Stability control in virtual machine: Resource allocation for cloud computing

2019 ◽  
Author(s):  
Mohd Badrulhisham Ismail ◽  
Habibah Hashim ◽  
Yusnani Mohd Yusof
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machine ◽  
Stability Control

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Virtual Machine Placement Using Statistical Mechanism in Cloud Computing Environment

2018 ◽  
Vol 9 (3) ◽  
pp. 23-31
Author(s):  
Narander Kumar ◽  
Surendra Kumar
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machine ◽  
Minimum Cost ◽  
Virtual Machine Placement ◽  
The Internet ◽  
Computing Environment ◽  
Assignment Problems ◽  
Cloud Computing Environment ◽  
Statistical Mechanism

The internet has become essential and is the basis of cloud computing and will continue to be in the future. Best resource allocation is a process of placing the resources at their minimum cost/time and minimizes the load to a virtual machine. In this article, the authors propose an algorithm to optimize assignment problems and get the best placements in the resources to maintain the load on the virtual machine. Further, they also make comparisons between various optimization mechanisms for assignment problems, which is formulated for the cloud in virtual machine placement.

scholarly journals Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 8296-8302
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Virtual Machine ◽  
Economies Of Scale ◽  
Arrival Rate ◽  
Workload Management ◽  
Q Learning ◽  
Machine Utilization ◽  
Cloud Users

Cloud computing is a delivery model of IT resources such as computing servers, storage, databases, networking and software over the Internet. It offers the resources as services based on demand with more faster, flexible and economies of scale. The major challenges in the cloud computing are resource allocation and workload management due to the scalability of the cloud users and the services deployed in it. Even though there are various approaches available to manage workload and resource allocation, unfortunately most of them fail to mange it properly. This paper proposes a Reinforcement Learning based Enhanced Resource Allocation and Workload Management (RL-ERAWM) approach to increase the performance of cloud with large number of tasks and users. It implements the Q-Learning approach which effectively considers arrival rate of the requests and workload of the virtual machine. Experimental results prove that the proposed method alleviates the performance of task scheduling and workload management process compared with other approaches in terms of response time, makespan and virtual machine utilization.

A Novel Approach for Dynamic Apportion and De-Allocate Resources from the Cloud

2018 ◽  
Vol 6 (9) ◽  
pp. 1
Author(s):  
K Valli Madhavi ◽  
CH Kalyani ◽  
S Durga Prasad
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machine ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Efficient Manner ◽  
On Demand ◽  
Novel Approach ◽  
Flexible Resource ◽  
Cloud Users

Cloud computing is on demand as it offers dynamic flexible resource allocation for reliable and guaranteed services in pay as-you-use manner to public. In Cloud computing multiple cloud users can request number of cloud services simultaneously. So there must be a provision that all resources are made available to requesting user in efficient manner to satisfy their need. Hence there is no need for getting licenses for individual products. 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. Effective and dynamic utilization of the resources in cloud can help to balance the load and avoid situations like slow run of systems.

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