DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint ReasoningTechnicalBest Student Paper
The rapidly changing workload of service-based systems can easily cause under-/over-utilization on the component services, which can consequently affect the overall Quality of Service (QoS), such as latency. Self-adaptive services composition rectifies this problem, but poses several challenges: (i) the effectiveness of adaptation can deteriorate due to over-optimistic assumptions on the latency and utilization constraints, at both local and global levels; and (ii) the benefits brought by each composition plan is often short term and is not often designed for long-term benefits – a natural prerequisite for sustaining the system. To tackle these issues, we propose a two levels constraint reasoning framework for sustainable self-adaptive services composition, called DATESSO. In particular, DATESSO consists of a re ned formulation that differentiates the “strictness” for latency/utilization constraints in two levels. To strive for long-term benefits, DATESSO leverages the concept of technical debt and time-series prediction to model the utility contribution of the component services in the composition. The approach embeds a debt-aware two level constraint reasoning algorithm in DATESSO to improve the efficiency, effectiveness and sustainability of self-adaptive service composition. We evaluate DATESSO on a service-based system with real-world WS-DREAM dataset and comparing it with other state-of-the-art approaches. The results demonstrate the superiority of DATESSO over the others on the utilization, latency and running time whilst likely to be more sustainable.
Tue 30 Jun Times are displayed in time zone: (UTC) Coordinated Universal Time change
06:00 - 07:30: Session 2: Testing, Analysis, Reasoning, and MonitoringSEAMS 2020 at SEAMS Chair(s): Sona GhahremaniHasso Plattner Institute, University of Potsdam | |||
06:00 - 06:05 Talk | Leveraging Test Logs for Building a Self-Adaptive Path PlannerNIER SEAMS 2020 Kun LiuPeking University, China, Xiaoyi ZhangNational Institute of Informatics, Japan, Paolo ArcainiNational Institute of Informatics
, Fuyuki IshikawaNational Institute of Informatics, Wenpin JiaoPeking University, China Pre-print Media Attached | ||
06:05 - 06:10 Talk | Supporting Viewpoints to Review the Lack of Requirements in Space Systems with Machine LearningExperience SEAMS 2020 Kenji MoriJapan Aerospace Exploration Agency, Japan, Naoko OkuboJapan Aerospace Exploration Agency, Japan, Yasushi UedaJapan Aerospace Exploration Agency, Japan, Masafumi KatahiraJapan Aerospace Exploration Agency, Toshiyuki AmagasaUniversity of Tsukuba, Japan Media Attached | ||
06:10 - 06:15 Talk | DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint ReasoningTechnicalBest Student Paper SEAMS 2020 Satish KumarUniversity of Birmingham, United Kingdom, Tao ChenLoughborough University, Rami BahsoonUniversity of Birmingham, Rajkumar BuyyaUniversity of Melbourne, Australia DOI Pre-print Media Attached | ||
06:15 - 06:20 Talk | Towards Bridging the Gap between Control and Self-Adaptive System PropertiesNIER SEAMS 2020 Javier CamaraUniversity of York, Alessandro Vittorio PapadopoulosMälardalen University, Thomas VogelHumboldt-Universität zu Berlin, Danny WeynsKU Leuven, David GarlanCarnegie Mellon University, Shihong HuangFlorida Atlantic University, Kenji TeiWaseda University / National Institute of Informatics, Japan DOI Pre-print Media Attached | ||
06:20 - 06:25 Talk | Explanation for Human-on-the-loop: a probabilistic model checking approachNIER SEAMS 2020 NIANYU LIPeking University, China, Sridhar AdepuSingapore University of Technology and Design, Singapore, Eunsuk KangCarnegie Mellon University, David GarlanCarnegie Mellon University Pre-print Media Attached | ||
06:25 - 07:30 Other | Q&A and Discussion (Session 2) SEAMS 2020 |