scholarly journals An approach towards development of a migration enabled improved datacenter broker policy

2020 ◽  
Vol 4 (3) ◽  
pp. 112-124
Author(s):  
Debashis Das ◽  
Sourav Banerjee ◽  
Ayan Kundu ◽  
Swagata Chandra ◽  
Saptarshi Pal ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Waiting Time ◽  
Service Provider ◽  
Cloud Service ◽  
Turnaround Time ◽  
Experimental Results ◽  
Cloud Service Provider ◽  
Allocation Policy ◽  
Critical Issues

Cloud computinghas left its remarkable note on the computing world over the last few years. Through itseffectiveness, litheness, scalability & availability cloud computinghas changed the nature of computer systemdeployment. The Quality of Service (QoS) of a cloud service provider (CSP) is an important element of research interestwhich includes different critical issues such as proper load, minimization of waiting time, turnaround time, makespanand suppressing the wastage of bandwidth of the system. The Datacenter Broker (DCB) policy helpsassigning acloudletto a VM. In present study, we proposed an algorithm, i.e., Migration enabled Cloudlet Allocation Policy(MCAP) for allocation of cloudlets to the VMs in a Datacenter by taking into accounttheload capacity of VMs andlength of the cloudlets. The experimental results obtained using CloudSim toolkit under extensive loads that establishperformance supremacy of MCAP algorithm over the existing algorithms.

scholarly journals An Approach towards Development of a Migration enabled Improved Datacenter Broker Policy

2019 ◽  
Vol 4 (3) ◽  
pp. 112-124
Author(s):  
Sourav Banerjee ◽  
Debashis Das ◽  
Ayan Kundu ◽  
Saptarshi Pal ◽  
Utpal Biswas
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Waiting Time ◽  
Load Capacity ◽  
Cloud Service ◽  
Turnaround Time ◽  
Cloud Service Provider ◽  
Allocation Policy ◽  
Critical Issues

Cloud computing has left its remarkable note on the computing world over the last few years. Through its effectiveness, litheness, scalability & availability cloud computing has changed the nature of computer system deployment. The Quality of Service (QoS)of a cloud service provider (CSP) is an important element of research interest which includes different critical issues such as proper load, minimization of waiting time, turnaround time, makespan and suppressing the wastage of bandwidth of the system. The Datacenter Broker (DCB) policy help assigning a cloudlet to a VM. In the present study, we proposed an algorithm, i.e., Migration enabled Cloudlet Allocation Policy (MCAP) for allocation of cloudlets to the VMs in a Datacenter by taking into account the load capacity of VMs and length of the cloudlets. The experimental results obtained using CloudSim toolkit under extensive loads that establish performance supremacy of MCAP algorithm over the existing algorithms.

Issues in On-Demand Cloud-Based Gaming Storage

2017 ◽  
pp. 115-138
Author(s):  
Ebin Deni Raj ◽  
L. D. Dhinesh Babu
Keyword(s):  
Mathematical Model ◽  
Cloud Computing ◽  
Quality Of Service ◽  
Service Provider ◽  
Quality Of Experience ◽  
Cloud Service ◽  
End User ◽  
Cloud Service Provider ◽  
On Demand

Cloud computing is the most utilized and evolving technology in the past few years and has taken computing to a whole new level such that even common man is receiving the benefits. The end user in cloud computing always prefers a cloud service provider which is efficient, reliable and best quality of service at the lowest possible price. A cloud based gaming system relieves the player from the burden of possessing high end processing and graphic units. The storage of games hosted in clouds using the latest technologies in cloud has been discussed in detail. The Quality of service of games hosted in cloud is the main focus of this chapter and we have proposed a mathematical model for the same. The various factors in dealing with the quality of service on cloud based games have been analyzed in detail. The quality of experience of cloud based games and its relation with quality of service has been derived. This chapter focuses on the various storage techniques, quality of experience factors and correlates the same with QoS in cloud based games.

scholarly journals Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

2019 ◽  
Vol 8 (3) ◽  
pp. 1457-1462
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Service Provider

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.

Optimal Cloud Service Provider Selection Based on QoS Metrics

2021 ◽  
Vol 13 (2) ◽  
pp. 1-15
Author(s):  
Arunambika T. ◽  
Senthil Vadivu P.
Keyword(s):  
Service Provider ◽  
Service Providers ◽  
Selection Process ◽  
Cloud Service ◽  
Experimental Result ◽  
User Needs ◽  
Cloud Service Provider ◽  
Service Broker ◽  
Qos Metrics

There are thousands of providers obtainable in the market, and more and more are being added to the service list every day. All of these providers claim that the services given to them are distinctive and hassle-free. To check their claim, the cloud service broker (CSB) verifies the quality of service (QoS) of the cloud service providers (CSPs) and the level of user needs. Depending on the requirements of the cloud consumer (CC), CSB allocates a CSP to it. This paper proposed optimal cloud service provider selection (OCSPS) based on QoS metrics. The CC would handovers the demand to the CSB for optimal CSP selection using QoS. Once the CSP selection process is complete, the outcomes would become back to the CC. Entire demands created to CCs are saved in the request buffer (RB) in the CSB. When a specific request is fulfilled, the subsequent demand would take from this RB. The experimental result shows that the proposed OCSPS algorithm takes less time for CSP ranking and optimal CSP selection.

scholarly journals ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

2018 ◽  
Vol 6 (5) ◽  
pp. 340-345
Author(s):  
Rajat Pugaliya ◽  
Madhu B R
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Dynamic Task ◽  
Main Challenge ◽  
High Level

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

scholarly journals Energy Efficient Resource Allocation in Cloud Computing

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2071-2074
Keyword(s):  
Virtual Machine ◽  
Service Provider ◽  
Physical System ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Host System ◽  
Physical Systems ◽  
Single Host

Cloud service provider in cloud environment will provide or provision resource based on demand from the user. The cloud service provider (CSP) will provide resources as and when required or demanded by the user for execution of the job on the cloud environment. The CSP will perform this in a static and dynamic manner. The CSP should also consider various other factors in order to provide the resources to the user, the prime among that will be the Service Level Agreement (SLA), which is normally signed by the user and cloud service provider during the inception phase of service. There are many algorithm which are used in order to allocate resources to the user in cloud environment. The algorithm which is proposed will be used to reduce the amount of energy utilized in performing various job execution in cloud environment. Here the energy utilized for execution of various jobs are taken into account by increasing the number of virtual machines that are used on a single physical host system. There is no thumb rule to calculate the number of virtual machines to be executed on a single host. The same can be derived by calculating the amount of space, speed required along with the time to execute the job on a virtual machine. Based up on this we can derive the number of Virtual machine on a single host system. There can be 10 virtual machines on a single system or even 20 number of virtual machines on single physical system. But if the same is calculated by the equation then the result will be exactly matching with the threshold capacity of the physical system[1]. If more number of physical systems are used to execute fewer virtual machines on each then the amount of energy consumed will be very high. So in order to reduce the energy consumption , the algorithm can be used will not only will help to calculate the number of virtual machines on single physical system , but also will help to reduce the energy as less number of physical systems will be in need[2].

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