Cost/Performance Evaluation for Cloud Applications Using Simulation

Rak, Massimiliano; Cuomo, Antonio; Villano, Umberto

doi:10.1109/wetice.2013.36

Cited by 12 publications

(9 citation statements)

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“…This is partially, due to the complexity of the problem in terms of workflow performance behavior and due to the modernity of cloud simulation. Some notable contributions in this area include Rak et al [11], who presents a technique to evaluate the trade-off between costs and performance of the cloud application through benchmarks and simulation based on the mOSAIC framework. In [13] the authors extend this approach to consider bag-of-tasks scientific applications.…”

Section: Background and Related Workmentioning

confidence: 99%

Simulation of Runtime Performance of Big Data Workflows on the Cloud

Llwaah

Cała

Thomas

2016

Computer Performance Engineering

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Section: Background and Related Workmentioning

confidence: 99%

Simulation of Runtime Performance of Big Data Workflows on the Cloud

Llwaah

Cała

Thomas

2016

Computer Performance Engineering

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“…Rehman et al [13] focus on the issue of cloud service monitoring, and propose a novel user-feedbackbased approach which can monitor cloud performance more reliably and accurately as compared with the existing mechanisms. [14] presents a technique to evaluate the trade-off between costs and performance of cloud applications through the use of benchmarks and simulation. For a given cloud application, it is possible to predict performance indexes and resource consumption under generic workloads.…”

Section: Related Workmentioning

confidence: 99%

Performance Difference Prediction in Cloud Services for SLA-Based Auditing

Zhang

Zhou

2015

2015 IEEE Symposium on Service-Oriented System Engineering

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Cloud computing allows individuals and organizations outsource their applications to cloud due to the flexibility and cost savings. However, cloud service providers (CSPs) may offer reduced resources to tenant by virtualization technology for illegal economic benefit. Most cloud tenants don't have specialized knowledge to detect the CSPs' service level agreement (SLA) disruption. To address this issue, we propose a worst-case performance prediction scheme to audit the SLA disruption of cloud by comparison between actual performance cost and the predicted worst-case performance cost. Firstly, we insert labels into application, and collect the running time information on cloud on a small input dataset. The historical information is used to identify the frequent block. Then, we construct the performance cost function for each hotspot block, and resort to curve fitting method to obtain the performance cost function of each frequent block. Finally, we get the worst-case performance prediction by performance estimation model and performance cost function of each one on large input dataset. The experiments show that our proposed scheme can achieve high sensitiveness on cloud SLA and fulfill cloud SLA auditing.

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“…The focus here will be on the design and implementation of portable job distributing application. However, we aim at integrating it such solution with existing performance/cost prediction tools as the one presented in [11].…”

Section: Job Scheduling In Hpc and In The Cloudmentioning

confidence: 99%

“…The development of cloud-aware applications has been discussed in [11]. In this paper, the focus will be instead on the use of "traditional" scientific MPI applications on virtual resources leased from a cloud.…”

Section: Introductionmentioning

confidence: 99%

“…Now it is up to the application developer/user, when running its application, to select carefully the resources to be used on the basis of the application characteristics and the expected workload, in order to obtain satisfactory performance and to minimize the cloud leasing costs. As outlined in [11], this optimization can be based on the selection of both the amount of resources, and the provider to be used.…”

Section: Introductionmentioning

confidence: 99%

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A Portable Tool for Running MPI Applications in the Cloud

Rak

Turtur

Villano

et al. 2014

2014 International Conference on Intelligent Networking and Collaborative Systems

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Cloud computing founds on the pay-per-use paradigm, and offers a simple way to acquire any kind of resources through an as-a-service approach. As a consequence, the cloud is currently seen as a viable and inexpensive alternative to customary parallel/distributed computing solutions, ranging from small clusters to GRIDs, which were widely used in the past.In this paper we present a portable cloud tool, cMe (cloud MPI enabler), able to run on any cloud provider, which builds up dedicated cloud-based MPI-enabled clusters. The objective is to help the end-users to choose dynamically both the cloud provider and the amount of the resources to be used. The cloud application performs simple job scheduling, based on the same principles adopted in the typical HPC environments (i.e., different jobs never share computing resources), but also enables the dynamical acquisition of resources. For user convenience, the application is provided with a simple web interface that helps the resource acquisition process.

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Cost/Performance Evaluation for Cloud Applications Using Simulation

Cited by 12 publications

References 12 publications

Simulation of Runtime Performance of Big Data Workflows on the Cloud

Simulation of Runtime Performance of Big Data Workflows on the Cloud

Performance Difference Prediction in Cloud Services for SLA-Based Auditing

A Portable Tool for Running MPI Applications in the Cloud

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