Autonomic resource provisioning for cloud-based software

Jamshidi, Pooyan; Ahmad, Aakash; Pahl, Claus

doi:10.1145/2593929.2593940

Cited by 122 publications

(139 citation statements)

References 37 publications

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“…3.1 was chosen based on previous capacity analysis of multitier cloud application [35,40,86]. The functions applied through both Pivotal.io and Azure.com developer portals 10 .…”

Section: Methodsmentioning

confidence: 99%

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

2017

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CLOUD COMPUTING COST AND ENERGY OPTIMIZATION THROUGH FEDERATED CLOUD SOSThe two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by:(1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means.This work analyzes the core SoS requirements, concept synthesis, and functional architecture.It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with ii security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks.iii ACKNOWLEDGEMENTS

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“…3.1 was chosen based on previous capacity analysis of multitier cloud application [35,40,86]. The functions applied through both Pivotal.io and Azure.com developer portals 10 .…”

Section: Methodsmentioning

confidence: 99%

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

2017

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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%

“…The authors of [47,67,97] proposed horizontal elasticity solution for Internet based systems using fuzzy systems. They all have used performance based metrics to make scaling decisions.…”

Section: Intelligentmentioning

confidence: 99%

“…They all have used performance based metrics to make scaling decisions. Jamshidi et al [47] highlighted the lack of handling uncertainty related issues in existing autoscaling approaches and the static scaling behaviour of commercially available rule based systems. They proposed the idea of qualitative elasticity rules using a fuzzy control system to the aforementioned issues.…”

Section: Intelligentmentioning

confidence: 99%

“…This approach however, assume that all the servers are homogeneous. Furthermore, in contrast to [47,67], they also consider resource utilisation in decision making in accompanying to their performance measurements.…”

Section: Intelligentmentioning

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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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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Autonomic resource provisioning for cloud-based software

Cited by 122 publications

References 37 publications

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

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

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