DATESSO

What is it about?

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 refined 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.

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