2 research outputs found
DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint Reasoning
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.Comment: Accepted to the SEAMS '20. Please use the following citation: Satish
Kumar, Tao Chen, Rami Bahsoon, and Rajkumar Buyya. DATESSO: Self-Adapting
Service Composition with Debt-Aware Two Levels Constraint Reasoning. In
IEEE/ACM 15th International Symposium on Software Engineering for Adaptive
and Self-Managing Systems, Oct 7-8, 2020, Seoul, Kore
Technical debt-aware and evolutionary adaptation for service composition in SaaS clouds
The advantages of composing and delivering software applications in the Cloud-Based Software as a Service (SaaS) model are offering cost-effective solutions with minimal resource management. However, several functionally-equivalent web services with diverse Quality of Service (QoS) values have emerged in the SaaS cloud, and the tenant-specific requirements tend to lead the difficulties to select the suitable web services for composing the software application. Moreover, given the changing workload from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilisation and over-utilisation on the component services that affects the service revenue and violates the service level agreement respectively. All those bring challenging decision-making tasks: (i) when to recompose the composite service? (ii) how to select new component services for the composition that maximise the service utility over time? at the same time, low operation cost of the service composition is desirable in the SaaS cloud. In this context, this thesis contributes an economic-driven service composition framework to address the above challenges. The framework takes advantage of the principal of technical debt- a well-known software engineering concept, evolutionary algorithm and time-series forecasting method to predictively handle the service provider constraints and SaaS dynamics for creating added values in the service composition. We emulate the SaaS environment setting for conducting several experiments using an e-commerce system, realistic datasets and workload trace. Further, we evaluate the framework by comparing it with other state-of-the-art approaches based on diverse quality metrics