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Qualitative classification of resource use, we have developed a suite of trained artificial neural networks and hidden Markov models
The Autopilot performance-directed adaptive control system
Future Generation Comp. Syst., no. 1 (2001): 175-187
High-performance computing is rapidly expanding to include distributed collections of heterogeneous sequential and parallel systems and irregular applications with complex, data dependent execution behavior and time-varying resource demands. To provide adaptive resource management for dynamic applications, we are developing the Autopilot ...More
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- The scope of high-performance computing is rapidly expanding from single parallel systems to distributed collections of heterogeneous sequential and parallel systems.
- Autopilot provides a flexible set of performance sensors, decision procedures, and policy actuators to realize adaptive control of applications and resource management policies on both parallel and wide area distributed systems.
- In Section 7, the authors describe flexible decision procedure mechanisms based on fuzzy logic, followed in Section 8 by a unified example of Autopilot’s application to adaptive input/output systems.
- The scope of high-performance computing is rapidly expanding from single parallel systems to distributed collections of heterogeneous sequential and parallel systems
- The performance sensitivity of current parallel and distributed systems is a direct consequence of resource interaction complexity and the failure to recognize that resource allocation and management must evolve with applications, becoming more flexible and resilient to changing resource availability and resource demands
- By integrating dynamic performance instrumentation and on-the-fly performance data reduction with configurable, malleable resource management algorithms and a real-time adaptive control mechanism, flexible runtime systems could automatically choose and configure resource management algorithms based on application request patterns and observed system performance
- Qualitative classification of resource use, we have developed a suite of trained artificial neural networks (ANNs) and hidden Markov models (HMMs) [10,12]
- Using the global access pattern classification, a file policy selection and configuration mechanism based on fuzzy logic might select a write back policy that merges file writes from each processor, forming larger, contiguous blocks for sequential write back to secondary storage devices
- As a complement to quantitative performance data, knowledge of qualitative application behavior, obtained from either user-written assertions or automatic classification techniques, is useful when dynamically choosing resource management policies.
- A set of fuzzy logic decision mechanisms that exploit real-time sensor inputs and behavioral classifications to dynamically select resource management policies;
- One can use the sensor infrastructure to capture real-time performance data streams and drive displays of system activity without use of either the decision procedure or actuator components.
- As an example of the latter, an attached function could process a sequence of file read/write offsets captured by a file input/output sensor and produce a qualitative classification of the file access pattern.
- Sensors provide the quantitative data needed to make resource policy management decisions, the experience with adaptive file system policy selection  has shown that qualitative data on current and future resource demands is an effective complement.
- New application programming interfaces (APIs) for high-performance input/output include interfaces for user specification of future file access patterns.
- Based on Nexus global pointers, sensors and actuators provide the mechanism needed to monitor and control both local and remote tasks.
- Fig. 7 illustrates a simple set of fuzzy logic rules that might be used to control file prefetching in an adaptive input/output system.
- As an example of the potential power of an adaptive parallel file system using the Autopilot infrastructure, consider a parallel application on P processors that first writes an N block data file using a strided access pattern.
- Using the global access pattern classification, a file policy selection and configuration mechanism based on fuzzy logic might select a write back policy that merges file writes from each processor, forming larger, contiguous blocks for sequential write back to secondary storage devices.
- Using performance data on disk queue lengths and response times, captured by real-time performance sensors, the fuzzy logic controls would use policy actuators to choose the write back policy and optimally choose the size of the write back units.
- ଝ This work was supported in part by the Defense Advanced Research Projects Agency under DARPA contracts DABT6394-C0049 (SIO Initiative), DAVT63-91-C-0029 and DABT63-93C-0040, F30602-96-C-0161, and DABT63-96-C-0027 by the National Science Foundation under grants NSF ASC 92-12369, NSF CDA 94-01124, and ASC 97-20202, and by the Department of Energy under contracts DOE B-341494, W-7405ENG-48, and 1-B-333164
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- Huseyin Simitci is an engineer at XIOtech Corporation, specializing in performance analysis of networked storage technologies. He received his PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2000. He obtained his MS and BS from Bilkent University, Ankara, Turkey in 1994 and 1992, respectively. His research interests include intelligent storage systems, high performance computing, and system performance analysis.