SLA-Based Resource Provisioning for Hosted Software-as-a-Service Applications in Cloud Computing Environments

Wu, Linlin; Garg, Saurabh; Versteeg, Steven; Buyya, Rajkumar

doi:10.1109/tsc.2013.49

Cited by 114 publications

(36 citation statements)

References 39 publications

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“…In [117] two multi-objective customer-driven SLAbased resource provisioning algorithms are proposed. The objectives are the minimization of both resource and penalty costs, as well as minimizing SLA violations.…”

Section: Infrastructure-user Capacity Allocationmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

218

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Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

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Section: Infrastructure-user Capacity Allocationmentioning

confidence: 99%

Quality-of-service in cloud computing: modeling techniques and their applications

Ardagna

Casale

Ciavotta

et al. 2014

J Internet Serv Appl

218

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“…Unfortunately, providing QoS guarantees is a challenging problem since the demand for VMs fluctuates over time. The work in [4] investigates various algorithms for resource provisioning in cloud computing systems. The main concern of the proposed algorithm is minimizing the penalty cost and improving customer satisfaction levels by minimizing QoS constraint violations.…”

Section: System Model and Related Workmentioning

confidence: 99%

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan

Itradat

Al‐Dubai

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

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“…VM instances are the basic units of resource allocation. Therefore, many researchers modeled infrastructure resources as VMs [29,31]. Some researchers defined an infrastructure resources as a set {R1,……Rm}, in which Rk (k=1..m) is a given level capacity (e.g.…”

Section: Related Workmentioning

confidence: 99%

Resource Demand Forecasting Model Based on Dynamic Cloud Workload

C¹,

Zhou²

2017

Preprint

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Abstract:The primary attraction of IaaS is providing elastic resources on demand. It becomes imperative that IaaS-users have an effective methodology for learning what resources they require, how many resources and for how long they need. However, the heterogeneity of resources, the diversity resource demands of different cloud applications and the variation of application-user behaviors pose IaaS-users big challenge. In this paper, we purpose a unified resource demand forecasting model suiting for different applications, various resources and diverse time-varying workload patterns. With the model, taking input from parameterized applications, resources and workload scenarios, the corresponding resources demands during any time interval can be deduced as output. The experiments configure concrete functions and parameters to help understanding the above model.

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SLA-Based Resource Provisioning for Hosted Software-as-a-Service Applications in Cloud Computing Environments

Cited by 114 publications

References 39 publications

Quality-of-service in cloud computing: modeling techniques and their applications

Quality-of-service in cloud computing: modeling techniques and their applications

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Resource Demand Forecasting Model Based on Dynamic Cloud Workload

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