  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.
