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.

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.

scholarly journals Cooperative Game-Based Virtual Machine Resource Allocation Algorithms in Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sungwook Kim
Keyword(s):  
Resource Allocation ◽  
Virtual Machine ◽  
Cooperative Game ◽  
Data Centers ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Memory Storage ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Allocation Algorithms

With the growing demand of cloud services, cloud data centers (CDCs) can provide flexible resource provisioning in order to accommodate the workload demand. In CDCs, the virtual machine (VM) resource allocation problem is an important and challenging issue to provide efficient infrastructure services. In this paper, we propose a unified resource allocation scheme for VMs in the CDC system. To provide a fair-efficient solution, we concentrate on the basic concept of Shapley value and adopt its variations to effectively allocate CDC resources. Based on the characteristics of value solutions, we develop novel CPU, memory, storage, and bandwidth resource allocation algorithms. To practically implement our algorithms, application types are assumed as cooperative game players, and different value solutions are applied to optimize the resource utilization. Therefore, our four resource allocation algorithms are jointly combined as a novel fourfold game model and take various benefits in a rational way through the cascade interactions while solving comprehensively some control issues. To ensure the growing demand of cloud services, this feature can leverage the full synergy of different value solutions. To check the effectiveness and superiority of our proposed scheme, we conduct extensive simulations. The simulation results show that our algorithms have significant performance improvement compared to the existing state-of-the-art protocols. Finally, we summarize our cooperative game-based approach and discuss possible major research issues for the future challenges about the cloud-assisted DC resource allocation paradigm.

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.

A Resource Allocation Model for Desktop Clouds

2015 ◽  
pp. 199-218 ◽  
Author(s):  
Abdulelah Alwabel ◽  
Robert John Walters ◽  
Gary B. Wills
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Cloud Services ◽  
New Paradigm ◽  
Allocation Mechanism ◽  
Desktop Grids ◽  
Allocation Model ◽  
Resource Allocation Model ◽  
Computing Services ◽  
New Type

Cloud computing is a new paradigm that promises to move IT a step further towards utility computing, in which computing services are delivered as a utility service. Traditionally, Cloud employs dedicated resources located in one or more data centres in order to provide services to clients. Desktop Cloud computing is a new type of Cloud computing that aims at providing Cloud capabilities at low or no cost. Desktop Clouds harness non dedicated and idle resources in order to provide Cloud services. However, the nature of such resources can be problematic because they are prone to failure at any time without prior notice. This research focuses on the resource allocation mechanism in Desktop Clouds.The contributions of this chapter are threefold. Firstly, it defines and explains Desktop Clouds by comparing them with both Traditional Clouds and Desktop Grids. Secondly, the paper discusses various research issues in Desktop Clouds. Thirdly, it proposes a resource allocation model that is able to handle node failures.

Cloud Computing

2018 ◽  
pp. 233-259
Author(s):  
Shailendra Singh ◽  
Sunita Gond
Keyword(s):  
Cloud Computing ◽  
Cloud Services ◽  
Educational Institutions ◽  
Personal Computing ◽  
The World ◽  
Long Time ◽  
The Cost ◽  
Shared Network ◽  
Cloud Users ◽  
Distributed And Parallel Computing

As this is the age of technology and every day we are receiving the news about growing popularity of internet and its applications. Cloud computing is an emerging paradigm of today that is rapidly accepted by the industry/organizations/educational institutions etc. for various applications and purpose. As computing is related to distributed and parallel computing which are from a very long time in the market, but today is the world of cloud computing that reduces the cost of computing by focusing on personal computing to data center computing. Cloud computing architecture and standard provide a unique way for delivering computation services to cloud users. It is having a simple API (Application Platform Interface) to users for accessing storage, platform and hardware by paying-as-per-use basis. Services provided by cloud computing is as same as other utility oriented services like electricity bill, water, telephone etc. over shared network. There are many cloud services providers in the market for providing services like Google, Microsoft, Manjrasoft Aneka, etc.

Examining IoT's Applications Using Cloud Services

2018 ◽  
pp. 147-163 ◽  
Author(s):  
Saravanan K ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Distributed Computing ◽  
Knowledge Sharing ◽  
Effective Communication ◽  
Cloud Services ◽  
On Demand ◽  
Computing Paradigm ◽  
Iot Devices ◽  
And Storage

Cloud IoT has evolved from the convergence of Cloud computing with Internet of Things (IoT). The networked devices in the IoT world grow exponentially in the distributed computing paradigm and thus require the power of the Cloud to access and share computing and storage for these devices. Cloud offers scalable on-demand services to the IoT devices for effective communication and knowledge sharing. It alleviates the computational load of IoT, which makes the devices smarter. This chapter explores the different IoT services offered by the Cloud as well as application domains that are benefited by the Cloud IoT. The challenges on offloading the IoT computation into the Cloud are also discussed.

Cloud Computing for On-Demand Virtual Desktops and Labs

2012 ◽  
pp. 111-125
Author(s):  
Christoph Reich ◽  
Sandra Hübner ◽  
Hendrik Kuijs
Keyword(s):  
Cloud Computing ◽  
Learning Experience ◽  
Cloud Services ◽  
The Internet ◽  
It Infrastructure ◽  
On Demand ◽  
E Learning ◽  
Collaboration Platforms ◽  
Computer Resources ◽  
Support Research

Cloud computing is used to provide users with computer resources on-demand any time over the Internet. At the Hochschule Furtwangen University (HFU) students, lecturers, and researchers can leverage cloud computing to enhance their e-learning experience. This chapter presents how cloud computing provides on-demand virtual desktops for problem solving, on-demand virtual labs for special courses, and on-demand collaboration platforms to support research groups. The focus is how cloud services can be used, how they can be integrated into the existing HFU-IT infrastructure, and how new didactic models could look.

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

An Analysis on the Terms and Conditions of the Clickwrap Agreement and the Benefits of Maintaining the Click Wrap Agreement in Cloud Computing

2022 ◽  
pp. 205-224
Author(s):  
Dhiviya Ram
Keyword(s):  
Cloud Computing ◽  
Service Provider ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Services ◽  
End User ◽  
Cloud Service Provider ◽  
Novel Approach ◽  
Cloud Service Providers ◽  
Provider Perspective

One of the most unique forms of contracting is apparent in cloud computing. Cloud computing, unlike other conventional methods, has adopted a different approach in the formation of binding contract that will be used for the governance of the cloud. This method is namely the clickwrap agreement. Click wrap agreement follows a take it or leave it basis in which the end users are provided with limited to no option in terms of having a say on the contract that binds them during the use of cloud services. The terms found in the contract are often cloud service provider friendly and will be less favourable to the end user. In this article, the authors examine the terms that are often found in the cloud computing agreement as well as study the benefit that is entailed in adopting this contracting method. This chapter has undertaken a qualitative study that comprises interviews of cloud service providers in Malaysia. Hence, this study is a novel approach that also provides insight in terms of the cloud service provider perspective regarding the click wrap agreement.

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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