Issue
DARTS, Volume 3, Issue 1
Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017)
-
Part of:
Volume:
DARTS, Volume 3 (ECOOP 2017)
Journal: Dagstuhl Artifacts Series (DARTS)
Editors
Publication Details
- published at: 2017-05-16
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- DBLP: db/journals/darts/darts3
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Front Matter - SEAMS 2017 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee
Abstract
Front Matter - SEAMS 2017 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee
Cite as
Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{ca'mara_et_al:DARTS.3.1.0,
author = {Cámara, Javier and Nuseibeh, Bashar and Garlan, David},
title = {{ Front Matter - SEAMS 2017 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee}},
pages = {0:i--0:xii},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Cámara, Javier and Nuseibeh, Bashar and Garlan, David},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.0},
URN = {urn:nbn:de:0030-drops-71389},
doi = {10.4230/DARTS.3.1.0},
annote = {Keywords: Front Matter - SEAMS 2017 Artifacts, Table of Contents, Preface, Artifact Evaluation Committee}
}
Document
Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact)
Abstract
Arising with the popularity of Hadoop, optimizing Hadoop executions has grabbed lots of attention from research community.
Many research contributions are proposed to elevate Hadoop performance, particularly in the domain of self-adaptive software systems.
However, due to the complexity of Hadoop operation and the difficulty to reproduce experiments, the efforts of these Hadoop-related research are hard to be evaluated.
To address this limitation, we propose a research acceleration platform for rapid prototyping and evaluation of self-adaptive behavior in Hadoop clusters.
It provides an automated manner to quickly and easily provision reproducible Hadoop environments and execute acknowledged benchmarks.
This platform is based on the state-of-the-art container technology that supports both distributed configurations as well as standalone single-host setups.
We demonstrate the approach on a complete implementation of a concrete Hadoop self-adaptive case study.
Cite as
Bo Zhang, Filip Krikava, Romain Rouvoy, and Lionel Seinturier. Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 1:1-1:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{zhang_et_al:DARTS.3.1.1,
author = {Zhang, Bo and Krikava, Filip and Rouvoy, Romain and Seinturier, Lionel},
title = {{Hadoop-Benchmark: Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters (Artifact)}},
pages = {1:1--1:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Zhang, Bo and Krikava, Filip and Rouvoy, Romain and Seinturier, Lionel},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.1},
URN = {urn:nbn:de:0030-drops-71392},
doi = {10.4230/DARTS.3.1.1},
annote = {Keywords: Hadoop, Docker, Rapid Prototyping, Benchmark}
}
Document
Self-Adaptive Video Encoder: Comparison of Multiple Adaptation Strategies Made Simple (Artifact)
Abstract
This paper presents an adaptive video encoder that can be used to compare the behavior of different adaptation strategies using multiple actuators to steer the encoder towards a global goal, composed of multiple conflicting objectives.
A video camera produces frames that the encoder manipulates with the objective of matching some space requirement to fit a given communication channel. A second objective is to maintain a given similarity index between the manipulated frames and the original ones.
To achieve the goal, the software can change three parameters: the quality of the encoding, the noise reduction filter radius and the sharpening filter radius.
In most cases the objectives - small encoded size and high quality - conflict, since a larger frame would have a higher similarity index to its original counterpart. This makes the problem difficult from the control perspective and makes the case study appealing to compare different adaptation strategies.
Cite as
Martina Maggio, Alessandro Vittorio Papadopoulos, Antonio Filieri, and Henry Hoffmann. Self-Adaptive Video Encoder: Comparison of Multiple Adaptation Strategies Made Simple (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 2:1-2:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{maggio_et_al:DARTS.3.1.2,
author = {Maggio, Martina and Papadopoulos, Alessandro Vittorio and Filieri, Antonio and Hoffmann, Henry},
title = {{Self-Adaptive Video Encoder: Comparison of Multiple Adaptation Strategies Made Simple (Artifact)}},
pages = {2:1--2:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Maggio, Martina and Papadopoulos, Alessandro Vittorio and Filieri, Antonio and Hoffmann, Henry},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.2},
URN = {urn:nbn:de:0030-drops-71408},
doi = {10.4230/DARTS.3.1.2},
annote = {Keywords: self-adaptive software, video encoding, comparison, control theory}
}
Document
UNDERSEA: An Exemplar for Engineering Self-Adaptive Unmanned Underwater Vehicles (Artifact)
Abstract
Recent advances in embedded systems and underwater communications raised the
autonomy levels in unmanned underwater vehicles (UUVs) from human-driven and
scripted to adaptive and self-managing. UUVs can execute longer and more
challenging missions,
and include functionality that enables adaptation to unexpected oceanic or
vehicle changes.
