Self-Adaptive Systems Artifacts and Model Problems
This site contains a set of exemplars for self-adaptive systems. An exemplar can be quite generic such as an artifact or rather specific such as a model problem in self-adaptive systems. The goal is to provide a repository of examples, challenge problems, and solutions that the software engineering for self-adaptive systems community can use to motivate research, exhibit and evaluate solutions and techniques, and compare results. Artifacts of interest include but are not limited to:
- Testbeds / Model Problems / Managed Systems, which are implementations or detailed specifications of systems that pose and highlight fundamental or characteristic challenges that self-adaptive systems should address.
- Datasets, which are data (e.g., logging data, sensor data, system traces, survey raw data) that can be used to develop, evaluate, and compare self-adaptation approaches.
- Frameworks, which are tools and services illustrating and implementing self-adaptation techniques or algorithms that are potentially useful in different contexts and that other researchers could use and customize to specific contexts.
Thus, frameworks primarily support developing self-adaptation approaches whereas testbeds / model problems / managed systems and datasets particularly support evaluating and comparing different approaches. However, this list is not exhaustive.
The intent is for this site to be an extensible resource that will grow over time through contributions from the community, especially to the Artifacts Track of the annual SEAMS symposium.
Current ExemplarsThe collection currently contains 27 exemplars.
Use the search and sorting by columns to navigate through the table.
To obtain details of an exemplar, click on its name.
|Name||Full Name||Short Description||Accepted at Conference||Domain|
|Znn.com||Znn.com||A webserver system providing a simplified news site. The testing environment simulates the slash-dot effect which are periods of abnormally high traffic that overload the system.||Web / Cloud / Service|
|ATRP||Automated Traffic Routing Problem||ATRP is an autonomous vehicle routing scenario. Vehicles, each with personal goals, attempt to travel around a map while competing for resources and handling noisy and partial views of the world and privacy concerns.||SEAMS 2012||Autonomous Vehicles / Robotics|
|Hogna||A Platform for Self-Adaptive Applications in Cloud Environments||Deploying and managing autonomic applications in cloud is a time consuming operation, that require many components to work together. The management will need to extract metrics from the deployed system, analyze them and the make a decision for changes that need to be implemented. Usually, a researcher's work is focused in only one component (investigating different strategies for adaptation, evaluating the impact of various metrics, etc.), while the rest must just work, without the researcher having to spend too much time on them.||SEAMS 2015||Web / Cloud / Service|
|DEECo||Distributed Dependable Ensembles of Components||To develop self-adaptive cyber-physical systems (CPS) we advocate the use of component-based abstractions and related tools. DEECo is a component system (model and runtime platform) that provides the architecture abstractions of autonomous components and dynamic component groups (called ensembles) on top of which different adaptation techniques can be deployed.||SEAMS 2015||Cyber-physical Systems / IoT|
|TAS||Tele Assistance System||TAS is an exemplar of a service-based system (SBS). SBSs are widely used in e-commerce, online banking, e-health and many other applications. In these systems, services offered by third-party providers are dynamically composed into workflows delivering complex functionality. SBSs increasingly rely on self-adaptation to cope with the uncertainties associated with third-party services, as the loose coupling of services makes online reconfiguration feasible.||SEAMS 2015||Web / Cloud / Service|
|FmFM||Feed me, Feed me||FmFm is an exemplar for engineering adaptive software. It is an IoT-based ecosystem to support food security; that is to ensure sufficient, safe, and nutritious food to the global population. Particularly, it describes four scenarios to experiment and evaluate self-adaptation techniques for the Internet of Things.||SEAMS 2016||Cyber-physical Systems / IoT|
|Hadoop-Benchmark||Rapid Prototyping and Evaluation of Self-Adaptive Behaviors in Hadoop Clusters||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.||SEAMS 2017||Web / Cloud / Service|
|Self-Adaptive Video Encoder||Comparison of Multiple Adaptation Strategies Made Simple||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.||SEAMS 2017||Other|
