CLIF, a framework based on Fractal for flexible, distributed load testing

Dillenseger, Bruno

doi:10.1007/s12243-008-0067-9

Cited by 19 publications

(19 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the implementation level, we have proposed an XML-based formalism to describe the cloud applications to be deployed, and we have developed a Java tool chain, named Virtual Application Management Platform (VAMP), which includes the reference implementation of the self-deployment protocol [13]. For evaluation purposes, this implementation has been used to deploy realworld applications, e.g., multitier Web application architectures or the Clif load-injection framework [10].…”

Section: Introductionmentioning

confidence: 99%

Reliable self-deployment of cloud applications

Etchevers

Salaün

Boyer

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

Cloud applications consist of a set of interconnected software elements distributed over several virtual machines, themselves hosted on remote physical servers. Most existing solutions for deploying such applications require human intervention to configure parts of the system, do not respect functional dependencies among elements that must be respected when starting them, and do not handle virtual machine failures that can occur when deploying an application. This paper presents a self-deployment protocol that was designed to automatically configure a set of software elements to be deployed on different virtual machines. This protocol works in a decentralized way, i.e., there is no need for a centralized server. It also starts the software elements in a certain order, respecting important architectural invariants. This protocol supports virtual machine and network failures, and always succeeds in deploying an application when faced with a finite number of failures. Designing such highly parallel management protocols is difficult, therefore formal modeling techniques and verification tools were used for validation purposes. The protocol was implemented in Java and was used to deploy industrial applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Reliable self-deployment of cloud applications

Etchevers

Salaün

Boyer

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

View full text Add to dashboard Cite

show abstract

“…In this section we will examine several studies that provide workloads for system evaluation in local and cloud environments. Dillenseger (2009) [10] propose a framework called CLIF to generate a distributed workload in heterogeneous computational systems. The framework consists of three main parts: 1) The workload injector which handles the requests for the target system; 2) The probes which monitor the metrics in environment; 3) other components which store and analyse the metrics derived from the probes.…”

Section: Related Workmentioning

confidence: 99%

“…The work adopts a flexible approach which can be extended to new proposals as discussed by Tchana et al (2013) [11] who set a benchmark with the aid of this architecture. Tchana et al (2015) draw on the work carried out by Dillenseger (2009) [10] and Tchana et al (2013) [11] to propose a Benchmark as a Service (BaaS) model, which is capable of generating self-scalable distributed workloads. A protocol is added to manage the workload injector in the CLIF framework.…”

Section: Related Workmentioning

confidence: 99%

“…It ishould be noted that all the related works set out a range of choices to generate and apply their systems in different contexts. Dillenseger (2009) [ [16] make use of the cloud environment to generate the workload. However they are not able to ensure the quality of the generated work or in some cases, fail to make use of distributed hosts.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

PEESOS-Cloud: A Workload-Aware Architecture for Performance Evaluation in Service-Oriented Systems

Ferreira

Nunes

Pereira

et al. 2016

2016 IEEE World Congress on Services (SERVICES)

View full text Add to dashboard Cite

Abstract-It is a challenging task to ensure quality in serviceoriented systems deployed in cloud computing owing to the dynamicity of its environment. Many approaches have been adopted to identify and evaluate bottlenecks and problems in performance. The most common scenario consists of distributed systems that use a workload capable of enabling clients to exploit the target system in different operational conditions. However, one requirement that tends to be overlooked is to determine how the workload is executed, as software and hardware faults can lead to its mischaracterization. In this paper, a number of problems in the workload generation have been identified and summarized. A new architecture, called PEESOS-Cloud, is proposed which allows these services to be evaluated as well as to improve the ability of the workload so that it conforms with its described characteristics. Experiments in a cloud environment were conducted to show how PEESOS-Cloud works and validate its capabilities. Our experiment also showed that the mischaracterization of the workload leads to poor results, whereas an workload-aware implementation leads to a better performance evaluation.

show abstract

“…As described in details in [1], CLIF's architecture is based on the Fractal component model [3], which easies its adaptation. Load injector and probe components are distributed through the network.…”

Section: The Clif Load Injection Frameworkmentioning

confidence: 99%

A Scalable Benchmark as a Service Platform

Tchana

Palma

El-Rheddane

et al. 2013

Distributed Applications and Interoperable Systems

Self Cite

View full text Add to dashboard Cite

Abstract. Load testing has always been a crucial and expensive activity for software companies. Classical solutions are a real burden to setup statically and their cost are prohibitive in terms of human and hardware resources. Cloud computing brings new opportunities to stress application scalability as load testing solutions can be provided on demand by the cloud. This paper describes a Benchmark-as-a-Service solution that scales automatically the load injection platform and eases its setup according to load profiles. Our approach is based on: (i) the virtualization of the Benchmarking platform to enable the injector's self-scalability, (ii) an online calibration mechanism to characterize injector capacity and impact on the benched application, (iii) a provisioning solution to scale the load injection platform sufficiently ahead of time. We also report experiments on a benchmark that shows the benefits in terms of cost and resources savings.

show abstract

CLIF, a framework based on Fractal for flexible, distributed load testing

Cited by 19 publications

References 9 publications

Reliable self-deployment of cloud applications

Reliable self-deployment of cloud applications

PEESOS-Cloud: A Workload-Aware Architecture for Performance Evaluation in Service-Oriented Systems

A Scalable Benchmark as a Service Platform

Contact Info

Product

Resources

About