Robust processor allocation for independent tasks when dollar cost for processors is a constraint

Includes bibliographical references (pages 9-10).In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. Different classes of machines used in such systems typically vary in dollar cost based on their computing efficiencies. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized. Resource allocation is often done based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. The dollar cost to purchase the machines for use can be a constraint such that only a subset of the machines available can be purchased. The goal of this study is to: (1) select a subset of all the machines available so that the cost constraint for the machines is satisfied, and (2) find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Six heuristic techniques to this problem are presented and evaluated

Application Aware Overlay One-to-Many Data Dissemination Protocol for High-Bandwidth Sensor Actuator Network

An application-aware Deterministic Overlay One-to-Many (DOOM) protocol is proposed for meeting heterogeneous QoS requirements of multiple end users of High-Bandwidth Sensor Actuator Network (HB-SAN) applications. Although DOOM is initially targeted for use in collaborative adaptive systems of weather radars, it has been designed for use in wider class of sensing systems. DOOM protocol performs rate-based application aware congestion control by selecting end user specific subset of the sensor data for transmission thus adapting to available network infrastructure under dynamic network conditions. Performance of DOOM is evaluated for radar networking using a combination of Planetlab as well as an emulation based test-bed. It is shown that DOOM protocol is able to meet individual end user QoS requirements as well as aggregate QoS requirements of different end users. Moreover, multiple DOOM streams are friendly to each other as well as to TCP cross- traffic sharing the bottleneck link

Performance Evaluation of Application Aware . . .

Advances in networking have led to the emergence of high-bandwidth sensor actuator network applications. In many of these applications, it is required to transmit high bandwidth data to multiple end users while meeting their heterogeneous QoS requirements. An application aware Deterministic Overlay One-to-Many (DOOM) protocol is proposed, that concurrently meets the heterogeneous real-time rate and data framing requirements of multiple end users under dynamic network conditions. DOOM protocol supports application aware congestion control by performing dynamic selection of data for transmission at a given rate as per the end user requirements. Moreover, DOOM protoco

An Architecture and a Programming Interface for Application-Aware Data Dissemination Using Overlay Networks

Many real-time distributed collaborative applications are emerging that require exchange of critical sensor data among geographically distant end users under resource-constrained network conditions. The QoS requirements, e.g., required bandwidth, latency, acceptable data quality, and reliability are interdependent, and critical to the operation of these applications. This paper presents an AWON (Application-aWare Overlay Networks) architecture for deploying application-aware services in an overlay network to best meet the application requirements over the available overlay networking infrastructure. An application programming interface (API) is presented to facilitate development of applications within the AWON architectural framework. The API supports the configuration of overlay nodes for in-network, application-aware processing. Application-defined plug-in modules are used to deploy applicationspecific functionality at each overlay node. The API also enables communication between application and the overlay routing protocol for the desired QoS support. The effectiveness of the AWON architecture and the API is demonstrated for a real-time weather radar data dissemination application using planetlab. Experimental results show that AWON-based application-aware services significantly improve the quality of the content delivered to the end users in bandwidth-constrained conditions

Application-aware in-network service deployment for collaborative adaptive sensing of the atmosphere (CASA)

An Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) funded by the National Science Foundation, seeks to revolutionize the way we detect, monitor and predict atmospheric phenomena by creating a dense network of small, low-cost, low-power radars that could collaboratively and adaptively sense the lower atmosphere. Such a network is expected to provide more timely and accurate forecasts for tornadoes, flash floods, and other hazardous weathers. In addition, the networked radars can offer improved accuracies and more specific inferences that could not be achieved by the use of a single long-range radar. In CASA, multiple end users may be present that have distinct sensing, communication and computation requirements for their operations. In addition, the underlying network infrastructure may itself be subjected to adverse conditions due to severe weather and link degradation/outage along wired and wireless links. We use overlay networking to provide acceptable quality of service (QoS) and robust data transport service for the CASA end-users. At CSU, we have developed an AWON (Application-aWare Overlay Networks) architecture for deploying application-aware services in an overlay network to best meet the end-users' QoS requirements over the available networking infrastructure; based on this, we have implemented an application-aware multicast service for CASA. We also present a multi-sensor fusion framework which can provide a mechanism for selecting a set of data for data fusion considering application-specific needs, and a distributed processing scheme to minimize the execution time required for processing data per integration algorithm.This work was supported primarily by the Engineering Research Centers program of the National Science Foundation under NSF award number 0313747.3rd place, ISTeC Student Research Poster Contest (April 7, 2008)

Robust static allocation of resources for independent tasks under makespan and dollar cost constraints

Includes bibliographical references (pages 413-414).Heterogeneous computing (HC) systems composed of interconnected machines with varied computational capabilities often operate in environments where there may be inaccuracies in the estimation of task execution times. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that needs to be optimized in such systems. Resource allocation is typically performed based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. In this research, the problem of finding a static mapping of tasks to maximize the robustness of makespan against the errors in task execution time estimates given an overall makespan constraint is studied. Two variations of this basic problem are considered: (1) where there is a given, fixed set of machines, (2) where an HC system is to be constructed from a set of machines within a dollar cost constraint. Six heuristic techniques for each of these variations of the problem are presented and evaluated

Robust Processor Allocation for Independent Tasks When Dollar Cost for Processors is a Constraint

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. Different classes of machines used in such systems typically vary in dollar cost based on their computing efficiencies. Makespan (defined as the completion time for an entire set of tasks) is often the performance feature that is optimized. Resource allocation is often done based on estimates of the computation time of each task on each class of machines. Hence, it is important that makespan be robust against errors in computation time estimates. The dollar cost to purchase the machines for use can be a constraint such that only a subset of the machines available can be purchased. The goal of this study is to: (1) select a subset of all the machines available so that the cost constraint for the machines is satisfied, and (2) find a static mapping of tasks so that the robustness of the desired system feature, makespan, is maximized against the errors in task execution time estimates. Six heuristic techniques to this problem are presented and evaluated

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static allocation of resources for independent tasks under makespa