Failure Prediction Resource Allocation for Dynamic Load in Virtual Machines using Hybrid Cloud

2017 ◽  
Vol 7 (2) ◽  
pp. 1047
Author(s):  
P. Sudhakar ◽  
R Vinoth
Keyword(s):  
Resource Allocation ◽  
Dynamic Load ◽  
Virtual Machines ◽  
Failure Prediction ◽  
Hybrid Cloud

scholarly journals Resource Allocation on a Hybrid Cloud for Smart Grids

2016 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Alan Briones ◽  
Ramon Martín de Pozuelo ◽  
Joan Navarro ◽  
Agustín Zaballos
Keyword(s):  
Resource Allocation ◽  
Smart Grid ◽  
Smart Grids ◽  
Virtual Machines ◽  
Operating Costs ◽  
Hybrid Cloud ◽  
Hybrid Clouds ◽  
To Come ◽  
Resource Allocation Scheme ◽  
Multi Cloud

The use of hybrid clouds enables companies to cover their demands of IT resources saving costs and gaining flexibility in the deployment of infrastructures by paying under demand these resources. However, considering a scenario with various services to be allocated in more than one cloud, it is necessary to find the distribution of services that minimizes the overall operating costs. This paper researches on the resource allocation methodology to be applied in a multi-cloud scenario based on the findings derived from the framework used for the FINESCE project. The purpose of this work is to define a methodology to assist on the hybrid cloud selection and configuration in the Smart Grid for both generic and highly-constrained scenarios in terms of latency and availability. Specifically, the presented method is aimed to determine which is the best cloud to allocate a resource by (1) optimizing the system with the information of the network and (2) minimizing the occurrence of collapsed or underused virtual machines. Also, to assess the performance of this method and any alternative proposals, a general set of metrics has been defined. These metrics have been refined taking into account the expertise of FINESCE partners in order to shape Smart Grid clouds and reduce the complexity of computation. Finally, using the data extracted from the FINESCE testbed, a decision tree is used to come up with the best resource allocation scheme.

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.

scholarly journals Multi-agent grid system Agent-GRID with dynamic load balancing of cluster nodes

2017 ◽  
Vol 7 (1) ◽  
pp. 485-490 ◽  
Author(s):  
M.N. Satymbekov ◽  
I.T. Pak ◽  
L. Naizabayeva ◽  
Ch.A. Nurzhanov
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Virtual Machines ◽  
Dynamic Load Balancing ◽  
Grid System ◽  
Multi Agent

AbstractIn this study the work presents the system designed for automated load balancing of the contributor by analysing the load of compute nodes and the subsequent migration of virtual machines from loaded nodes to less loaded ones. This system increases the performance of cluster nodes and helps in the timely processing of data. A grid system balances the work of cluster nodes the relevance of the system is the award of multi-agent balancing for the solution of such problems.

Cloud Adaptive Resource Allocation Mechanism for Efficient Parallel Processing

2014 ◽  
Vol 4 (4) ◽  
pp. 1-6 ◽  
Author(s):  
Manisha Malhotra ◽  
Rahul Malhotra
Keyword(s):  
Resource Allocation ◽  
Parallel Processing ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Cloud Provider ◽  
Adaptive Resource Allocation ◽  
Cloud Environment ◽  
Allocation Mechanism ◽  
Adaptive Resource ◽  
Resource Allocation Mechanism

As cloud based services becomes more assorted, resource provisioning becomes more challenges. This is an important issue that how resource may be allocated. The cloud environment offered distinct types of virtual machines and cloud provider distribute those services. This is necessary to adjust the allocation of services with the demand of user. This paper presents an adaptive resource allocation mechanism for efficient parallel processing based on cloud. Using this mechanism the provider's job becomes easier and having the least chance for the wastage of resources and time.

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 OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing

2015 ◽  
Vol 15 (4) ◽  
pp. 138-148 ◽  
Author(s):  
B. Mallikarjuna ◽  
P. Venkata Krishna
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Distributed Systems ◽  
Load Balancing ◽  
Virtual Machines ◽  
Simulation Analysis ◽  
Hash Table ◽  
Distributed Hash Table ◽  
Efficient Resource

Abstract Load balancing is treated as one of the important mechanisms for efficient resource allocation in cloud computing. In future there will appear a necessity of fully autonomic distributed systems to address the load balancing issues. With reference to this, we proposed a load balancing mechanism called Osmosis Load Balancing (OLB). OLB works on the principle of osmosis to reschedule the tasks in virtual machines. The solution is based on the Distributed Hash Table (DHT) with a chord overlay mechanism. The Chord overlay is used for managing bio inspired agents and status of the cloud. By simulation analysis, the proposed algorithm has shown better performance in different scenarios, both in heterogeneous and homogeneous clouds.

Sign in / Sign up

Export Citation Format

Share Document