|UNDERSEA||An Exemplar for Engineering Self-Adaptive Unmanned Underwater Vehicles||UNDERSEA facilitates the development, evaluation and comparison of self-adaptation solutions in the new and important application domain of unmanned underwater vehicles (UUVs). It comes with predefined oceanic surveillance UUV missions, adaptation scenarios, and a reference controller implementation, all of which can easily be extended or replaced.||SEAMS 2017||Autonomous Vehicles / Robotics|
|DeltaIoT||A Real World Exemplar for Self-Adaptive Internet of Things||The DeltaIoT exemplar enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in Internet of Things (IoT). It applies multi-hop communication, where each IoT mote must have a path towards the gateway along other motes. The focus is on dynamically adapting the network settings of the IoT motes. The exemplar provides several reference scenarios for experimentation and comprises a simulator for offline experimentation and a physical setup of 25 motes that can be accessed remotely for experimentation in the field.||SEAMS 2017||Cyber-physical Systems / IoT|
|CrowdNav and RTX||Model Problem (CrowdNav) and Framework (RTX) for Self-Adaptation Based on Big Data Analytics||This artifact provides 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. It also provides 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.||SEAMS 2017||Web / Cloud / Service|
|Intelligent Ensembles||A Declarative Group Description Language and Java Framework||Smart cyber-physical systems (sCPS) typically operate in a highly uncertain and dynamically changing environment where the ability to cooperate and adapt in groups to cope with various situations becomes a crucial and challenging task. This framework consists 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.||SEAMS 2017||Cyber-physical Systems / IoT|
|Lotus@Runtime||A Tool for Runtime Monitoring and Verification of Self-adaptive Systems||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. Then, runtime checks of a set of reachability properties are performed against the updated probabilistic model.||SEAMS 2017||Other|
|mRUBiS||An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization||An extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform model-based self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation.||SEAMS 2018||Web / Cloud / Service|
|K8-Scalar||A workbench to compare autoscalers for container-orchestrated database clusters||An easy-to-use and extensible workbench exemplar, which allows researchers to implement and evaluate different self-adaptive approaches to autoscaling container-orchestrated services. The workbench is based on Docker, relies on the container orchestration framework Kubernetes (K8s), and integrates and extends Scalar, a generic testbed for evaluating the scalability of large-scale systems with support for evaluating the performance of autoscalers for database clusters.||SEAMS 2018||Web / Cloud / Service|
|SWIM||An Exemplar for Evaluation and Comparison of Self-Adaptation Approaches for Web Applications||An exemplar that simulates a web application and that can be used as a target system with an external adaptation manager interacting with it through its TCP-based interface. An experiment using a simulated 60-server cluster, processing 18 hours of traffic with 29 million requests takes only 5 minutes to run on a laptop computer. SWIM has been used for evaluating self-adaptation approaches, and for a comparative study of model-based predictive approaches to self-adaptation.||SEAMS 2018|
(Best Artifact Award)
|Web / Cloud / Service|
|TRAPP||TRAPPed in Traffic? A Self-Adaptive Framework for Decentralized Traffic Optimization||Optimizing the traffic flow in a city is a challenging problem, especially in a future traffic system of self-driving cars. This is due to the interactions between the individual traffic agents (vehicles) who compete for the use of the common infrastructure (streets) given traffic dynamics such as stop-and-go effects, changing lanes, and other. The goal is to provide a solution to the above problem that works in a fully decentralized and participatory way.||SEAMS 2019||Autonomous Vehicles / Robotics|
|PiStarGODA-MDP||A Goal-Oriented Framework to Support Assurances Provision||Goal-Oriented Requirements Engineering (GORE) offers proved means to decompose requirements into well-defined entities (goals) and reason about the alternatives to meet them. PistarGODA-MDP is a goal-oriented framework to model self-adaptive systems under different classes of uncertainty (system itself, system’s goals, and environment), and to automatically generate a Markov Decision Process (MDP) model in PRISM language, and reliability and cost parametric formulae of the corresponding system.||SEAMS 2019||Other|
