Enhanced Modeling and Solution of Layered Queueing Networks

Franks, Greg; Al-Omari, Tariq; Woodside, Murray; Das, Olivia; Derisavi, S.

doi:10.1109/tse.2008.74

Cited by 171 publications

(128 citation statements)

References 27 publications

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“…State of the art solution engines such as LINE [8] or LQNS [9] can be used to evaluate the performance model and derive the QoS metrics. The designed solution is also evaluated in term of costs in order to provide an estimation of the daily cost involved in using the proposed cloud infrastructure.…”

Section: Space4cloudmentioning

confidence: 99%

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Guerriero

Ciavotta

Gibilisco

et al. 2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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Recently we witnessed a deep transformation in the the design, development and management of modern applications, which have grown in scope and size becoming distributed and service-oriented. A big part in this metamorphosis is played by the Cloud with the availability of almost-infinite resources, high availability and outsourced maintenance. This has led to the emergence of new software development methodologies to effectively deal with this paradigm shift in the field of software engineering. DevOps is one of them, it advocates for a greater level of collaboration and convergence between developers and other IT professionals. Consequently, new tools, purposely designed to ease this process, are required. In this scenario, we present SPACE4Cloud, a DevOps integrated environment for model-driven design-time quality of service assessment and optimization, and runtime capacity allocation of Cloud applications.

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Section: Space4cloudmentioning

confidence: 99%

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Guerriero

Ciavotta

Gibilisco

et al. 2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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“…Performance metrics generated include mean response time, throughputs and utilisation information for each service class. A detailed description of the layered queuing method can be found in [9,23].…”

Section: Definition and Experimental Investigation Of The Layered Quementioning

confidence: 99%

Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

Bacigalupo

Hemert

Chen

et al. 2011

Simulation Modelling Practice and Theory

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The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic − urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management.

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“…Recent examples of such validation include using the SPEC jAppServer 2004 workload running on WebSphere and Oracle [17], using an online stock trating simulation with two different architectures and two different platforms [21,20], using a generic performance model of an EJB server in various scenarios [32], or using a model of an air traffic control system [12]. While the cited validation examples are without doubt more realistic than our approach, they are also a case in point we are making: the expenses of manually constructing the performance models prevent validation on a large number of case studies.…”

Section: Related Workmentioning

confidence: 99%

Validating Model-Driven Performance Predictions on Random Software Systems

Babka

Tůma

Bulej

2010

Research Into Practice – Reality and Gaps

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Abstract. Software performance prediction methods are typically validated by taking an appropriate software system, performing both performance predictions and performance measurements for that system, and comparing the results. The validation includes manual actions, which makes it feasible only for a small number of systems. To significantly increase the number of systems on which software performance prediction methods can be validated, and thus improve the validation, we propose an approach where the systems are generated together with their models and the validation runs without manual intervention. The approach is described in detail and initial results demonstrating both its benefits and its issues are presented.Key words: performance modeling, performance validation, MDD MotivationState of the art in model-driven software performance prediction builds on three related factors: the availability of architectural and behavioral software models, the ability to solve performance models, and the ability to transform the former models into the latter. This is illustrated for example by the survey of modeldriven software performance prediction [3], which points out that the typical approach is to use UML diagrams for specifying both the architecture and the behavior of the software system, and to transform these diagrams into performance models based on queueing networks.Both the models and the methods involved in the prediction process necessarily include simplifying assumptions that help abstract away from some of the complexities of the modeled system, e.g., approximating real operation times with probability distributions or assuming independence of operation times. These simplifications are necessary to make the entire prediction process tractable, but the complexity of the modeled system usually makes it impossible to say how the simplifications influence the prediction precision.Self-archived copy. The original publication is available at www.springerlink.com,

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Enhanced Modeling and Solution of Layered Queueing Networks

Cited by 171 publications

References 27 publications

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

A Model-Driven DevOps Framework for QoS-Aware Cloud Applications

Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models

Validating Model-Driven Performance Predictions on Random Software Systems

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