Self-Aware Monitoring Based Adaptive Systems

Monitoring based computer system should be self-aware first because self-aware computer systems will be capable of adjusting and adapting their behavior and resources thousands of times in a second automatically to find the best way for the achievement of an assigned goal. Such an ability can benefits a wide range of computer systems from embedded systems to supercomputers and is especially helpful for meeting power, performance, and resource-metering challenges in monitoring and many other fields like mobile computing, cloud computing, multicore computing and parallel operating systems.Here a framework is being proposed, called Application Pulse-rate that offers a general, standardized approach for applications to observe their performance and provides that information to external observers. Through a deep view of a self-optimizing synchronization library called Smartlocks, a controlling technique can be determined that systems use to decide which optimization actions should be taken for monitoring of environment

Physically-Adaptive Computing via Introspection and Self-Optimization in Reconfigurable Systems.

Digital electronic systems typically must compute precise and deterministic results, but in principle have flexibility in how they compute. Despite the potential flexibility, the overriding paradigm for more than 50 years has been based on fixed, non-adaptive inte-grated circuits. This one-size-fits-all approach is rapidly losing effectiveness now that technology is advancing into the nanoscale. Physical variation and uncertainty in com-ponent behavior are emerging as fundamental constraints and leading to increasingly sub-optimal fault rates, power consumption, chip costs, and lifetimes. This dissertation pro-poses methods of physically-adaptive computing (PAC), in which reconfigurable elec-tronic systems sense and learn their own physical parameters and adapt with fine granu-larity in the field, leading to higher reliability and efficiency. We formulate the PAC problem and provide a conceptual framework built around two major themes: introspection and self-optimization. We investigate how systems can efficiently acquire useful information about their physical state and related parameters, and how systems can feasibly re-implement their designs on-the-fly using the information learned. We study the role not only of self-adaptation—where the above two tasks are performed by an adaptive system itself—but also of assisted adaptation using a remote server or peer. We introduce low-cost methods for sensing regional variations in a system, including a flexible, ultra-compact sensor that can be embedded in an application and implemented on field-programmable gate arrays (FPGAs). An array of such sensors, with only 1% to-tal overhead, can be employed to gain useful information about circuit delays, voltage noise, and even leakage variations. We present complementary methods of regional self-optimization, such as finding a design alternative that best fits a given system region. We propose a novel approach to characterizing local, uncorrelated variations. Through in-system emulation of noise, previously hidden variations in transient fault sus-ceptibility are uncovered. Correspondingly, we demonstrate practical methods of self-optimization, such as local re-placement, informed by the introspection data. Forms of physically-adaptive computing are strongly needed in areas such as com-munications infrastructure, data centers, and space systems. This dissertation contributes practical methods for improving PAC costs and benefits, and promotes a vision of re-sourceful, dependable digital systems at unimaginably-fine physical scales.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78922/1/kzick_1.pd

Engineering self-awareness with knowledge management in dynamic systems: a case for volunteer computing

The complexity of the modem dynamic computing systems has motivated software engineering researchers to explore new sources of inspiration for equipping such systems with autonomic behaviours. Self-awareness has recently gained considerable attention as a prominent property for enriching the self-adaptation capabilities in systems operating in dynamic, heterogeneous and open environments. This thesis investigates the role of knowledge and its dynamic management in realising various levels of self-awareness for enabling self­adaptivity with different capabilities and strengths. The thesis develops a novel multi-level dynamic knowledge management approach for managing and representing the evolving knowledge. The approach is able to acquire 'richer' knowledge about the system's internal state and its environment in addition to managing the trade-offs arising from the adaptation conflicting goals. The thesis draws on a case from the volunteer computing, as an environment characterised by openness, heterogeneity, dynamism, and unpredictability to develop and evaluate the approach. This thesis takes an experimental approach to evaluate the effectiveness of the of the dynamic knowledge management approach. The results show the added value of the approach to the self-adaptivity of the system compared to classic self­adaptation capabilities

Enabling technologies for self-aware adaptive systems

Self-aware computer systems will be capable of adapting their behavior and resources thousands of times a second to automatically find the best way to accomplish a given goal despite changing environmental conditions and demands. Such a capability benefits a broad spectrum of computer systems from embedded systems to supercomputers and is particularly useful for meeting power, performance, and resource-metering challenges in mobile computing, cloud computing, multicore computing, adaptive and dynamic compilation environments, and parallel operating systems. Some of the challenges in implementing self-aware systems are a) knowing within the system what the goals of applications are and if they are meeting them, b) deciding what actions to take to help applications meet their goals, and c) developing standard techniques that generalize and can be applied to a broad range of self-aware systems. This work presents our vision for self-aware adaptive systems and proposes enabling technologies to address these three challenges. We describe a framework called Application Heartbeats that provides a general, standardized way for applications to monitor their performance and make that information available to external observers. Then, through a study of a self-optimizing synchronization library called Smartlocks, we demonstrate a powerful technique that systems can use to determine which optimization actions to take. We show that Heartbeats can be applied naturally in the context of reinforcement learning optimization strategies as a reward signal and that, using such a strategy, Smartlocks are able to significantly improve performance of applications on an important emerging class of multicore systems called asymmetric multicores.Roberto Rocca Foundatio