Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan, Ayoub; Itradat, Awni; Al‐Dubai, Ahmed; Zomaya, Albert Y.; Min, Geyong

doi:10.1109/tpds.2017.2748578

Cited by 60 publications

(30 citation statements)

References 30 publications

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“…In this case effective resource procurement and task mapping helps. A.Alsarhan, A.Itradat and A.Y.Zomaya [3] formulated resource provisioning with guarantee of Quality of Service (QoS) as specified in Service Level Agreement. Changes in service, cost and service demand are adapted and Virtual machine hiring is done using reinforcement learning.…”

Section: Literature Surveymentioning

confidence: 99%

Resource Provisioning for Scientific Workflow Applications using Aws Cloud

Jaybhaye,

Attar

2020

IJEAT

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Cloud computing play a very important role in day to day life of everyone. In recent years cloud services are much popular for hosting the applications. Virtual Machine Instances are the Images of physical machines which are described with its specification and configurations such as number of microprocessor (CPU) cycles, Memory access and network bandwidth. Cloud provider must contribute special interest while designing and implementing the Iaas. The role of quality and service performance is crucial aspects in application execution Scientific workflow based applications are both compute and data intensive. These application can take advantage of cloud features. Resource provisioning approaches varies based on the user’s requirements and the metric which is used to allocated resources

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Section: Literature Surveymentioning

confidence: 99%

Resource Provisioning for Scientific Workflow Applications using Aws Cloud

Jaybhaye,

Attar

2020

IJEAT

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“…In the cloud computing, several resources such as; storage, memory, CPUs etc, where key resources are provisioned and then, they are leased to the users/clients for a guaranteed QoS. In [18], authors have presented a novel-SLA (Service Level Agreement) framework for cloud computing. The framework contains the reinforcement learning (RL) to develop the hiring policy of VM, which can be considered to assure the QoS.…”

Section: Related Workmentioning

confidence: 99%

“…The outcome of equations (18) and (19) is applied to get the ( ) end-to-end function that is dependent upon the control variable ( ( ); ( )), which can be modified by altering the control variable ( )to the other 'control variable' ( ) in eq. (20).…”

Section: Proposed Rtaes Technique For Optimization Problemmentioning

confidence: 99%

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

B¹,

Krishnappa²,

Rajan³

et al. 2019

IJDPS

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Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive Energy-Scheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud Computing-Virtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.

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“…Ayoub Alsarhan et al [28] propose a novel Service Level Agreement (SLA) structure for distributed computing, in which a value control parameter is utilized to meet QoS requests for all classes in the market. The structure utilizes fortification learning (RL) to determine a VM procuring arrangement that can adjust to changes in the framework to ensure the QoS for all customer classes.…”

Section: G Adaptive Based Resource Provisioningmentioning

confidence: 99%

Investigating Methods of Resource Provisioning Mechanisms in Cloud: A Review

Malik¹,

Anwar²,

Ilyas³

et al. 2018

ijacsa

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Abstract-Delivering information through cloud computing become a modern computation. For this purpose, electronic device is required to access with an active web server. For delivering different resources, the cloud supplier provides computing power for the cloud users to organize their multiple type of application at any time on different platforms. In cloud computing, the main drawback is relevant to the best use of resources as well as resource provisioning. In cloud computing there is a lack of desired resources that is why the cloud resource provision becomes a daring work. To maintain the quality of services, the provisioning of reasonable resources is need of workloads. The main problem is to find the appropriate workload that depends on the cloud user that is related to resource pair application requirements. This paper reveals the cloud resource provisioning and identification in general and in specific, respectively. In this paper, a methodical analysis of resource provisioning in cloud computing is presented, in which resource provisioning, different types of resource provisioning mechanisms and their comparisons, and benefits are described.

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Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Cited by 60 publications

References 30 publications

Resource Provisioning for Scientific Workflow Applications using Aws Cloud

Resource Provisioning for Scientific Workflow Applications using Aws Cloud

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

Investigating Methods of Resource Provisioning Mechanisms in Cloud: A Review

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