Virtual Machine Resource Allocation in Cloud Computing via Multi-Agent Fuzzy Control

2013 ◽  
Author(s):  
Dorian Minarolli ◽  
Bernd Freisleben
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Fuzzy Control ◽  
Virtual Machine ◽  
Multi Agent

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.

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

The Study on Cloud Computing Resource Allocation Method

2012 ◽  
Vol 198-199 ◽  
pp. 1506-1513 ◽  
Author(s):  
Ling Yan Wang ◽  
Ai Min Liu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
Optimization Methods ◽  
Resource Provisioning ◽  
Scheduling Problems ◽  
Multi Agent Systems ◽  
Optimization Simulation ◽  
Multi Agent ◽  
Fair Scheduler

Resource allocation and scheduling problems in the field of cloud computing can be classified into two major groups. The first one is in the area of MapReduce task scheduling. The default scheduler is the FIFO one. Two other schedulers that are available as plug-in for Hadoop: Fair scheduler and Capacity scheduler. We presented recent research in this area to enhance performance or to better suit a specific application. MapReduce scheduling research involves introducing alternative schedulers, or proposing enhancements for existing schedulers such as streaming and input format specification. The second problem is the provisioning of virtual machines and processes to the physical machines and its different resources. We presented the major cloud hypervisors available today. We described the different methods used to solve the resource allocation problem including optimization, simulation, distributed multi-agent systems and SoA. Finally, we presented the related topic of connecting clouds which uses similar resource provisioning methods. The above two scheduling problems are often mixed up, yet they are related. For example, MapReduce benchmarks can be used to evaluate VM provisioning methods. Enhancing the solution to one problem can affect the other. Similar methods can be used in solving both problems, such as optimization methods. Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing.

scholarly journals Distributed resource allocation in cloud computing using multi-agent systems

2017 ◽  
Vol 9 (2) ◽  
pp. 110-115 ◽  
Author(s):  
Artan Mazrekaj ◽  
Dorian Minarolli ◽  
Bernd Freisleben
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Multi Agent Systems ◽  
Distributed Resource Allocation ◽  
Agent Systems ◽  
Multi Agent

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.

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