3 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
Zone-based formal specification and timing analysis of real-time self-adaptive systems
Self-adaptive software systems are able to autonomously adapt their behavior at run-time to react to internal
dynamics and to uncertain and changing environment conditions. Formal specification and verification
of self-adaptive systems are tasks generally very difficult to carry out, especially when involving time constraints.
In this case, in fact, the system correctness depends also on the time associated with events.
This article introduces the Zone-based Time Basic Petri nets specification formalism. The formalism
adopts timed adaptation models to specify self-adaptive behavior with temporal constraints, and relies on
a zone-based modeling approach to support separation of concerns. Zones identified during the modeling
phase can be then used as modules either in isolation, to verify intra-zone properties, or all together, to verify
inter-zone properties over the entire system. In addition, the framework allows the verification of (timed)
robustness properties to guarantee self-healing capabilities when higher levels of reliability and availability
are required to the system, especially when dealing with time-critical systems. This article presents also
the ZAFETY tool, a Java software implementation of the proposed framework, and the validation and
experimental results obtained in modeling and verifying two time-critical self-adaptive systems: the Gas
Burner system and the Unmanned Aerial Vehicle system