Multi-Objective Local-Search Optimization using Reliability Importance Measuring

Abstract

In recent years, reliability has become a major issue and objective during the design of embedded systems. Here, different techniques to increase reliability like hardware-/softwarebased redundancy or component hardening are applied systematically during Design Space Exploration (DSE), aiming at achieving highest reliability at lowest possible cost. Existing approaches typically solely provide reliability measures, e. g. failure rate or Mean-Time-To-Failure (MTTF), to the optimization engine, poorly guiding the search which parts of the implementation to change. As a remedy, this work proposes an efficient approach that (a) determines the importance of resources with respect to the system's reliability and (b) employs this knowledge as part of a local search to guide the optimization engine which components/design decisions to investigate. First, we propose a novel approach to derive Importance Measures (IMs) using a structural evaluation of Success Trees (STs). Since ST-based reliability analysis is already used for MTTF calculation, our approach comes at almost no overhead. Second, we enrich the global DSE with a local search. Here, we propose strategies guided by the IMs that directly change and enhance the implementation. In our experimental setup, the available measures to enhance reliability are the selection of hardening levels during resource allocation and software-based redundancy during task binding; exemplarily, the proposed local search considers the selected hardening levels. The results show that the proposed method outperforms a state-of-the-art approach regarding optimization quality, particularly in the search for highly-reliable yet affordable implementationsat negligible runtime overhead.

Uncertainty-Aware Compositional System-Level Reliability Analysis

“…Later, to improve the reliability of a system with limited budgets, we only need to improve the reliability of highly important components. In [5,28], we show this guides the DSE! towards highly reliable, yet affordable implementations.…”

Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores

“…To our knowledge, this is the first work specialized for independent tasks in the design of fault-tolerant multicores, as all the previous works (Das et al 2014;Gan et al 2012;Khosravi et al 2014;Pop et al 2009; Stralen and Pimentel 2012) consider general task sets (e.g., there are data dependences between tasks). As mentioned above, task mapping for general task sets is a well-known NP-hard problem.…”

“…Since different fault-tolerant techniques are usually characterized by different time and space overheads, there is an optimization trade-off in task hardening, i.e., determining one of the fault-tolerant techniques (e.g., DMR or TMR) for each task. This trade-off, together with the traditional optimization in task mapping, i.e., mapping each task to one of the cores, makes the design of fault-tolerant multi-cores very challenging (Das et al 2014;Gan et al 2012;Khosravi et al 2014;Pop et al 2009;Stralen and Pimentel 2012). It is notable that replication-based task hardening will introduce new tasks, i.e., replicas, into the system, and these replicas should be mapped as well.…”

Toward Efficient Design Space Exploration for Fault-Tolerant Multiprocessor Systems

“…Given all these degrees of freedom, in order to obtain optimal designs of fault-tolerant multiprocessor systems, a variety of DSE methods have been proposed over the last decade, especially the ones based on evolutionary algorithms (EAs) [6]- [9]. As a population-based metaheuristic optimization algorithm, EAs often provide good near optimal solutions to many types of problems, but their high time complexity [22], [23] is a prohibiting factor in real-word applications. Besides, EAs usually do not involve any highlevel problem-specific knowledge beyond that required in fitness evaluation [24], [25].…”