|OCCI Monitoring||OCCI-compliant, fully causal-connected architecture runtime models supporting sensor management||The Open Cloud Computing Interface (OCCI) specification describes a service provider independent application programming interface for the management of heterogeneous cloud resources. With the presented OCCI monitoring extension, it is possible to additionally manage the deployment and configuration of monitoring sensors in the cloud. It enables the representation of the sensors as well as their monitoring results in an OCCI-compliant runtime model.||SEAMS 2019||Web / Cloud / Service|
|DingNet||A Self-Adaptive Internet-of-Things Exemplar||A reference implementation for research on self-adaptation in the domain of IoT. DingNet offers a simulator that maps directly to a physical IoT system that is deployed in the area of Leuven, Belgium. DingNet models a set of geographically distributed gateways that are connected to a user application deployed at a front-end server. The gateways can interact over a LoRaWAN network with local, possibly mobile motes that can be equipped with sensors and actuators.||SEAMS 2019||Cyber-physical Systems / IoT|
|Dragonfly||A Tool for Simulating Self-Adaptive Drone Behaviours||Systems-of-systems are formed by the composition of independently created components into a single system. Such components are designed to satisfy their own requirements, and may not satisfy the overall requirements of the system-of-systems. We refer to components that cannot be adapted to meet both individual and global requirements as “defiant” components. We propose a “cautious” adaptation approach that supports changing the behaviour of such defiant components.||SEAMS 2019||Autonomous Vehicles / Robotics|
|DARTSim||An Exemplar for Evaluation and Comparison of Self-Adaptation Approaches for Smart Cyber-Physical Systems||In cyber-physical systems, self-adaptation approaches face particular challenges, incl. (i) environment monitoring that is subject to sensing errors; (ii) adaptation actions that take time; (iii) dire consequences for not adapting in a timely manner; and (iv) incomparable objectives that cannot be conflated into a single utility metric. To enable researchers to evaluate and compare self-adaptation approaches aiming to address these unique challenges, the DARTSim exemplar can be used.||SEAMS 2019|
(Best Artifact Award)
|Autonomous Vehicles / Robotics|
|AMELIA||Analyzable ModELs Inference from trAjectories||A technique to infer analyzable models from general trajectories of spatially-distributed systems, which may be used for engineering analysis or planning facilities for the overall self-adaptive systems. Such spatial models are used for evaluation of requirements predicating about the structure of space, the spatial distribution of devices, temporal as well as quantitative aspects through formal spatio-temporal verification.||SEAMS 2019||Cyber-physical Systems / IoT|
|Platooning LEGOs||An Open Physical Exemplar for Engineering Self-Adaptive Cyber-Physical Systems-of-Systems||A physical exemplar of an industrial cyber-physical systems of systems (CPSoS), called Platooning LEGOs, which employs platooning technology that is actively being developed by the autonomous driving industry. A platoon, in which independent vehicles drive together, achieves SoS-level goals through adaptive behavioral decisions of the vehicles. This exemplar provides a physical experimental environment that can be implemented with LEGOs.||SEAMS 2021 (Best Artifact Award)||Cyber-physical Systems / IoT|
|Body Sensor Network||A Self-Adaptive System Exemplar in the Healthcare Domain||An exemplar in the healthcare domain. It is a sensor network to explore the rather dynamic patient's health status monitoring, and is focused on self-adaptation and comes with scenarios that hinder an interplay between system reliability and battery consumption.||SEAMS 2021||Cyber-physical Systems / IoT|
|RoboMAX||Robotic Mission Adaptation eXemplars||An extensible repository of robotic mission adaptation exemplars. Co-designed with robotic application stakeholders including researchers, developers, operators, and end-users, our repository captures key sources of uncertainty, adaptation concerns, and other distinguishing characteristics of such applications.||SEAMS 2021||Autonomous Vehicles / Robotics|
|RDMSim||An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation||RDMSim enables researchers to evaluate and compare techniques for decision-making under environmental uncertainty that support self-adaptation. The focus of the exemplar is on the domain problem related to Remote Data Mirroring. RDMSim provides probe and effector components for easy integration with external adaptation managers, which are associated with decision-making techniques and based on the MAPE-K loop.||SEAMS 2021||Web / Cloud / Service|