Elasticity in cloud computing: a survey

Coutinho, Emanuel Ferreira; Sousa, Flávio R. C.; Rego, Paulo A. L.; Gomes, Danielo G.; Souza, José Neuman de

doi:10.1007/s12243-014-0450-7

Cited by 145 publications

(79 citation statements)

References 75 publications

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“…They, however, used adaptation techniques as one of the dimensions to review the literature. At last, elasticity functions such as reactive migration, resizing and proactive replication are used as a means of classification in [23].…”

Section: Cloud Elasticitymentioning

confidence: 99%

“…Majority of the existing such review papers utilise the various cloud elasticity features to classify the overall cloud elasticity literature. In such a classification, the underlying implementation technique is consider as one attribute, (e.g., in [7,9]) and therefore no classification of the existing literature is performed using the attributes of a particular implementation technique. This result in the lack of conducting an exhaustive review of the proposals of each implementation technique.…”

Section: Taxonomymentioning

confidence: 99%

“…However, these attributes do not tell anything about the elasticity characteristics of the proposed system. Therefore, we also included the basic characteristics of the elasticity domain, which are commonly used in the related survey papers such as [9]. Thus to summarise, this section introduce the taxonomy (shown in Fig.…”

Section: Taxonomymentioning

confidence: 99%

“…This taxonomy consists of characteristics from control theoretical point of view (i.e., as an implementation technique) as well as from cloud elasticity perspective (i.e., as an application domain). In terms of the structure, our taxonomy complements the taxonomies proposed in [9,26]. The following subsections explain all the characteristics of the taxonomy in detail.…”

Section: Taxonomymentioning

confidence: 99%

“…There are also several survey papers available that provide a concise review of different aspects of cloud elasticity. Lorido-Botran et al [7] classified the overall elasticity proposals based on the underlying implementation techniques, whereas Naskos et al [8] distributed the literature based on the decision-making mechanism, and Coutinho et al [9] on the other hand provided a systematic review of utilised performance metrics, measurements tools and evaluation. The scopes of all these papers have been broad where they mainly focused on the high-level view of overall elasticity research rather than the specific details on one implementation technique.…”

Section: Introductionmentioning

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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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: Cloud Elasticitymentioning

confidence: 99%

Section: Taxonomymentioning

confidence: 99%

Section: Taxonomymentioning

confidence: 99%

Section: Taxonomymentioning

confidence: 99%

Section: Introductionmentioning

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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Joint‐analysis of performance and energy consumption when enabling cloud elasticity for synchronous HPC applications

Righi

Costa

Rodrigues

et al. 2015

Concurrency and Computation

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Summary A key characteristic of cloud computing is elasticity, automatically adjusting system resources to an application's workload. Both reactive and horizontal approaches represent traditional means to offer this capability, in which rule‐condition‐action statements and upper and lower thresholds occur to instantiate or consolidate compute nodes and virtual machines. Although elasticity can be beneficial for many HPC (high‐performance computing) scenarios, it also imposes significant challenges in the development of applications. In addition to issues related to how we can incorporate this new feature in such applications, there is a problem associated with the performance and resource pair and, consequently, with energy consumption. Further exploring this last difficulty, we must be capable of analyzing elasticity effectiveness as a function of employed thresholds with clear metrics to compare elastic and non‐elastic executions properly. In this context, this article explores elasticity metrics in two ways: (i) the use of a cost function that combines application time with different energy models; (ii) the extension of speedup and efficiency metrics, commonly used to evaluate parallel systems, to cover cloud elasticity. To accomplish (i) and (ii), we developed an elasticity model known as AutoElastic, which reorganizes resources automatically across synchronous parallel applications. The results, obtained with the AutoElastic prototype using the OpenNebula middleware, are encouraging. Considering a CPU‐bound application, an upper threshold close to 70% was the best option for obtaining good performance with a non‐prohibitive elasticity cost. In addition, the value of 90% for this threshold was the best option when we plan an efficiency‐driven execution. Copyright © 2015 John Wiley & Sons, Ltd.

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An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications

Mazidi

Mahdavi

Roshanfar

2021

Concurrency and Computation

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Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.

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Elasticity in cloud computing: a survey

Cited by 145 publications

References 75 publications

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

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

Joint‐analysis of performance and energy consumption when enabling cloud elasticity for synchronous HPC applications

An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications

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