As such, the UNDERSEA artifact
facilitates the development, evaluation and comparison of self-adaptation
solutions in a new
and important application domain.
UNDERSEA comes with predefined oceanic surveillance UUV missions, adaptation
scenarios, and a
reference controller implementation, all of which can easily be
extended or replaced.
Cite as
Simos Gerasimou, Radu Calinescu, Stepan Shevtsov, and Danny Weyns. UNDERSEA: An Exemplar for Engineering Self-Adaptive Unmanned Underwater Vehicles (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 3:1-3:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{gerasimou_et_al:DARTS.3.1.3,
author = {Gerasimou, Simos and Calinescu, Radu and Shevtsov, Stepan and Weyns, Danny},
title = {{UNDERSEA: An Exemplar for Engineering Self-Adaptive Unmanned Underwater Vehicles (Artifact)}},
pages = {3:1--3:2},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Gerasimou, Simos and Calinescu, Radu and Shevtsov, Stepan and Weyns, Danny},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.3},
URN = {urn:nbn:de:0030-drops-71416},
doi = {10.4230/DARTS.3.1.3},
annote = {Keywords: Unmanned underwater vehicle exemplar; self-adaptive embedded systems; oceanic surveillance}
}
Document
DeltaIoT: A Real World Exemplar for Self-Adaptive Internet of Things (Artifact)
Abstract
The DeltaIoT exemplar enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in Internet of Things (IoT). The exemplar applies multi-hop communication, where each IoT mote must have a path towards the gateway along other motes. Our motes use LoRa radio technology supporting long range communication. The focus is on dynamically adapting the network settings of the IoT motes (e.g., transmission power and spreading factor) to reduce the energy consumption of the motes and guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance. Self-adaptation can automate these tasks. The exemplar provides several reference scenarios for experimentation. DeltaIoT comprises a simulator for offline experimentation and a physical setup of 25 motes that can be accessed remotely for experimentation in the field. This IoT system is deployed at the Computer Science Department Campus of KU Leuven.
Cite as
M. Usman Iftikhar, Gowri Sankar Ramachandran, Pablo Bollansée, Danny Weyns, and Danny Hughes. DeltaIoT: A Real World Exemplar for Self-Adaptive Internet of Things (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 4:1-4:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{iftikhar_et_al:DARTS.3.1.4,
author = {Iftikhar, M. Usman and Ramachandran, Gowri Sankar and Bollansée, Pablo and Weyns, Danny and Hughes, Danny},
title = {{DeltaIoT: A Real World Exemplar for Self-Adaptive Internet of Things (Artifact)}},
pages = {4:1--4:2},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Iftikhar, M. Usman and Ramachandran, Gowri Sankar and Bollansée, Pablo and Weyns, Danny and Hughes, Danny},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.4},
URN = {urn:nbn:de:0030-drops-71425},
doi = {10.4230/DARTS.3.1.4},
annote = {Keywords: Internet-of-Things; self-adaptation; exemplar}
}
Document
Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)
Abstract
This artifact supports our research in self-adaptation in large-scale
software-intensive distributed systems. The main problem
in making such systems self-adaptive is that their adaptation
needs to consider the current situation in the whole system.
However, developing a complete and accurate model of such
systems at design time is very challenging. We are instead investigating
a novel approach where the system model consists only
of the essential input and output parameters and Big Data
analytics is used to guide self-adaptation based on a continuous
stream of operational data. In this artifact, we provide a concrete model
problem that can be used as a case study for evaluating different self-adaptation
techniques pertinent to complex large-scale distributed systems.
We also provide an extensible tool-based framework for endorsing an arbitrary
system with self-adaptation based on analysis of operational
data coming from the system. The model problem (CrowdNav) and the framework (RTX) have been packaged together in this artifact, but can also work independently.
