Collective Value QoS: A Performance Measure Framework for Distributed Heterogeneous Networks

When users' tasks in a distributed heterogeneous computing environment are allocated resources, and the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service, receive no service at all, or may be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such an environment is the collective value of the tasks completed during an interval of time, as perceived by the user, the application, or the policy maker. For the case where a task may be a data communication request, the collective value of data communication requests that are satisfied during an interval of time is measured. The Flexible Integrated System Capability (FISC) measure defined here is one way of obtaining a multi-dimensional measure for quantifying this collective value. While the FISC measure itself is not sufficient for scheduling purposes, it can be a critical part of a scheduler or a scheduling heuristic. The primary contribution of this work is providing a way to measure the collective value accrued by an RMS using a broad range of attributes and to construct a flexible framework that can be extended for particular problem domains.DARPA/ITO Quorum ProgramDARPA/ISO BADD ProgramOffice of Naval Research under ONR grant number N00014-97-1-0804DARPA/ITO AICE program under contract numbers DABT63-99-C-0010 and DABT63-99-C-0012DARPA/ITO Quorum ProgramDARPA/ISO BADD ProgramOffice of Naval Research under ONR grant number N00014-97-1-0804DARPA/ITO AICE program under contract numbers DABT63-99-C-0010 and DABT63-99-C-0012Approved for public release; distribution is unlimited

Synthesizing variable instruction issue interpreters for implementing functional parallelism on SIMD computers

Abstract—Functional parallelism can be supported on SIMD machines by interpretation. Under such a scheme, the programs and data of each task are loaded on the processing elements (PEs) and the Control Unit of the machine executes a central control algorithm that causes the concurrent interpretation of the tasks on the PEs. The central control algorithm is, in many respects, analogous to the control store program on microprogrammed machines. Accordingly, the organization of the control algorithm greatly influences the performance of the synthesized MIMD environment. Most central control algorithms are constructed to interpret the execution phase of all instructions during every cycle (iteration). However, it is possible to delay the interpretation of infrequent and costly instructions to improve the overall performance. Interpreters that attempt improved performance by delaying the issue of infrequent instructions are referred to as variable issue control algorithms. This paper examines the construction of optimized variable issue control algorithms. In particular, a mathematical model for the interpretation process is built and two objective functions (instruction throughput and PE utilization) are defined. The problem of deriving variable issue control algorithms for these objective functions has been shown elsewhere to be NP-complete. Therefore, this paper investigates three heuristic algorithms for constructing near optimal variable issue control algorithms. The performance of the algorithms is studied on four different instruction sets and the trends of the schedulers with respect to the instruction sets and the objective functions are analyzed. Index Terms—MIMD on SIMD, interpretation, variable instruction issue, scheduling instruction execution, SIMD computers

The Concurrent Execution of Non-communicating Programs on SIMD Processors

This paper explores the use of SIMD (or SIMD-like) hardware to support the efficient interpretation of concurrent, non-communicating programs. This approach places compiled programs into the local memory space of each distinct processing element (PE). Within each PE, a local program counter is initialized and the instructions are interpreted in parallel across all of the PEs by control signals emanating from the central control unit. Initial experiments have been conducted with two distinct software architectures (MINTABs and MIPS R2000) on the MasPar MP-1 and two distinct applications (program mutation analysis and Monte Carlo simulation). While these experiments have shown only marginal performance improvement, it appears that with several minor hardware modifications, SIMD-like hardware can be constructed that will cost-effectively support both SIMD and MIMD processing

Collective Value QoS: A Performance Measure Framework for Distributed Heterogeneous Networks

