Measuring the Quality of an Artificial Hormone System based Task Mapping

Brinkschulte, Uwe; Renteln, Alexander von; Pacher, Mathias

doi:10.4108/icst.autonomics2008.4662

Cited by 14 publications

(8 citation statements)

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“…Initial task allocation is found by exchanging different hormone signals and optimization is done by periodic re-allocation They also show how to guarantee an upper bound for self-configuration time, which is beneficial for real-time applications. They derive a quality measure which includes communication distance, CPU share and suitability of the PE to task mapping; their results showed an average improvement of 10% over the simple load balancing technique on a 4x4 network [18]. Mudry et al [10] present a fully distributed task allocation approach inspired by the healing and growth process found in bioorganisms.…”

Section: B Dynamic Task Remappingmentioning

confidence: 98%

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Mendis

Indrusiak

Audsley

2015

2015 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia)

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Centralised management of distributed systems require a significant amount of monitoring traffic to maintain an accurate view of the system global state. The communication overhead of these systems becomes a bottleneck as the number of processing elements in the network and workload increase. State-of-the art in decentralised resource management techniques address this issue by allowing individual or clusters of nodes to make decisions at runtime to manage the dynamic workload. The primary contribution of this paper is using a bio-inspired, distributed, task remapping technique to manage dynamic multiple video stream decoding workloads. Our proposed technique has a low-communication overhead and is used to reduce the cumulative job lateness of the video streams. Secondary contributions include, several improvements to an existing clusterbased resource management approach to introduce awareness of task blocking and relocation distance. We evaluate these two remapping methods by comparing the improvement of job lateness, communication overhead and distribution of utilisation via simulation of several workload patterns.

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Section: B Dynamic Task Remappingmentioning

confidence: 98%

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Mendis

Indrusiak

Audsley

2015

2015 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia)

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“…For this reason, we introduced the concept of an artificial hormone system (AHS), which can map tasks onto a grid of heterogeneous processing elements [1,2,5,39]. The failing of a single resource may not lead to a system crash, and real-time constraints may not be hurt by such a failure.…”

Section: Introductionmentioning

confidence: 99%

Implementation and evaluation of a self‐organizing artificial hormone system to assign time‐dependent tasks

Pacher

Brinkschulte

2011

Concurrency and Computation

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We present a new method to assign tasks, which are connected by time dependencies within a heterogeneous environment, (e.g., several resources communicating with each other). Our approach for task distribution uses an artificial hormone system, which is self-organizing and able to meet real-time constraints. Several enhancements to the artificial hormone system have been made, such as partial suppressing of tasks and distributed task termination. A priority program is a funding instrument to advance knowledge in an emerging field of research through collaborative networked support over several locations, see [8]. § Collaborative Research Centres are institutions established at universities for a period of up to 12 years that enable researchers to pursue an outstanding research program, see [7]. ¶ Research training groups are established by universities to promote young researchers, see [9]. Receive stage:Resource receives the modified eager values Em i , suppressors S i and accelerators A i for each task T i from each resource in the network. The communication between the different resources is depicted by the dashed lines. Compute and decision stage:Resource computes the modified eager values Em i for all of its tasks in the following way. The local static eager value E i indicates how suited is to execute task T i . From this value, all

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“…|| || The assignment quality was evaluated by using a quality measure published in . This measure rates PE suitability, load and communication distance.…”

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confidence: 99%

An artificial DNA for self‐descripting and self‐building embedded real‐time systems

Brinkschulte

2015

Concurrency and Computation

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Summary Embedded systems are growing more and more complex because of the increasing chip integration density, larger number of chips in distributed applications, and demanding application fields (e.g., in cars and in households). Bio‐inspired techniques like self‐organization are a key feature to handle this complexity. However, self‐organization needs a guideline for setting up and managing the system. In biology, the structure and organization of a system are coded in its DNA. In this paper, we present an approach to use an artificial DNA for that purpose. Because many embedded systems can be composed from a limited number of basic elements, the structure and parameters of such systems can be stored in a compact way, representing an artificial DNA deposited in each computation node. This leads to a self‐describing system. Based on the DNA, the self‐organization mechanisms can build the system autonomously, providing a self‐building system. System repair and optimization at runtime are also possible, leading to higher robustness, dependability, and flexibility. Copyright © 2015 John Wiley & Sons, Ltd.

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Measuring the Quality of an Artificial Hormone System based Task Mapping

Cited by 14 publications

References 0 publications

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Implementation and evaluation of a self‐organizing artificial hormone system to assign time‐dependent tasks

An artificial DNA for self‐descripting and self‐building embedded real‐time systems

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