Performance model driven QoS guarantees and optimization in clouds

Li, Jim; Chinneck, John W.; Woodside, Murray; Litoiu, Marin; Iszlai, Gabriel

doi:10.1109/cloud.2009.5071528

Cited by 86 publications

(55 citation statements)

References 19 publications

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“…This approach comes from software performance engineering [99,100], which is a systematic, quantitative technique to construct software systems that meet performance objectives. It includes performance requirements and goals into a software development process, a technique also known as performance-driven development [68,74,77]. As in the test-driven development [15], the performance-driven development is an iterative process composed of development and performance evaluation phases at each cycle.…”

Section: Energy-aware Software Development Processmentioning

confidence: 99%

Energy-efficient Algorithms for Ultrascale Systems

Carretero

Distefano

Petcu

et al. 2015

JSFI

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The chances to reach Exascale or Ultrascale Computing are strongly connected with the problem of the energy consumption for processing applications. For physical and economical reasons, the energy consumption has to be reduced significantly to make Ultrascale Computing possible. The research efforts towards energy-saving mechanisms of the hardware have already made energy-aware hardware systems available. However, to achieve a strong energy reduction, hardware mechanisms must be complemented with new energy-efficient software that can exploit them so that the foreseen energy savings actually result. In the software area, there also exist a multitude of research approaches towards energy saving, often concentrating either on the system software level or the application organization level, reflecting the expertise of the corresponding research group. The challenge of reducing the energy consumption dramatically to make Ultrascale Computing possible is so ambitious that a concerted action combining research efforts through all the software levels seems reasonable. In this article, we discuss the current research efforts and results related to energy efficiency in the diverse areas of software. We conclude with open problems and questions concerning energy-related techniques with an emphasis on the application or algorithmic side.

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Section: Energy-aware Software Development Processmentioning

confidence: 99%

Energy-efficient Algorithms for Ultrascale Systems

Carretero

Distefano

Petcu

et al. 2015

JSFI

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“…While the increasing popularity of cloud computing has spawned very interesting research on private and public clouds, to the best of our knowledge, no previous work has evaluated IaaS clouds using complex n-tier systems to find performance and scalability issues at all tiers. Jim et al presented a method for achieving optimization in clouds by using performance models in the development, deployment, and operation of applications that run in the cloud [10]. They illustrated the architecture of the cloud, the services offered by the cloud to support optimization, and the methodology used by developers to enable runtime optimization of the clouds.…”

Section: Related Workmentioning

confidence: 99%

Variations in Performance and Scalability When Migrating n-Tier Applications to Different Clouds

Jayasinghe

Malkowski

Wang

et al. 2011

2011 IEEE 4th International Conference on Cloud Computing

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Abstract-The increasing popularity of computing clouds continues to drive both industry and research to provide answers to a large variety of new and challenging questions. We aim to answer some of these questions by evaluating performance and scalability when an n-tier application is migrated from a traditional datacenter environment to an IaaS cloud. We used a representative n-tier macro-benchmark (RUBBoS) and compared its performance and scalability in three different testbeds: Amazon EC2, Open Cirrus (an open scientific research cloud), and Emulab (academic research testbed). Interestingly, we found that the best-performing configuration in Emulab can become the worst-performing configuration in EC2. Subsequently, we identified the bottleneck components, high context switch overhead and network driver processing overhead, to be at the system level. These overhead problems were confirmed at a finer granularity through micro-benchmark experiments that measure component performance directly. We describe concrete alternative approaches as practical solutions for resolving these problems.

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“…LQNs are one of the most adopted technique and have been efficiently applied also in other related contexts, like ERP applications [10] heavily adopting multithreading and multi-core architectures, in the early phases of Software Product Lines [12], in the optimization of the deployment of multiple Web applications on a cloud [8], with replicated components [9], where LQN based approaches scale very well.…”

Section: Related Workmentioning

confidence: 99%

Model-Driven Web Engineering Performance Prediction with Layered Queue Networks

Gambi

Toffetti

Comai

2010

Current Trends in Web Engineering

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Abstract. This position paper describes an approach to predict the performances of a Web application already in the early stages of application development. It leverages the wealth of information of MDWE solutions to automatically obtain accurate representations of the running application in terms of layered queue networks (LQNs), i.e., analytical models simulating the behavior of the system and computing the performances mathematically. In particular, the paper discusses how a MDWE methodology can be exploited to generate such performance models and presents a proof of concept example.

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Performance model driven QoS guarantees and optimization in clouds

Abstract: Abstract

Cited by 86 publications

References 19 publications

Energy-efficient Algorithms for Ultrascale Systems

Energy-efficient Algorithms for Ultrascale Systems

Variations in Performance and Scalability When Migrating n-Tier Applications to Different Clouds

Model-Driven Web Engineering Performance Prediction with Layered Queue Networks

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