Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines

Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information.Comment: Published and presented in the 11th IEEE International Conference and Workshops on Engineering of Autonomic and Autonomous Systems (EASe 2014

Time-Space Efficient Regression Testing for Configurable Systems

Configurable systems are those that can be adapted from a set of options. They are prevalent and testing them is important and challenging. Existing approaches for testing configurable systems are either unsound (i.e., they can miss fault-revealing configurations) or do not scale. This paper proposes EvoSPLat, a regression testing technique for configurable systems. EvoSPLat builds on our previously-developed technique, SPLat, which explores all dynamically reachable configurations from a test. EvoSPLat is tuned for two scenarios of use in regression testing: Regression Configuration Selection (RCS) and Regression Test Selection (RTS). EvoSPLat for RCS prunes configurations (not tests) that are not impacted by changes whereas EvoSPLat for RTS prunes tests (not configurations) which are not impacted by changes. Handling both scenarios in the context of evolution is important. Experimental results show that EvoSPLat is promising. We observed a substantial reduction in time (22%) and in the number of configurations (45%) for configurable Java programs. In a case study on a large real-world configurable system (GCC), EvoSPLat reduced 35% of the running time. Comparing EvoSPLat with sampling techniques, 2-wise was the most efficient technique, but it missed two bugs whereas EvoSPLat detected all bugs four times faster than 6-wise, on average.Comment: 14 page

Software-based and regionally-oriented traffic management in Networks-on-Chip

Since the introduction of chip-multiprocessor systems, the number of integrated cores has been steady growing and workload applications have been adapted to exploit the increasing parallelism. This changed the importance of efficient on-chip communication significantly and the infrastructure has to keep step with these new requirements. The work at hand makes significant contributions to the state-of-the-art of the latest generation of such solutions, called Networks-on-Chip, to improve the performance, reliability, and flexible management of these on-chip infrastructures

Feature-relative real-time obstacle avoidance and mapping

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaves 59-60).A substantial challenge in robotics is integration of complex software systems for real- time performance. This thesis integrates the robust and generic mapping framework Atlas, a feature-based local Simultaneous Localization and Mapping (SLAM) module, and obstacle avoidance using information from mapped features. The resulting system performs autonomous feature-relative Real-time Obstacle Avoidance and Mapping (ROAM) with laser or sonar range sensors, and results are shown for wide-beam sonar. This system will allow high-speed feature-relative obstacle and avoidance and navigation on mobile robots with wide-beam sonar and/or laser sensors.by Jacques Chadwick Leedekerken.M.Eng.and S.B

The "MIND" Scalable PIM Architecture

MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND architecture

Embedded Social Insect-Inspired Intelligence Networks for System-level Runtime Management

Large-scale distributed computing architectures such as, e.g. systems on chip or many-core devices, offer advantages over monolithic or centralised single-core systems in terms of speed, power/thermal performance and fault tolerance. However, these are not implicit properties of such systems and runtime management at software or hardware level is required to unlock these features. Biological systems naturally present such properties and are also adaptive and scalable. To consider how these can be similarly achieved in hardware may be beneficial. We present Social Insect behaviours as a suitable model for enabling autonomous runtime management (RTM) in many-core architectures. The emergent properties sought to establish are self-organisation of task mapping and systemlevel fault tolerance. For example, large social insect colonies accomplish a wide range of tasks to build and maintain the colony. Many thousands of individuals, each possessing relatively little intelligence, contribute without any centralised control. Hence, it would seem that social insects have evolved a scalable approach to task allocation, load balancing and robustness that can be applied to large many-core computing systems. Based on this, a self-optimising and adaptive, yet fundamentally scalable, design approach for many-core systems based on the emergent behaviours of social-insect colonies are developed. Experiments capture decision-making processes of each colony member to exhibit such high-level behaviours and embed these decision engines within the routers of the many-core system