Autonomic Performance and Power Control for Co-Located Web Applications in Virtualized Datacenters

Lama, Palden; Guo, Yanfei; Jiang, Changjun; Zhou, Xiaobo

doi:10.1109/tpds.2015.2453971

Cited by 16 publications

(21 citation statements)

References 38 publications

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“…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%

“…In last, the following control solutions [34,39,45,59] have considered the energy efficiency perspective of cloud elasticity, where the control objective is to reduce the power consumption behaviour of the system in association with the performance goal. Regression based controller [122] Reactive controller [123] None provided…”

Section: Control Objectivementioning

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%

“…In contrast to MPC approaches mentioned above, Lama et al [45] proposed a distributed MIMO control solution to address vertical elasticity. They used multiple MPC to manage the allocation of resources (CPU and memory) to achieve the target performance requirements of the co-located multi-tier web applications deployed on shared computational nodes of a data center.…”

Section: Model Predictive Controller (Mpc)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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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: Control Objectivementioning

confidence: 99%

Section: Control Inputmentioning

confidence: 99%

Section: Model Predictive Controller (Mpc)mentioning

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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“…[39,43] [44] [45][46][47] The first approaches to determine the required level of parallelism and task mapping using GLMs were empirically derived using Amdahl's Law [48]. However, this fails to capture inter-thread communication, data dependence synchronizations and hardware contentions that lead to sub-optimal scaling.…”

Section: Task Mapping and Parallelismmentioning

confidence: 99%

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

Singh¹,

Leech²,

Basireddy³

et al. 2017

Journal of Low Power Electronics

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Abstract-Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the ever-increasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges.

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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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Autonomic Performance and Power Control for Co-Located Web Applications in Virtualized Datacenters

Cited by 16 publications

References 38 publications

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

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

Learning-Based Run-Time Power and Energy Management of Multi/Many-Core Systems: Current and Future Trends

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

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