A hybrid cloud controller for vertical memory elasticity: A control-theoretic approach

Farokhi, Soodeh; Jamshidi, Pooyan; Lakew, Ewnetu Bayuh; Brandić, Ivona; Elmroth, Erik

doi:10.1016/j.future.2016.05.028

Cited by 39 publications

(28 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…(2) Whereas, in the case of vertical elasticity, the objective depends on the nature of reconfigurable resources. For example, there are control solutions, where the control objective is the readjustment of the memory allocation (such as [53,54]) or the readjustment of the CPU allocation (such as [49,[55][56][57][58]). …”

Section: Control Objectivementioning

confidence: 99%

“…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%

“…The control solutions that are only based on capacity based are inadequate to guarantee application performance because of their no consideration of performance aspects at runtime [54].…”

Section: Adaptivementioning

confidence: 99%

“…In contrast to these approaches, the authors of [31,49,54,63,73] included performance based metrics into their decision making mechanism. Amongst these, the target performance measurements were used to readjust, the memory size in [31] and CPU entitlement in [63,73].…”

Section: Adaptivementioning

confidence: 99%

“…Amongst these, the target performance measurements were used to readjust, the memory size in [31] and CPU entitlement in [63,73]. All these three methods only rely on performance based metrics that may not be adequate to ensure the desired quality of service, because the performance can be also disturbed by an internal bug [54]. The control solution of [31] is further extended in [54], where memory utilisation is considered as part of the decision additional to response time, making their methodology hybrid, which consider both aspects.…”

Section: Adaptivementioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Control Objectivementioning

confidence: 99%

Section: Control Inputmentioning

confidence: 99%

“…The control solutions that are only based on capacity based are inadequate to guarantee application performance because of their no consideration of performance aspects at runtime [54].…”

Section: Adaptivementioning

confidence: 99%

Section: Adaptivementioning

confidence: 99%

Section: Adaptivementioning

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

View full text Add to dashboard Cite

show abstract

End‐to‐end elasticity control of cloud‐network slices

Medeiros

Neto

Sampaio

et al. 2019

Internet Technology Letters

View full text Add to dashboard Cite

The design of efficient elasticity control mechanisms for dynamic resource allocation is crucial to increase the efficiency of future cloud-network slice-defined systems. Current elasticity control mechanisms proposed for cloud-or network-slicing, only consider cloud-or network-type resources respectively. In this paper, we introduce the elaSticity in cLOud-neTwork Slices (SLOTS) which aims to extend the horizontal elasticity control to multi-providers scenarios in an end-to-end fashion, as well as to provide a novel vertical elasticity mechanism to deal with critical insufficiency of resources by harvesting underused resources on other slices. Finally, we present a preliminary assessment of the SLOTS prototype in a real testbed, revealing outcomes that suggest the viability of the proposal. K E Y W O R D S5G, cloud-network slicing, end-to-end slicing, resource elasticity

show abstract

STRATModel: Elasticity Model Description Language for Evaluating Elasticity Strategies for Business Processes

Bhiri

Tata²

2017

On the Move to Meaningful Internet Systems. OTM 2017 Conferences

View full text Add to dashboard Cite

A hybrid cloud controller for vertical memory elasticity: A control-theoretic approach

Cited by 39 publications

References 17 publications

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

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

End‐to‐end elasticity control of cloud‐network slices

STRATModel: Elasticity Model Description Language for Evaluating Elasticity Strategies for Business Processes

Contact Info

Product

Resources

About