A dynamic programming approach to manage virtual machines allocation in cloud computing (original) (raw)
Related papers
2021
Due to the complex and dynamic nature of virtual machine allocation in the cloud, it is difficult to manage and control the resources or to choose the best allocation of these resources. In this paper, we propose an optimal model for allocating computing resources to assess the best management of system resources where a group of physical machines is defined as ”reserves”. The controller activates them one by one when the system has a high number of tasks. The objective is to maximize the reward of the cloud computing system. This reward is calculated based on the energy and execution time of each customer and the characteristics of the system. Finding the best allocation for such a complex system is a challenge. For this, we used a heuristic algorithm and dynamic programming approach. The results analysis showed the advantage of using our model to control the use of reserve machines to get high quality of service and low energy consumption.
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 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 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.
An Heuristics based Dynamic Power-Aware Resource Allocation for Cloud Computing
IJCSMC, 2018
Cloud computing is public pools of configurable mainframe system resources and higher-level services that can be rapidly provisioned with minimal running effort, often over the Internet. One of the main challenges in cloud computing is how to reduce the massive amount of energy consumption in cloud computing data centers. The many research authors proposed power aware resource allocation algorithm to solve this issue based on virtual machine allocation and consolidation approaches. The most of existing energy efficient cloud solutions save energy cost at a price of the significant performance degradation. In this paper propose a genetic heuristic search optimization technique based dynamic consolidation of VMs based on adaptive utilization thresholds, which ensures a high level of meeting the service level agreements (SLA).The dynamic virtual machine allocation policy heuristics based on the idea of setting upper and lower utilization thresholds for hosts and keeping total utilization of CPU by all VMs between these dynamic changing thresholds. The power-aware scheduling-based resource allocation (G-PARS) has been proposed to solve the dynamic virtual machine allocation policy problem. The experiments result shows that the proposed strategy has a better performance than particle swarm optimization strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much clearer, especially when it is under life-threatening workloads.
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.
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.
A Review of Dynamic Resource Management in Cloud Computing Environments
Computer Systems Science and Engineering, 2021
In a cloud environment, Virtual Machines (VMs) consolidation and resource provisioning are used to address the issues of workload fluctuations. VM consolidation aims to move the VMs from one host to another in order to reduce the number of active hosts and save power. Whereas resource provisioning attempts to provide additional resource capacity to the VMs as needed in order to meet Quality of Service (QoS) requirements. However, these techniques have a set of limitations in terms of the additional costs related to migration and scaling time, and energy overhead that need further consideration. Therefore, this paper presents a comprehensive literature review on the subject of dynamic resource management (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used to enhance the development of predictive resource management techniques, by considering the awareness of performance variation, energy consumption and cost to efficiently manage the cloud resources.