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

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.

Cloud-Based Platforms and Infrastructures

2017 ◽  
pp. 47-90
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

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.

QoS Based Efficient Resource Allocation and Scheduling in Cloud Computing

2019 ◽  
Vol 15 (4) ◽  
pp. 13-29
Author(s):  
Harvinder Chahal ◽  
Anshu Bhasin ◽  
Parag Ravikant Kaveri
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Provisioning ◽  
Cloud Environment ◽  
Allocation Decision ◽  
Research Article ◽  
Large Pool ◽  
Efficient Resource ◽  
Algorithm Implementation

The Cloud environment is a large pool of virtually available resources that perform thousands of computational operations in real time for resource provisioning. Allocation and scheduling are two major pillars of said provisioning with quality of service (QoS). This involves complex modules such as: identification of task requirement, availability of resource, allocation decision, and scheduling operation. In the present scenario, it is intricate to manage cloud resources, as Service provider aims to provide resources to users on productive cost and time. In proposed research article, an optimized technique for efficient resource allocation and scheduling is presented. The proposed policy used heuristic based, ant colony optimization (ACO) for well-ordered allocation. The suggested algorithm implementation done using simulation, shows better results in terms of cost, time and utilization as compared to other algorithms.

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