Design of a Performance Measurement Framework for Cloud Computing

Bautista, Luis; Abran, Alain; April, Alain

doi:10.4236/jsea.2012.52011

Cited by 24 publications

(30 citation statements)

References 6 publications

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“…This work extends the Performance Measurement Framework for Cloud Computing Applications proposed by Bautista et al [5] (Bautista's Framework), which is based on the COSMIC model, to support generic software as illustrated in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

“…The notion of "Quality in Use" of a CCA is directly linked to the quality of the network infrastructure as well as the configuration of the pooled resources [5].…”

Section: Introductionmentioning

confidence: 99%

“…From the external perspective, performance log measures try to reflect the actual utilization of the system by its end-users, who are one of the stakeholders of the software and are using it to perform a task for achieving a particular business goal [5]. These software product measurement perspectives (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Identifying Most Relevant Performance Measures for Root Cause Analysis of Performance Degradation Events on a Private Cloud Computing Application: Experiment in an Industry Environment

Ravanello

April

Gherbi

et al. 2016

BJMCS

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Cloud computing applications (CCA) are defined by their elasticity, on-demand provisioning and ability to address, cost-effectively, volatile workloads. These new cloud computing (CC) applications are being increasingly deployed by organizations but without a means of managing their performance proactively. While CCA provide advantages and disadvantages over traditional client-server applications, their unreliable application performance due to the intricacy and the high number of multi connected moving parts of its underlying infrastructure, has become a major challenge for software engineers and system Original Research Article© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/ licenses/by-nc-nd/4.0/ Ravanello et al.; BJMCS, 18(3): 1-28, 2016; Article no.BJMCS.27872 2 administrators. For example, capturing how the end-users perceive the application performance as they complete their daily tasks has not been addressed satisfactorily. One possible approach for identifying the most relevant performance measures for Root Cause Analysis (RCA) of performance degradation events on CCA, from an end-user perspective, is to leverage the information captured in performance logs, a source of data that is widely available in today's datacenters, and where detailed records of resource consumption and performance logs is captured from numerous systems, servers and network components used by the CCA. This paper builds on a model proposed for measuring CC application performance and extends it with the addition of the end-user perspective, exploring how it can be used in identifying root causes (RC) for performance degradation events in a large-scale industrial scenario. The experimentation required adjustments to the original proposal in order to determine, with the help of a multivariate statistical technique, the performance of a CCA from the perspective of an end-user. An experiment with a corporate email CCA is also presented and illustrates how the performance model can identify most relevant performance measures and help predict future performance issues.

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Section: Introductionmentioning

confidence: 99%

“…The notion of "Quality in Use" of a CCA is directly linked to the quality of the network infrastructure as well as the configuration of the pooled resources [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Most Relevant Performance Measures for Root Cause Analysis of Performance Degradation Events on a Private Cloud Computing Application: Experiment in an Industry Environment

Ravanello

April

Gherbi

et al. 2016

BJMCS

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“…Based on the above scheme, the PMFCC [16] maps the possible outcomes of a service request onto quality concepts extracted from the ISO 25010 standard. The ISO 25010 [4] standard defines software product and computer system quality from two distinct perspectives: 1) a quality in use model, and 2) a product quality model.…”

Section: Performance Measurement Framework For Cloud Computingmentioning

confidence: 99%

“…The Performance Measurement Framework for Cloud Computing (PMFCC) [16] is based on the scheme for performance analysis shown in Figure 3. This scheme establishes a set of performance criteria (or characteristics) to help to carry out the process of analysis of system performance.…”

Section: Performance Measurement Framework For Cloud Computingmentioning

confidence: 99%

Performance analysis model for big data applications in cloud computing

2014

Self Cite

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The foundation of Cloud Computing is sharing computing resources dynamically allocated and released per demand with minimal management effort. Most of the time, computing resources such as processors, memory and storage are allocated through commodity hardware virtualization, which distinguish cloud computing from others technologies. One of the objectives of this technology is processing and storing very large amounts of data, which are also referred to as Big Data. Sometimes, anomalies and defects found in the Cloud platforms affect the performance of Big Data Applications resulting in degradation of the Cloud performance. One of the challenges in Big Data is how to analyze the performance of Big Data Applications in order to determine the main factors that affect the quality of them. The performance analysis results are very important because they help to detect the source of the degradation of the applications as well as Cloud. Furthermore, such results can be used in future resource planning stages, at the time of design of Service Level Agreements or simply to improve the applications. This paper proposes a performance analysis model for Big Data Applications, which integrates software quality concepts from ISO 25010. The main goal of this work is to fill the gap that exists between quantitative (numerical) representation of quality concepts of software engineering and the measurement of performance of Big Data Applications. For this, it is proposed the use of statistical methods to establish relationships between extracted performance measures from Big Data Applications, Cloud Computing platforms and the software engineering quality concepts.

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