A multi-model framework to implement self-managing control systems for QoS management

Patikirikorala, Tharindu; Colman, Alan; Han, Jun; Wang, Liuping

doi:10.1145/1988008.1988040

Cited by 42 publications

(34 citation statements)

References 18 publications

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“…Patikirikorala et al [112] proposes a multi-model control-based framework to deal with the highly nonlinear nature of software systems. An extensible meta-model and a class library with an initial set of five models are developed.…”

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

View full text Add to dashboard Cite

show abstract

“…For example, the fixed approaches are criticized for unsuitable with dynamic and unpredictable workload [39], while the adaptive controllers have been blamed for unable to cope with sudden burst in workload [13] and high computational cost because of on-line estimation [39]. The multi-model approach in [39,40] is analogous to our approach, but with the following two main differences: firstly, their selection of suitable controller is only based on the prediction of control error; secondly, it is not clear how the system can be partitioned into sub models. The approaches from [41][42][43] are different in the context, where each of the approaches is applicable at the data centre level, while our approach advocates fine grained resource control over the application level.…”

Section: Related Workmentioning

confidence: 99%

Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning

Ullah

Hussain

et al. 2016

Cogn Comput

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Cloud elasticity augments applications to dynamically adapt to changes in demand by acquiring or releasing computational resources on the fly. In the past, we developed a framework for cloud elasticity utilizing multiple feedback controllers simultaneously. Each controller determines the scaling action with different intensity, whereby the selection of a suitable controller is realized with a fuzzy inference system. In this paper, we aim to identify the similarities between cloud elasticity and action selection mechanism in animal's brain. We treat each controller in our previous framework as an action and propose a novel bio-inspired, soft switching approach. This approach integrates a basal ganglia computational model as an action selection mechanism. Initial experimental results demonstrate that the basal ganglia based approach has higher potential to improve the overall system performance and stability.

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“…Most of the existing works on VM provisioning for webbased systems can be classified into two main categories: plan-based approaches and control theoretic approaches [16,29,30,33]. Plan-based approaches can be further classified into workload prediction approaches [6,17,31,39] and performance dynamics model approaches [12, 15, 20-22, 24, 38, 40].…”

Section: Vm Provisioning Approachesmentioning

confidence: 99%

“…Dutreilh [30] proposed a multi-model framework for implementing self-managing control systems for QoS management. The work is based on a control theoretic approach called the Multi-Model Switching and Tuning (MMST) adaptive control.…”

Section: Vm Provisioning Approachesmentioning

confidence: 99%

Prediction-based VM provisioning and admission control for multi-tier web applications

2016

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We present a prediction-based, cost-efficient Virtual Machine (VM) provisioning and admission control approach for multi-tier web applications. The proposed approach provides automatic deployment and scaling of multiple web applications on a given Infrastructure as a Service (IaaS) cloud. It monitors and uses collected resource utilization metrics itself and does not require a performance model of the applications or the infrastructure dynamics. The approach uses the OSGi component model to share VM resources among deployed applications, reducing the total number of required VMs. The proposed approach comprises three sub-approaches: a reactive VM provisioning approach called ARVUE, a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling (CRAMP), and a session-based adaptive admission control approach called adaptive Admission Control for Virtualized Application Servers (ACVAS). Performance under varying load conditions is guaranteed by automatic adjustment and tuning of the CRAMP and ACVAS parameters. The proposed approach is demonstrated in discrete-event simulations and is evaluated in a series of experiments involving synthetic as well as realistic load patterns.

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A multi-model framework to implement self-managing control systems for QoS management

Cited by 42 publications

References 18 publications

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

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

Towards a Biologically Inspired Soft Switching Approach for Cloud Resource Provisioning

Prediction-based VM provisioning and admission control for multi-tier web applications

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