Automated control of multiple software goals using multiple actuators

Abstract

Modern software should satisfy multiple goals simultaneously: it should provide predictable performance, be robust to failures, handle peak loads and deal seamlessly with unexpected conditions and changes in the execution environment. For this to happen, software designs should account for the possibility of runtime changes and provide formal guarantees of the software's behavior. Control theory is one of the possible design drivers for runtime adaptation, but adopting control theoretic principles often requires additional, specialized knowledge. To overcome this limitation, automated methodologies have been proposed to extract the necessary information from experimental data and design a control system for runtime adaptation. These proposals, however, only process one goal at a time, creating a chain of controllers. In this paper, we propose and evaluate the first automated strategy that takes into account multiple goals without separating them into multiple control strategies. Avoiding the separation allows us to tackle a larger class of problems and provide stronger guarantees. We test our methodology's generality with three case studies that demonstrate its broad applicability in meeting performance, reliability, quality, security, and energy goals despite environmental or requirements changes.

Testing self-adaptive software with probabilistic guarantees on performance metrics

“…the baseline. This is consistent with the adaptation objectives stated in the design of the strategy, where the size compression was considered the main objective [43]. The Integral adaptation achieves the complementary result with respect to the MPC strategy.…”

“…The presence of different service providers and variety of potential needs for each request introduces the need to adapt the software behaviour to the current operating conditions. Adaptation strategies were introduced with the aim of selecting given services based on properties to be enforced for the overall system, e.g., [14,24,43,60]. In our experiments, the adaptation strategy should recognise the service providers with higher service rates and prioritise them when distributing the requests.…”

“…It solves a model-based optimisation problem for each frame and uses the result to determine the encoding parameters for the next frame. For our tests, we used the tuning parameters from the original publication [43]. • Integral: we developed an heuristic adaptation strategy, inspired by control theory principles.…”

Self-Adaptation of Software Using Automatically Generated Control-Theoretical Solutions

“…In this approach, the controller acts based on the current feedback from the software, but uses the model of its own behavior to predict the software evolution. The fully automated MPC-based approach is called Automated Multi-objective Control of Software with Multiple Actuators (AMOCS-MA) [36].…”

Caloree

“…CALOREE łprotectsž that single application from interference by other applications. In future work, we will investigate applying CALOREE to competing applications by extending prior control work that addresses competing application needs while assuming fully accurate models of all applications that might run together [48]. We compare CALOREE and HBM reacting to input variations.…”