An Integrated Approach to Automatic Management of Virtualized Resources in Cloud Environments

Li, Qiang; Hao, Qinfen; Xiao, Limin; Li, Zhou-jun

doi:10.1093/comjnl/bxq082

Cited by 20 publications

(19 citation statements)

References 24 publications

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“…In cloud elasticity domain, the majority of existing solutions belong to the category of either regulatory control (e.g., [28][29][30][31][32][33]) or optimisation (e.g., [34][35][36][37][38][39]). The control solutions having the objective of disturbance rejection often assist another control solution (e.g., [40][41][42][43]).…”

Section: Control Objectivementioning

confidence: 99%

“…Furthermore, they do not consider system resource utilisation level. [30,[68][69][70][71]. Apart from Response time and Throughput, the use of some other performance metrics is also observed.…”

Section: Reference Inputmentioning

confidence: 99%

“…However, in terms of vertical elasticity, the existing literature lacks on providing the justification of the choice of Control input. It is evident from Tables 1, 2 [32, 34, 55-57, 59, 63-66, 68, 70, 73, 75, 76] and as combined with Memory allocation in [30,42,45,71], combined with bandwidth in [62,77] and combined with both memory and bandwidth in [30,69]. In last, the control solutions proposed in [31,53,54] …”

Section: Control Inputmentioning

confidence: 99%

“…Li et al [30] proposed an integrated three-layer automatic management approach. The three layers include resource management at VM, node and cloud level.…”

Section: State Space Feedback (Ssf)mentioning

confidence: 99%

“…However, in [30], the gain is adaptive and may change at runtime, whereas the gain remains fixed in [28]. Moreover, Moulavi et al [28] used an additional fuzzy controller, which is responsible for allowing or discarding the control decision considering the standard deviations of CPU loads.…”

Section: State Space Feedback (Ssf)mentioning

confidence: 99%

See 4 more Smart Citations

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

Ullah

Shen

et al. 2018

Cluster Comput

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The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the highlevel view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.

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Section: Control Objectivementioning

confidence: 99%

Section: Reference Inputmentioning

confidence: 99%

Section: Control Inputmentioning

confidence: 99%

“…Li et al [30] proposed an integrated three-layer automatic management approach. The three layers include resource management at VM, node and cloud level.…”

Section: State Space Feedback (Ssf)mentioning

confidence: 99%

Section: State Space Feedback (Ssf)mentioning

confidence: 99%

See 3 more Smart Citations

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

Ullah

Shen

et al. 2018

Cluster Comput

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A cloud computing system in windows azure platform for data analysis of crystalline materials

Xing

Blaisten‐Barojas

2012

Concurrency and Computation

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SUMMARYCloud computing is attracting the attention of the scientific community. In this paper, we develop a new cloud‐based computing system in the Windows Azure platform that allows users to use the Zeolite Structure Predictor (ZSP) model through a Web browser. The ZSP is a novel machine learning approach for classifying zeolite crystals according to their framework type. The ZSP can categorize entries from the Inorganic Crystal Structure Database into 41 framework types. The novel automated system permits a user to calculate the vector of descriptors used by ZSP and to apply the model using the Random Forest™ algorithm for classifying the input zeolite entries. The workflow presented here integrates executables in Fortran and Python for number crunching with packages such as Weka for data analytics and Jmol for Web‐based atomistic visualization in an interactive compute system accessed through the Web. The compute system is robust and easy to use. Communities of scientists, engineers, and students knowledgeable in Windows‐based computing should find this new workflow attractive and easy to be implemented in scientific scenarios in which the developer needs to combine heterogeneous components. Copyright © 2012 John Wiley & Sons, Ltd.

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A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services

Qing

Zheng

Wei

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Resource elastic scheduling is a key feature of cloud services. The elastic makes cloud services have the ability to flexibly increase or decrease resources to satisfy user needs, and dynamically allocate resources for cloud services on demand. The amount of resources to be configured is determined at runtime based on the changes in workload to flexibly respond to the fluctuating demands of cloud services. Appropriate resources need to be configured in advance. In this article, we propose a dynamic resource provisioning framework based on the MAPE loop, and use a two-tier elastic resource configuration for collaborative work. In order to implement the proposed framework, we propose an elastic resource scheduling algorithm based on a combination of the autonomic computing and deep reinforcement learning (DRL) to reduce task rejection rate of the virtual machine (VM) and increase utilization to obtain as much profit as possible. In this paper, Experimental results using actual Google cluster tracking results show that the proposed policy reduces the total cost about 17%-58% and increases the profit by up to not less than 9%, reduces the service level agreement (SLA) violations to less than 0.4% to better guarantee the quality of service (QoS).

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An Integrated Approach to Automatic Management of Virtualized Resources in Cloud Environments

Cited by 20 publications

References 24 publications

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

A cloud computing system in windows azure platform for data analysis of crystalline materials

A Deep Reinforcement Learning Based Resource Autonomic Provisioning Approach for Cloud Services

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