Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints

Farokhi, Soodeh; Lakew, Ewnetu Bayuh; Klein, Cristian; Brandić, Ivona; Elmroth, Erik

doi:10.1109/iccac.2015.20

Cited by 32 publications

(38 citation statements)

References 24 publications

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“…The integration includes controllers either of same kind (e.g., [42,46]) or different kinds (e.g., [57,69]). The proposals reviewed in this category hint on the following three different types of integration.…”

Section: Discussion Issues and Challengesmentioning

confidence: 99%

“…The proposals reviewed in this category hint on the following three different types of integration. Firstly, the multiple controllers act in parallel, e.g., [42,46]. Secondly, the different controllers used are executing at different levels, e.g., [57,69].…”

Section: Discussion Issues and Challengesmentioning

confidence: 99%

“…Farokhi et al [46] also used two different controllers each for CPU and memory allocation of VMs. Their approach, however in contrast to [42,94] consists of a fuzzy based coordination mechanism.…”

Section: Hybridmentioning

confidence: 99%

“…These objectives are either in the form of the regulation of different performance metrics (e.g., the control solutions proposed in [31,41,[44][45][46][47] aiming to maintain the response time of system less than certain threshold) or in the form of optimisation of the system (e.g., obtaining the best value for the number of VMs to avoid under-utilised and over-utilised behaviour [48]). In such control solutions, the operating cost factor is indirectly considered, but the primary objective is to maintain the desired level of performance.…”

Section: Control Objectivementioning

confidence: 99%

“…The control solutions proposed in [42,46,57,69] utilised multiple controllers in various capacity to directly maintain performance based metrics that identify the target quality of service. Amongst these, the control solution proposed in [42] consists of the use of three feedback controllers to dynamically allocate CPU, memory and application performance tuning respectively.…”

Section: Hybridmentioning

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: Discussion Issues and Challengesmentioning

confidence: 99%

Section: Discussion Issues and Challengesmentioning

confidence: 99%

Section: Hybridmentioning

confidence: 99%

Section: Control Objectivementioning

confidence: 99%

Section: Hybridmentioning

confidence: 99%

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

Ullah

Shen

et al. 2018

Cluster Comput

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Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment

Vijayaraj

Uganya

Balasaraswathi

et al. 2021

Sensor Data Analysis and Management

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FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

Anglano

Canonico

Guazzone

2016

Concurrency and Computation

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Summary Cloud providers (CPs) rely on server consolidation (the allocation of several virtual machines [VMs] on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become 1 of the major goals of cloud providers. This is a challenging task because it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multitier cloud applications that must meet service‐level agreements (SLAs) in face of nonstationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations. We tackle this problem by devising FCMS, a feedback fuzzy controller that is able to dynamically adjust the CPU and memory capacity allocated to each VM in a coordinated way, to precisely match the needs induced by the incoming workload. By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state‐of‐the‐art alternative solution in all the considered experimental scenarios.

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Coordinating CPU and Memory Elasticity Controllers to Meet Service Response Time Constraints

Cited by 32 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

Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment

FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

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