resource management requires complex policies and decisions for multi-objective optimization. Cloud computing is an effective computing model since it allows for the provision of resources on demand. In the resource management problems the Dynamic resource allocation problem is one of the most important problems. To present a better solution for solving the problem of dynamic resource allocation in a cloud computing environment, the proposed system represents the skewness algorithm to determine the unevenness in the multi-dimensional resource utilization of server. In this paper system uses virtualization technology to allocate data centre resources dynamically based on application demands and support green computing by optimizing the number of servers in use.