When users' tasks in a distributed heterogeneous computing environment are allocated resources, and the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service, receive no service at all, or may be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such an environment is the collective value of the tasks completed during an interval of time, as perceived by the user, the application, or the policy maker. For the case where a task may be a data communication request, the collective value of data communication requests that are satisfied during an interval of time is measured. The Flexible Integrated System Capability (FISC) measure defined here is one way of obtaining a multi-dimensional measure for quantifying this collective value. While the FISC measure itself is not sufficient for scheduling purposes, it can be a critical part of a scheduler or a scheduling heuristic. The primary contribution of this work is providing a way to measure the collective value accrued by an RMS using a broad range of attributes and to construct a flexible framework that can be extended for particular problem domains.DARPA/ITO Quorum ProgramDARPA/ISO BADD ProgramOffice of Naval Research under ONR grant number N00014-97-1-0804DARPA/ITO AICE program under contract numbers DABT63-99-C-0010 and DABT63-99-C-0012DARPA/ITO Quorum ProgramDARPA/ISO BADD ProgramOffice of Naval Research under ONR grant number N00014-97-1-0804DARPA/ITO AICE program under contract numbers DABT63-99-C-0010 and DABT63-99-C-0012Approved for public release; distribution is unlimited

A Flexible Multi-Dimensional QoS Performance Measure Framework for Distributed Heterogeneous Systems

When users' tasks in a distributed heterogeneous computing environment (e.g.cluster of heterogeneous computers) are allocated resources, the total demand placed on some system resources by the tasks, for a given interval of time, may exceed the availability of those resources. In such a case, some tasks may receive degraded service or be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such a distributed environment is the collective value of the tasks completed during an interval of time, as perceived by the user, application, or policy maker. The Flexible Integrated System Capability (FISC) measure presented here is a measure for quantifying this collective value. The FISC measure is a flexible multidimensional measure, and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domain-specific QoS, and task dependencies. For an environment where it is important to investigate how well data communication requests are satisfied, the data communication request satisfied can be the basis of the FISC measure instead of tasks completed

Collective value of qos: A performance measure framework for distributed heterogeneous networks

When users ’ tasks in a distributed heterogeneous computing environment are allocated resources, and the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service, receive no service at all, or may be dropped from the system. One part of a measure to quantify the success of a resource management system (RMS) in such an environment is the collective value of the tasks completed during an interval of time, as perceived by the user, the application, or the policy maker. For the case where a task may be a data communication request, the collective value of data communication requests that are satisfied during an interval of time is measured. The Flexible Integrated System Capability (FISC) measure defined here is one way of obtaining a multi-dimensional measure for quantifying this collective value. While the FISC measure itself is not sufficient for scheduling purposes, it can be a critical part of a scheduler or a scheduling heuristic. The primary contribution of this work is providing a way to measure the collective value accrued by an RMS using a broad range of attributes and t

An Overview of the Management System for Heterogeneous Networks (MSHN) The Management System for Heterogeneous Networks

The Management System for Heterogeneous Networks (MSHN) is a resource management system for use in heterogeneous environments. This paper describes the goals of MSHN, its architecture, and both completed and ongoing research experiments. MSHN's main goal is to determine the best way to support the execution of many different applications, each with its own quality of service (QoS) requirements, in a distributed, heterogeneous environment. MSHN's architecture consists of seven distributed, potentially replicated components that communicate with one another using CORBA (Common Object Request Broker Architecture). MSHN's experimental investigations include: (1) the accurate, transparent determination of the end-to-end status of resources; (2) the identification of optimization criteria and how non-determinism and the granularity of models affect the performance of various scheduling heuristics that optimize those criteria; (3) the determination of how security should be incorporated between components as well as how to account for security as a QoS attribute; and (4) the identification of problems inherent in application and system characterization.Approved for public release; distribution is unlimited

A QoS Performance Measure Framework

In a distributed heterogeneous computing environment, users' tasks are allocated resources to simultaneously satisfy, to varying degrees, the tasks' different, and possibly conflicting, quality of service (QoS) requirements. When the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service or no service at all. One part of a measure to quantify the success of a resource management system (RMS) in such a distributed environment is the collective value of the tasks completed during an interval of time, as perceived by the user, application, or policy maker. The Flexible Integrated System Capability (FISC) ratio introduced here is a measure for quantifying this collective value. The FISC ratio is a multi-dimensional measure, and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domainspecific QoS, and dependencies. In addition to being used for evaluating and comparing RMSs, the FISC ratio can be incorporated as part of the objective function in a system's scheduling heuristics