DATESSO: Self-Adapting Service Composition with Debt-Aware Two Levels Constraint Reasoning
This addresses the challenge of maintaining QoS in service-based systems under dynamic workloads, offering a more sustainable adaptation method, though it appears incremental as it builds on existing constraint reasoning with added debt-awareness.
The paper tackles the problem of self-adaptive service composition in service-based systems, where changing workloads cause under-/over-utilization affecting QoS like latency, by proposing DATESSO, a two-level constraint reasoning framework that incorporates technical debt and time-series prediction for long-term benefits. The results show DATESSO outperforms state-of-the-art approaches in utilization, latency, and running time, with improved sustainability.
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.