SEEC: A Framework for Self-aware Management of Multicore Resources

This paper presents SEEC, a self-aware programming model, designed to reduce programming effort in modern multicore systems. In the SEEC model, application programmers specify application goals and progress, while systems programmers separately specify actions system software and hardware can take to affect an application (e.g. resource allocation). The SEEC runtime monitors applications and dynamically selects actions to meet application goals optimally (e.g. meeting performance while minimizing power consumption). The SEEC runtime optimizes system behavior for the application rather than requiring the application programmer to optimize for the system. This paper presents a detailed discussion of the SEEC model and runtime as well as several case studies demonstrating their benefits. SEEC is shown to optimize performance per Watt for a video encoder, find optimal resource allocation for an application with complex resource usage, and maintain the goals of multiple applications in the face of environmental fluctuations

SEEC: A Framework for Self-aware Computing

As the complexity of computing systems increases, application programmers must be experts in their application domain and have the systems knowledge required to address the problems that arise from parallelism, power, energy, and reliability concerns. One approach to relieving this burden is to make use of self-aware computing systems, which automatically adjust their behavior to help applications achieve their goals. This paper presents the SEEC framework, a unified computational model designed to enable self-aware computing in both applications and system software. In the SEEC model, applications specify goals, system software specifies possible actions, and the SEEC framework is responsible for deciding how to use the available actions to meet the application-specified goals. The SEEC framework is built around a general and extensible control system which provides predictable behavior and allows SEEC to make decisions that achieve goals while optimizing resource utilization. To demonstrate the applicability of the SEEC framework, this paper presents fivedifferent self-aware systems built using SEEC. Case studies demonstrate how these systems can control the performance of the PARSEC benchmarks, optimize performance per Watt for a video encoder, and respond to unexpected changes in the underlying environment. In general these studies demonstrate that systems built using the SEEC framework are goal-oriented, predictable, adaptive, and extensible

Framework for self-aware management of goals and constraints in computing systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 165-172).Modern computing systems require applications to balance competing goals, e.g.,high performance and low power or high performance and high precision. Achieving the right balance for a particular application and system places an unrealistic burden on application programmers who must understand the power, performance, and precision implications of a variety of application and system configurations (e.g.,changing algorithms or allocating cores). To address this problem, we propose the Self-aware Computing framework, or SEEC. SEEC automatically and dynamically configures systems and applications to meet goals accurately and efficiently. While other self-aware implementations have been proposed, SEEC is uniquely distinguished by its decoupled approach, which allows application and systems programmers to separately specify goals and configurations, each according to their expertise. SEEC's runtime decision engine observes and configures the system automatically, reducing programmer burden. This general and extensible decision engine employs both control theory and machine learning to reason about previously unseen applications and system configurations while automatically adapting to changes in both application and system behavior. This thesis describes the SEEC framework and evaluates it in several case studies. SEEC is evaluated by implementing its interfaces and runtime system on multiple, modern Linux x86 servers. Applications are then instrumented to emit goals and progress, while system services are instrumented to describe available adaptations. The SEEC runtime decision engine is then evaluated for its ability to meet goals accurately and efficiently. For example, SEEC is shown to meet performance goals with less than 3% average error while bringing average power consumption within 92% of optimal. SEEC is also shown to meet power goals with less than 2% average error while achieving over 96% of optimal performance on average. Additional studies show SEEC reacting to maintain performance in response to unexpected events including fluctuations in application workload and reduction in available resources. These studies demonstrate that SEEC can have a positive impact on real systems by understanding high level goals and adapting to meet those goals online.by Henry Hoffmann.Ph.D