A Cost-Aware Elasticity Provisioning System for the Cloud

Sharma, Upendra; Shenoy, Prashant; Sahu, Sambit; Shaikh, Anees

doi:10.1109/icdcs.2011.59

Cited by 218 publications

(138 citation statements)

References 12 publications

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“…The curve of the pricing model may be tuned according to the provider's needs and goals [24]. For example, the cost of resources may increase superlinearly for more capable (faster) resources [23], or it may increase sublinearly with the performance level as in [23,24]. Similarly, in [14] the price increases exponentially with the speed of the VMs.…”

Section: Problem Description and Assumptionsmentioning

confidence: 99%

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Pietri

Sakellariou

2016

Economics of Grids, Clouds, Systems, and Services

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Abstract. Cloud providers now offer resources as combinations of CPU frequencies and prices, with faster resources (which operate at higher frequencies) charged at a higher monetary cost. With the emergence of this new pricing scheme, the problem of choosing cost-efficient configurations is becoming even more challenging for users. The frequencies required to achieve cost-efficient configurations may vary in different scenarios, depending on both the provider's pricing model and the application characteristics. In this paper, two cost-aware algorithms that select low-cost CPU frequencies for each resource to complete a scientific workflow application within a deadline and at a minimum cost are presented. The proposed approaches are evaluated and compared through simulation using different pricing models that charge resource provisioning also based on the CPU frequency.

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Section: Problem Description and Assumptionsmentioning

confidence: 99%

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Pietri

Sakellariou

2016

Economics of Grids, Clouds, Systems, and Services

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“…This situation is complicated by the current IaaS pricing models: machines configurations are not priced linearly with their performance. For example, an EC2 large instance can serve more web requests per core than the small instance, but their price per core is the same [9]. Moreover, for the same machine configuration, the clouds offer different billing options on-demand-, reserved-, and spot-instances, which are charged differently.…”

Section: Finding Scheduling Polices That Can Schedule Diverse Workloamentioning

confidence: 99%

“…Moreover, for the same machine configuration, the clouds offer different billing options on-demand-, reserved-, and spot-instances, which are charged differently. Scheduling enough resources to meet user demands yet keep the cost low while adapting to workload changes remains challenging, despite recent research efforts [9][10][11].…”

Section: Finding Scheduling Polices That Can Schedule Diverse Workloamentioning

confidence: 99%

“…We focus on the CPU requirement. In practice, r i can be obtained though profiling [9,14] or can be provided by the user.…”

Section: Workload and Resource Modelmentioning

confidence: 99%

“…Mao et al [26] propose a linear program for provisioning, and allocate jobs randomly to VMs. Sharma et al [9] use on-demand instances and use migration but only for homogeneous workloads.…”

Section: Related Workmentioning

confidence: 99%

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Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

Shen

Deng

Iosup

et al. 2013

Euro-Par 2013 Parallel Processing

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Abstract. Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy -larger or faster instances? on-demand or reserved instances? etc.-and to configure the leasing strategy with appropriate scheduling policies is still daunting for the (potential) cloud user. In this work, we investigate leasing strategies and their policies from a broker's perspective. We propose, CoH, a family of Cloud-based, online Hybrid scheduling policies that minimizes rental cost by making use of both on-demand and reserved instances. We formulate the resource provisioning and job allocation policies as Integer Programming problems. As the policies need to be executed online, we limit the time to explore the optimal solution of the integer program, and compare the obtained solution with various heuristics-based policies; then automatically pick the best one. We show, via simulation and using multiple real-world traces, that the hybrid leasing policy can obtain significantly lower cost than typical heuristics-based policies.

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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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A Cost-Aware Elasticity Provisioning System for the Cloud

Cited by 218 publications

References 12 publications

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

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

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