DYNAMIC RESOURCE ALLOCATION USING VIRTUAL MACHINES FOR CLOUD COMPUTING ENVIRONMENT

ABSTRACT 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

Virtualization in Cloud Computing : Developments and Trends

Cloud computing is an interesting paradigm that is making computing and other related activities easy for consumers. The cloud infrastructure is not new, but it is working on new technology based on various services offered. The cloud provides application software online for users to conduct common activities like word processing. Cloud computing also enables consumers to leverage on cloud infrastructure by designing and deploying their application on the cloud. A unique feature of the cloud is the provision of scalable storage for data which are usually spread across several geographical locations. A core technology used on the cloud is virtualization. This allows virtual machines to be hosted on physical servers. This provides great benefits to users on the cloud. This paper presents the state of the art from some literature available on cloud virtualisation. The study was executed by means of review of some literature available on cloud virtualisation. The study was performed by means of review of some literature using reliable methods. This paper examines present trends in the area of cloud virtualisation and provides a guide for future research. In the present work, the objective is to answer the following question: what is the current trend and development in cloud virtualisation? Papers published in journals, conferences, white papers and those published in reputable magazines were analysed. The expected result at the end of this review is the identification of trends in cloud virtualisation. This will be of benefit to prospective cloud users and even cloud providers

Survey on dynamic resource allocation strategy in cloud computing enviornment

Abstract-Cloud computing becomes quite popular among cloud users by offering a variety of resources. This is an on demand service because it offers dynamic flexible resource allocation and guaranteed services in pay as-you-use manner to public. In this paper, we present the several dynamic resource allocation techniques and its performance. This paper provides detailed description of the dynamic resource allocation technique in cloud for cloud users and comparative study provides the clear detail about the different techniques

Virtualization Technology to Allocate Data Centre Resources Dynamically Based on Application Demands in Cloud Computing

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Load Balancing and Resource Allocation Model for SaaS Applications with Time and Cost constraints forcloud-computing

Instead of Traditional Software, nowadays we are using Cloud Computing. It enables the on-going revenue for software providers..Advancement of Cloud Computing due to use of well established research in Web Services, networks, utility computing and virtualization has resulted in many advantages in cost, flexibility and availability for service users. These advantages has further increased the demand for Cloud Services, increasing both the Cloud's customer base and the scale of Cloud installations. This has resulted in many technical issues in Service Oriented Architectures and Internet of Services (IoS) type applications such as high availability and scalability, fault tolerance. Central to these issues is the establishment of effective load balancing techniques. In this paper focus on the load balancing and resources provisioning approaches.Here, using the linear programming approach for dynamically allocates the resources with balancing the load.Mainly focus on the time and cost constraints. DOI: 10.17762/ijritcc2321-8169.15072

A Simulation-based Approach to Optimize the Execution Time and Minimization of Average Waiting Time Using Queuing Model in Cloud Computing Environment

Cloud computing is the emerging domain in academia and IT Industry. It is a business framework for delivering the services and computing power on-demand basis. Cloud users have to pay the service providers based on their usage. For enterprises, cloud computing is the worthy of consideration and they try to build business systems with lower costs, higher profits and quality-of-service. Considering cost optmization, service provider may initially try to use less number of CPU cores and data centers. For that reason, this paper deals with CloudSim simulation tool which has been utilized for evaluating the number of CPU cores and execution time. Minimization of waiting time is also a considerable issue. When a large number of jobs are requested, they have to wait for getting allocated to the servers which in turn may increase the queue length and also waiting time. This paper also deals with queuing model with multi-server and finite capacity to reduce the waiting time and queue length

Machine learning based Model for Cloud Load Prediction and Resource Allocation

Elasticity and the lack of upfront capital investment offered by cloud computing is appealing to many businesses. There is a lot of discussion on the benefits and costs of the cloud model and on how to move legacy applications onto the cloud platform. Here we study a different problem: how can a cloud service provider best multiplex its virtual resources onto the physical hardware? This is important because much of the touted gains in the cloud model come from such multiplexing. Studies have found that servers in many existing data centers are often severely under-utilized due to over-provisioning for the peak demand. The cloud model is expected to make such practice unnecessary by offering automatic scale up and down in response to load variation. Besides reducing the hardware cost, it also saves on electricity which contributes to a significant portion of the operational expenses in large data centers. Proper resource allocation for various virtualized resources must be based on these cloud load predictions. The presence of heterogeneous applications, such as content delivery networks, web applications, and MapReduce tasks, complicates this process. Cloud workloads with conflicting resource allocation needs for communication and information processing further exacerbate the difficulty

ADAPTIVE FRAMWORK FOR DATA DISTRIBUTION IN CLOUD-ELASTIC SERVER ARCHITECTURE

ABSTRACT The increasing quantity of information to be processed and store in a data center and cloud also, th