Modeling Decision Making to Control the Allocation of Virtual Machines in a Cloud Computing System with Reserve Machines (original) (raw)

A dynamic programming approach to manage virtual machines allocation in cloud computing

International Journal of Engineering & Technology

As a result of the dynamic nature of Virtual Machine allocation in cloud computing, it is not easy to manage system resources or choose the best configuration based solely on human experience. In this work, we used stochastic modelling instead of comprehensive experiments to evaluate the best resource management of the system. In such complex systems, choosing the best decision is a challenge, for this reason we have designed a heuristic algorithm, specifically, dynamic programming as a resource management and programming tool that finds a way that attempts to satisfy the conflicting objectives of high performance and low power consumption. As a scenario for using this algorithm, we addressed the problem of virtual machine allocation, a subset of physical machines is designated as "reserve", and the reserves are actives when the number of jobs in the system is sufficiently high. The question is how to decide when to activate the reserves. The simulation results demonstrat...

An Optimal Strategy for Resource Utilization in Cloud Data Centers

IEEE Access, 2019

Cloud computing has emerged in recent years as one of the most interesting developments in technology. With the gaining popularity of cloud-based solutions, more and more applications are migrating into the Cloud and thus have highly demanding critical requirements for networking resources. Virtual technology associated with a Data Center consists of a set of servers, storage and network devices, power systems, cooling systems, etc., and makes it possible for the resource management of physical machines to be more finely tuned and thus support multiple virtual machines well. The growing challenge, however, is how to efficiently provision these resources to meet the requirements of the different qualities of service levels. This paper offers and investigates a general situation wherein a datacenter can determine the cost of using resources and a Cloud service user can decide whether it will pay the price for the resource or not for an incoming task. By establishing a Continuous-Time Markov Decision Process model for both an average reward model and a discounted expected reward model, the optimal policy of each model for admitting tasks can be verified to be a State-related control limit (threshold) policy, respectively. Further, a detailed statement and verification of the upper boundaries for such an optimal policy, and a comprehensive set of experiments on the various cases to validate this proposed solution are provided. Particularly, the machine learning method is implemented to obtain the optimal threshold values by using a feed-forward neural network model. Several numerical examples are also provided on how to derive optimal threshold values. The results offered in this paper can be easily utilized to help datacenter operate in an economically optimal way when providing different needed application services to Cloud service users. INDEX TERMS Cloud computing, continuous-time Markov decision process, datacenter, optimal control policy, resource allocation, virtual machine.

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.

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

Implementing Virtual Machines for Dynamic Resource Allocation in Cloud Computing Environment

International Conference on Information Engineering, Management and Security 2014, 2014

To scale up and down the resource usage of stake holders such as customers, the cloud computing environment is used. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands, the green computing is supported 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.

Resource Scheduling Policy for Dynamic Virtual Machine Allocation in Cloud

Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and guaranteed computing dynamic environment for end-users. The consumers of the cloud and the data center are widely distributed. Each data center vary in their resource availability they provide. In cloud computing environment, the request from the customer takes a long time for the cloud system to identify the appropriate data center. As the workload of any cloud system depends on the task that is running on the system. This paper defines the tasks based on the application workloads and virtual machine monitor(VMM) manages the allocation of underlined resources. A novel approach to resource scheduling policy are considered, to configure the resources and dynamically allocate to the virtual machines. Several performance metrics are considered to fine grain the allocation mechanism. Finally, we compare our scheduling policy with Round Robin algorithm thereby, improving the performance and reliability of the system.

"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.

Dynamic Resource Allocation Scheme in Cloud Computing

Cloud Computing environment provisions the supply of computing resources on the basis of demand, as and when needed. It builds upon advances of virtualisation and distributed computing to support cost efficient usage of computing resources, emphasizing on resource scalability and on-demand services. It allows business outcomes to scale up and down their resources based on needs. Managing the customer demand creates the challenges of on-demand resource allocation. Virtual Machine (VM) technology has been employed for resource provisioning. It is expected that using virtualized environment will reduce the average job response time as well as executes the task according to the availability of resources. Hence VMs are allocated to the user based on characteristics of the job. Effective and dynamic utilization of the resources in cloud can help to balance the load and avoid situations like slow run of systems. This paper mainly focuses on allocation of VM to the user, based on analyzing the characteristics of the job. Main principle of this work is that low priority jobs (deadline of the job is high) should not delay the execution of high priority jobs (deadline of the job is low) and to dynamically allocate VM resources for a user job within deadline

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.