SEGUE: Quality of Service Aware Edge Cloud Service Migration

2016 ◽  
Author(s):  
Wuyang Zhang ◽  
Yi Hu ◽  
Yanyong Zhang ◽  
Dipankar Raychaudhuri
Keyword(s):  
Quality Of Service ◽  
Cloud Service ◽  
Service Migration

scholarly journals Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

2021 ◽  
Author(s):  
Ivana Stupar ◽  
Darko Huljenić
Keyword(s):  
Quality Of Service ◽  
Cloud Service ◽  
Operational Cost ◽  
Cloud Infrastructure ◽  
Application Service ◽  
Cloud Application ◽  
Wide Range ◽  
Service Deployment ◽  
Cloud Applications

Abstract Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

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 EVALUATION OF CLOUD COMPUTING CLUSTER PERFORMANCE

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 72-79
Author(s):  
Aleksandr O. Volkov ◽  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Providers ◽  
Poisson Model ◽  
Cloud Service ◽  
Computing System ◽  
Processor Sharing ◽  
Cloud Systems ◽  
Cloud Computing System

For cloud service providers, one of the most relevant tasks is to maintain the required quality of service (QoS) at an acceptable level for customers. This condition complicates the work of providers, since now they need to not only manage their resources, but also provide the expected level of QoS for customers. All these factors require an accurate and well-adapted mechanism for analyzing the performance of the service provided. For the reasons stated above, the development of a model and algorithms for estimation the required resource is an urgent task that plays a significant role in cloud systems performance evaluation. In cloud systems, there is a serious variance in the requirements for the provided resource, as well as there is a need to quickly process incoming requests and maintain the proper level of quality of service – all of these factors cause difficulties for cloud providers. The proposed analytical model for processing requests for a cloud computing system in the Processor Sharing (PS) service mode allows us to solve emerging problems. In this work, the flow of service requests is described by the Poisson model, which is a special case of the Engset model. The proposed model and the results of its analysis can be used to evaluate the main characteristics of the performance of cloud systems.

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.

A Salp Swarm Optimization for Dynamic Resource Management to Improve Quality-of-Service in Cloud Computing and IoT Environment

2021 ◽  
Vol 11 ◽  
Author(s):  
Narander Kumar ◽  
Surendra Kumar
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Data Centers ◽  
Service Providers ◽  
Cloud Service ◽  
Research Problem ◽  
Resource Provisioning ◽  
Quality Of Services ◽  
Computing Services

Background: Cloud Computing can utilize processing and efficient resources on a metered premise. This feature is a significant research problem, like giving great Quality-of-Services (QoS) to the cloud clients. Objective: Quality of Services confirmation with minimum utilization of resource and their time/costs, cloud service providers ought to receive self-versatile of the resource provisioning at each level. Currently, various guidelines, as well as model-based methodologies, have been intended to the management of resources aspects in the cloud computing services. Method: In this Research article, manage resource allocations dependent optimization Salp Swarm Algorithm (SSA) areused to merge various numbers of VMs on lessening Data Centers to SLA as well as required Quality-of-Service (QoS) with most extreme data centers use. Result: We compared with the various approaches like the First fit (FF), greedy crow search (GCS), and hybrid crow search with the response time and resource utilization. Conclusion: The proposed mechanism is simulated on Cloudsim Simulator, the simulation results show less migration time that improves the QoS as well minimize the energy consumssion in a cloud computing and IoT environment.

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.

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