About
|
Contact Us
|
Register
|
Login
Proceedings
Series
Journals
Search
EAI
ISSN:
2410-4051
Submit Article
Submission Instructions
Ethics and Malpractice Statement
Back to Journal Page
2016
Issue 8
Issue 7
Issue 6
Issue 5
2015
Issue 4
Issue 3
Issue 2
Issue 1
EAI Endorsed Transactions on Self-Adaptive Systems
Issue 3, 2015
Editor(s)-in-Chief:
Emil Vassev
Articles
Information
Hybrid Petri nets with multiple stochastic transition firings
Appears in:
sas
15
(
3
)
:
e1
Authors:
Hamed Ghasemieh, Anne Remke, Boudewijn Haverkort
Abstract:
This paper introduces an algorithm for the efficient computation of transient measures of interest in Hybrid Petri nets in which the stochastic transitions are allowed to fire an arbitrary but finite
...
number of times. Each firing increases the dimensionality of the underlying discrete/continuous s…This paper introduces an algorithm for the efficient computation of transient measures of interest in Hybrid Petri nets in which the stochastic transitions are allowed to fire an arbitrary but finite number of times. Each firing increases the dimensionality of the underlying discrete/continuous state space. The algorithm evolves around a partitioning of the multi-dimensional state-space into regions, making use of advanced algorithms (and libraries) for computational geometry. To bound the number of stochastic transition firings the notion of control tokens is newly introduced. While the new partitioning algorithm is general, the implementation is currently limited to only two stochastic firings. The feasibility and usefulness of the new algorithm is illustrated in a case study of a water refinery plant with cascading failures. more »
more >>
On Discrete Time Reversibility modulo State Renaming and its Applications
Appears in:
sas
15
(
3
)
:
e2
Authors:
Sabina Rossi, Andrea Marin
Abstract:
Time reversibility plays an important role in the analysis of continuous and discrete time Markov chains (DTMCs). Specifically, the computation of the stationary distribution of a reversible Markov ch
...
ain has been proved to be very efficient and does not require the solution of the system of global …Time reversibility plays an important role in the analysis of continuous and discrete time Markov chains (DTMCs). Specifically, the computation of the stationary distribution of a reversible Markov chain has been proved to be very efficient and does not require the solution of the system of global balance equations. A DTMC is reversible when the processes at forward and reversed time are probabilistically indistinguishable. In this paper we introduce the concept of ρ-reversibility, i.e., a notion of reversibility modulo a renaming of the states, and we contrast it with the previous definition of dynamic reversibility especially with respect to the assumptions on the state renaming function. We also discuss the applications of discrete time reversibility in the embedded and uniformized chains of continuous time processes. more »
more >>
A Framework for System Event Classification and Prediction by Means of Machine Learning
Appears in:
sas
15
(
3
)
:
e3
Authors:
Teerat Pitakrat, Jonas Grunert, Oliver Kabierschke, Fabian Keller, Andre van Hoorn
Abstract:
During operation, software systems produce large amounts of log events, comprising notifications of different severity from various hardware and software components. These data include important infor
...
mation that helps to diagnose problems in the system, e.g., post-mortem root cause analysis. Manual…During operation, software systems produce large amounts of log events, comprising notifications of different severity from various hardware and software components. These data include important information that helps to diagnose problems in the system, e.g., post-mortem root cause analysis. Manual processing of system logs after a problem occurred is a common practice. However, it is time-consuming and error-prone. Moreover, this way, problems are diagnosed after they occurred—even though the data may already include symptoms of upcoming problems. To address these challenges, we developed the SCAPE approach for automatic system event classification and prediction, employing machine learning techniques. This paper introduces SCAPE, including a brief description of the proof-of-concept implementation. SCAPE is part of our Hora framework for online failure prediction in component-based software systems. The experimental evaluation, using a publicly available supercomputer event log, demonstrates SCAPE’s high classification accuracy and first results on applying the prediction to a real world data set. more »
more >>
Detecting Performance Change in Enterprise Application Versions Using Resource Profiles
Appears in:
sas
15
(
3
)
:
e4
Authors:
Andreas Brunnert, Helmut Krcmar
Abstract:
Performance characteristics (i.e., response time, throughput, resource utilization) of enterprise applications change for each version due to feature additions, bug fixes or configuration changes. The
...
refore, performance needs to be continuously evaluated to detect performance changes (i.e., improve…Performance characteristics (i.e., response time, throughput, resource utilization) of enterprise applications change for each version due to feature additions, bug fixes or configuration changes. Therefore, performance needs to be continuously evaluated to detect performance changes (i.e., improvements or regressions). This work proposes a performance change detection process by creating and versioning resource profiles for each application version that is being built. Resource profiles are models that describe the resource demand per transaction for each component of an enterprise application and their control flow. Combined with workload and hardware environment models, resource profiles can be used to predict performance. Performance changes can be identified by comparing the performance metrics resulting from predictions of different resource profile versions (e.g., by observing an increase or decrease of response time). The source of changes in the resulting performance metrics can be identified by comparing the profiles of different application versions. We propose and evaluate an integration of these capabilities into a deployment pipeline of a continuous delivery process. more »
more >>
Automatic Extraction of Probabilistic Workload Specifications for Load Testing Session-Based Application Systems
Appears in:
sas
15
(
3
)
:
e5
Authors:
Andre van Hoorn, Christian Vögele, Eike Schulz, Wilhelm Hasselbring, Helmut Krcmar
Abstract:
Workload generation is essential to systematically evaluate performance properties of application systems under controlled conditions, e.g., in load tests or benchmarks. The definition of workload spe
...