Cite as
Sanny Schmid, Ilias Gerostathopoulos, Christian Prehofer, and Tomas Bures. Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 5:1-5:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{schmid_et_al:DARTS.3.1.5,
author = {Schmid, Sanny and Gerostathopoulos, Ilias and Prehofer, Christian and Bures, Tomas},
title = {{Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics (Artifact)}},
pages = {5:1--5:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Schmid, Sanny and Gerostathopoulos, Ilias and Prehofer, Christian and Bures, Tomas},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.5},
URN = {urn:nbn:de:0030-drops-71435},
doi = {10.4230/DARTS.3.1.5},
annote = {Keywords: self-adaptation; Big Data analytics; model problem, tool, framework}
}
Document
Intelligent Ensembles – a Declarative Group Description Language and Java Framework (Artifact)
Abstract
Smart cyber-physical systems (sCPS) is a growing research field focused on scenarios such as smart cities or smart mobility, where autonomous components are deployed in a physical environment, and are expected to cooperate with one another, as well as with humans. As these systems typically operate in a highly uncertain and dynamically changing environment, being able to cooperate and adapt in groups to cope with various (possibly unanticipated) situations becomes a crucial and challenging task. In this artifact, we respond to this challenge by presenting the Intelligent Ensembles framework, consisting of a high-level declarative language for describing dynamic cooperation groups, and a Java runtime library for automatically forming groups that best satisfy the given specification. The framework provides dynamic architecture adaptation (i.e., forming groups of components and exchanging data between them) based on the state of components and situation in their environment. Further, the framework can be used as a first step of a group-wise adaptation (i.e., identifying components that are to negotiate and coordinate in an adaptation). The framework is built on top of the Z3 SMT solver and the Eclipse Modelling Framework.
Cite as
Filip Krijt, Zbynek Jiracek, Tomas Bures, Petr Hnetynka, and Ilias Gerostathopoulos. Intelligent Ensembles – a Declarative Group Description Language and Java Framework (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{krijt_et_al:DARTS.3.1.6,
author = {Krijt, Filip and Jiracek, Zbynek and Bures, Tomas and Hnetynka, Petr and Gerostathopoulos, Ilias},
title = {{Intelligent Ensembles – a Declarative Group Description Language and Java Framework (Artifact)}},
pages = {6:1--6:3},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Krijt, Filip and Jiracek, Zbynek and Bures, Tomas and Hnetynka, Petr and Gerostathopoulos, Ilias},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.6},
URN = {urn:nbn:de:0030-drops-71444},
doi = {10.4230/DARTS.3.1.6},
annote = {Keywords: Smart Cyber-physical Systems; Adaptive Architecture; Ensemble-based Component System; Group-wise Adaptation; Autonomic Systems}
}
Document
Lotus@Runtime: A Tool for Runtime Monitoring and Verification of Self-adaptive Systems (Artifact)
Abstract
This paper presents Lotus@Runtime, an extensible tool that uses models@runtime to monitor and verify self-adaptive systems. The tool monitors the execution traces generated by a self-adaptive system and annotates the probabilities of occurrence of each system action on their respective transition on the system model, which is created at design time in the tool as a Labelled Transition System (LTS). Then, runtime checks of a set of reachability properties are performed against the updated probabilistic model. If a property is violated, the self-adaptive system can be informed by a notification mechanism provided by Lotus@Runtime. The applicability of the proposed tool has been demonstrated by two service-based self-adaptive systems taken and adapted from the literature.
Cite as
Davi Monteiro Barbosa, Rómulo Gadelha de Moura Lima, Paulo Henrique Mendes Maia, and Evilásio Costa Junior. Lotus@Runtime: A Tool for Runtime Monitoring and Verification of Self-adaptive Systems (Artifact). In Special Issue of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 1, pp. 7:1-7:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
Copy BibTex To Clipboard
@Article{monteirobarbosa_et_al:DARTS.3.1.7,
author = {Monteiro Barbosa, Davi and Gadelha de Moura Lima, Rómulo and Maia, Paulo Henrique Mendes and Junior, Evilásio Costa},
title = {{Lotus@Runtime: A Tool for Runtime Monitoring and Verification of Self-adaptive Systems (Artifact)}},
pages = {7:1--7:5},
journal = {Dagstuhl Artifacts Series},
ISSN = {2509-8195},
year = {2017},
volume = {3},
number = {1},
editor = {Monteiro Barbosa, Davi and Gadelha de Moura Lima, Rómulo and Maia, Paulo Henrique Mendes and Junior, Evilásio Costa},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.1.7},
URN = {urn:nbn:de:0030-drops-71454},
doi = {10.4230/DARTS.3.1.7},
annote = {Keywords: Self-adaptive systems; Runtime models; Runtime verification; Tool; Framework}
}