Quality of Service (QoS)-driven resource provisioning for large-scale graph processing in cloud computing environments: Graph Processing-as-a-Service (GPaaS)

The aim of this paper is to propose and evaluate GreenMACC (Green Metascheduler Architecture to Provide QoS in Cloud Computing), an extension of the MACC architecture (Metascheduler Architecture to provide QoS in Cloud Computing) which uses greenIT techniques to provide Quality of Service. The paper provides an evaluation of the performance of the policies in the four stages of scheduling focused on energy consumption and average response time. The results presented confirm the consistency of the proposal as it controls energy consumption and the quality of services requested by different users of a large-scale private cloud.

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Randomized routing of virtual machines in IaaS data centers

PeerJ Computer Science ◽

10.7717/peerj-cs.211 ◽

2019 ◽

Vol 5 ◽

pp. e211

Author(s):

Hadi Khani ◽

Hamed Khanmirza

Keyword(s):

Cloud Computing ◽

Quality Of Service ◽

Power Consumption ◽

Analytical Model ◽

Data Center ◽

Large Scale ◽

Data Centers ◽

Virtual Machines ◽

General Distribution

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.

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

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327911666210126122119 ◽

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.

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Virtualization as the Enabling Technology of Cloud Computing

Encyclopedia of Information Science and Technology, Fourth Edition ◽

10.4018/978-1-5225-2255-3.ch101 ◽

2018 ◽

pp. 1174-1184

Author(s):

Mohamed Fazil Mohamed Firdhous

Keyword(s):

Cloud Computing ◽

Quality Of Service ◽

Resource Utilization ◽

Operating Systems ◽

Service Providers ◽

Resource Provisioning ◽

Enabling Technology ◽

Server Virtualization ◽

The Way

Cloud computing is the newest paradigm in computing that turned upside down the way of resource provisioning and payment. Cloud computing improves the resource utilization through virtualization enabling both customers as well as service providers to reap the benefits. Thus virtualization is the enabling technology that made this revolution possible. Through virtualization, it is possible to host multiple independent systems on a single hardware without interfering with each other. Server virtualization techniques can be grouped based how the underlying hardware and operating systems are presented to the users. In this chapter, the author takes an in depth look at how different virtualization has been implemented along with their security and quality of service issues.

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Virtualization as the Enabling Technology of Cloud Computing

Advances in Computer and Electrical Engineering - Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics ◽

10.4018/978-1-5225-7598-6.ch021 ◽

2019 ◽

pp. 285-297

Author(s):

Mohamed Fazil Mohamed Firdhous

Keyword(s):

Cloud Computing ◽

Quality Of Service ◽

Resource Utilization ◽

Operating Systems ◽

Service Providers ◽

Resource Provisioning ◽

Enabling Technology ◽

Server Virtualization ◽

The Way

Cloud computing is the newest paradigm in computing that turned upside down the way of resource provisioning and payment. Cloud computing improves the resource utilization through virtualization enabling both customers as well as service providers to reap the benefits. Thus, virtualization is the enabling technology that made this revolution possible. Through virtualization, it is possible to host multiple independent systems on a single hardware without interfering with each other. Server virtualization techniques can be grouped based how the underlying hardware and operating systems are presented to the users. In this chapter, the author takes an in depth look at how different virtualization has been implemented along with their security and quality of service issues.

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Resource Scheduling Techniques in Cloud Computing Environment : A Survey

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset11841119 ◽

2018 ◽

pp. 64-68

Author(s):

Shailendra Raghuvanshi ◽

Priyanka Dubey

Keyword(s):

Cloud Computing ◽

Time Management ◽

Large Scale ◽

Service Level Agreement ◽

Service Level ◽

Resource Provisioning ◽

Network Access ◽

Configurable Computing ◽

Dynamic Resource Provisioning

Cloud Computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, applications and services) that can be rapidly provisioned and released. Resource Provisioning means the selection, deployment, and run-time management of software (e.g., database server management systems, load balancers) and hardware resources (e.g., CPU, storage, and network) for ensuring guaranteed performance for applications. Resource Provisioning is an important and challenging problem in the large-scale distributed systems such as Cloud computing environments. There are many resource provisioning techniques, both static and dynamic each one having its own advantages and also some challenges. These resource provisioning techniques used must meet Quality of Service (QoS) parameters like availability, throughput, response time, security, reliability etc., and thereby avoiding Service Level Agreement (SLA) violation. In this paper, survey on Static and Dynamic Resource Provisioning Techniques is made.

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Dynamic partner selection in Cloud Federation for ensuring the quality of service for cloud consumers

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962317500362 ◽

2017 ◽

Vol 08 (03) ◽

pp. 1750036 ◽

Cited By ~ 4

Author(s):

Manoj V. Thomas ◽

K. Chandrasekaran

Keyword(s):

Cloud Computing ◽

Quality Of Service ◽

Large Scale ◽

Service Providers ◽

Partner Selection ◽

Cloud Service ◽

Analytic Hierarchy ◽

Cloud Federation ◽

Global Coordination

Cloud Computing has become the popular paradigm for accessing the various scalable and on-demand computing services over the internet. Nowadays, individual Cloud Service Providers (CSPs) offering specialized services to the customers collaborate to form the Cloud Federation, in order to reap the real benefits of Cloud Computing. By collaboration, the member CSPs of the federation achieve better resource utilization and Quality of Service (QoS), thereby increasing their business prospects. When a CSP runs out of resources in the Cloud Federation, in order to offload the customer requests for resources to other CSP(s), identifying a suitable partner is a challenging task due to the lack of global coordination among them. In this paper, we propose the design and implementation of an efficient partner selection mechanism in the Cloud Federation, using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, and also considering the trust values of various CSPs in the federation. The AHP method is used to calculate the weights of the QoS parameters used in the TOPSIS method which is used to rank the various CSPs in the Cloud Federation according to the user requirements. Simulation results show the effectiveness of this approach in order to efficiently select the trustworthy partners in large scale federations to ensure the required QoS to the cloud consumers.

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