cifications that represent the real workload as accurately as possible is one of the biggest challe…Workload generation is essential to systematically evaluate performance properties of application systems under controlled conditions, e.g., in load tests or benchmarks. The definition of workload specifications that represent the real workload as accurately as possible is one of the biggest challenges in this area. This paper presents our approach for the modeling and automatic extraction of probabilistic workload specifications for load testing session-based application systems. The approach, called WESSBAS, comprises (i.) a domain specific language (DSL) enabling layered modeling of workload specifications as well as support for (ii.) automatically extracting instances of the DSL from recorded sessions logs and (iii.) transforming instances of the DSL to workload specifications of existing load testing tools. During the extraction process, different groups of customers with similar navigational patterns are identified using clustering techniques. We developed corresponding tool support including a transformation to probabilistic test scripts for the Apache JMeter load testing tool. The evaluation of the proposed approach using the industry standard benchmark SPECjEnterprise2010 demonstrates its applicability and the representativeness of the extracted workloads. more »
more >>
Scope
A large class of software-intensive systems, including those for industrial automation, consumer electronics, airplanes, automobiles, medical devices, and civic infrastructure, must interact with the
...
physical world. More advanced systems, such as unmanned autonomous systems, don’t just interact but…A large class of software-intensive systems, including those for industrial automation, consumer electronics, airplanes, automobiles, medical devices, and civic infrastructure, must interact with the physical world. More advanced systems, such as unmanned autonomous systems, don’t just interact but also perceive important structural and dynamic aspects of their operational environment. To become interactive, an autonomous system must be aware of its physical environment and whereabouts, as well as its current internal status. This ability helps software-intensive systems sense, draw inferences, and react by exhibiting self-adaptation. As software is used for more pervasive and critical applications, support for self-adaptation is increasingly seen as necessary in avoiding costly disruptions for repair, maintenance and evolution of systems. A common understanding about the process of self-adaptation is the ability of a system to autonomously monitor its behavior and eventually modify the same according to changes in the operational environment or in the system itself. A good example of self-adaptive systems can be addressed to contemporary robotics systems that rely on the most recent advances in automation and robotic technologies to promote autonomy and self-adaptation to robotized systems. The paradigm of self-adaptive systems is closely related to AI, which makes the research and development of such systems extremely challenging and demanding new approaches that can efficiently tackle the problems of expressing autonomy requirements, designing and implementing self-adaptive features, and efficiently testing self-adaptive behavior. more »
more >>
Topics
AI for wireless communications AI for signal processing AI for image processing AI for medical/biological analysis, visualization and diagnosis AI for robotics AI for audio, language and speech proces
...
sing AI for big data mining and analysis AI for emotion analysis AI for s… AI for wireless communications AI for signal processing AI for image processing AI for medical/biological analysis, visualization and diagnosis AI for robotics AI for audio, language and speech processing AI for big data mining and analysis AI for emotion analysis AI for self-piloting automobile Applications in neuroscience Computer imaging, vision and graphics Machine learning Deep learning Deep reinforcement learning Pattern recognition Automated reasoning and inference Natural language processing Knowledge and data engineering more »
more >>
Special Issues
Special Issues Editor:
Jorge De-j. Lozoya-santos (University of Monterrey, Mexico)
Special Issues Editor:
Jorge De-j. Lozoya-santos (University of Monterrey, Mexico)
more »
Editorial Board
Bashar Nuseibeh (The Open University, UK and Lero, the Irish Software Engineering Research Center, University of Limerick) Christopher Rouff (Johns Hopkins Applied Physics Laboratory) Danny Weyns (Lin
...
naeus University) Diana Spears (Swarmotics LLC, Laramie, Wyoming, USA) Franco Zambonel…Bashar Nuseibeh (The Open University, UK and Lero, the Irish Software Engineering Research Center, University of Limerick) Christopher Rouff (Johns Hopkins Applied Physics Laboratory) Danny Weyns (Linnaeus University) Diana Spears (Swarmotics LLC, Laramie, Wyoming, USA) Franco Zambonelli (UNIMORE, Italy) Genaina Rodrigues (University of Brasilia) Giacomo Cabri (UNIMORE, Italy) Imrich Chlamtac (CREATE-NET Research Consortium, University of Trento, Italy) James Windsor (ESTEC, European Space Agency, Netherlands) Michael O'Neill (UCD, Ireland) Mike Hinchey (Lero, the Irish Software Engineering Research Center, University of Limerick) Richard Antony (University of Greenwich) Simon Dobson (University of St Andrews) more »
more >>
Journal Blurb
Temporarily closed open access journal abstracted/indexed in DBLP, CrossRef, EBSCO Discovery Services, and WorldCat. It focuses on artificial intelligence including smart manufacturing, smart cities a
...
nd smart medical services and more, with no publishing fees.Temporarily closed open access journal abstracted/indexed in DBLP, CrossRef, EBSCO Discovery Services, and WorldCat. It focuses on artificial intelligence including smart manufacturing, smart cities and smart medical services and more, with no publishing fees. more »
more >>
Publisher
EAI
ISSN
2410-4051
Volume
1
Published
2015-11-10