A Specification Language for Performance and Economical Analysis of Short Term Data Intensive Energy Management Services

Merino, Alberto; Tolosana-Calasanz, Rafael; Bañares, José Ángel; Colom, José Manuel

doi:10.1007/978-3-319-43177-2_10

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…In [20], an iterative, and step-wise refinement methodology was proposed for cloud applications, covering all the software lifecycle steps. The methodology is centered around a performance model that captures (non) functional requirements, control flow, data flow and the involved computing resources.…”

Section: Related Workmentioning

confidence: 99%

Characterising resource management performance in Kubernetes

Medel

Tolosana-Calasanz

Bañares

et al. 2018

Computers & Electrical Engineering

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Section: Related Workmentioning

confidence: 99%

Characterising resource management performance in Kubernetes

Medel

Tolosana-Calasanz

Bañares

et al. 2018

Computers & Electrical Engineering

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“…In [19], the researchers propose an iterative and step-wise refinement methodology that begins with the modelling of functional requirements. Afterwards, they model control and data flow together with the specification of the computational resources available.…”

Section: Discussionmentioning

confidence: 99%

Client-Side Scheduling Based on Application Characterization on Kubernetes

Medel

Tolón

Arronategui

et al. 2017

Lecture Notes in Computer Science

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A key challenge for supporting elastic behaviour in cloud systems is performant automated (de-)provisioning and scheduling of computing resources. Containers are rapidly replacing Virtual Machines (VMs) as the compute instance of choice in many cloud deployments, due to lower overhead of starting up and terminating containers in comparison to VMs. Performance overheads associated with deploying, terminating and maintaining a container can be significant. We analyse performance of the Kubernetes system and develop a Petri net-based model of resource management within this system. Our model is characterised using data from a Kubernetes deployment, and can be used as a basis to support capacity planning and design of scaleable applications that make use of Kubernetes.

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“…We do not provide a graphical representation model of both the functional and operational models linked together because we understand it will be difficult to display. For that reason, we developed an abstraction and specification language [16] that can be found in the Appendix.…”

Section: A Grid Operational Modelmentioning

confidence: 99%

“…In [16], a preliminary specification language to support the methodology described here was provided. The language intends to simplify the definition of Petri net models, by providing high-level constructions on top of the formalism.…”

Section: Introductionmentioning

confidence: 99%

Model-driven development of data intensive applications over cloud resources

Tolosana-Calasanz

Bañares

Colom

2018

Future Generation Computer Systems

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The proliferation of sensors over the last years has generated large amounts of raw data, forming data streams that need to be processed. In many cases, cloud resources are used for such processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and lowlatency requirements that go beyond the cost effective and flexible solutions supported by existing cloud frameworks, such as Apache Kafka, Apache Spark Streaming, or Map-Reduce Streams. In this paper, we describe a model-driven and stepwise refinement methodological approach for streaming applications executed over clouds. The central role is assigned to a set of Petri Net models for specifying functional and non-functional requirements. They support model reuse, and a way to combine formal analysis, simulation, and approximate computation of minimal and maximal boundaries of non-functional requirements when the problem is either mathematically or computationally intractable. We show how our proposal can assist developers in their design and implementation decisions from a performance perspective. Our methodology allows to conduct performance analysis: The methodology is intended for all the engineering process stages, and we can (i) analyse how it can be mapped onto cloud resources, and (ii) obtain key performance indicators, including throughput or economic cost, so that developers are assisted in their development tasks and in their decision taking. In order to illustrate our approach, we make use of the pipelined wavefront array.

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A Specification Language for Performance and Economical Analysis of Short Term Data Intensive Energy Management Services

Cited by 6 publications

References 14 publications

Characterising resource management performance in Kubernetes

Characterising resource management performance in Kubernetes

Client-Side Scheduling Based on Application Characterization on Kubernetes

Model-driven development of data intensive applications over cloud resources

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