Evaluation and superiority analysis of a decentralized task control mechanism for dependable real-time SoC architectures

Betting, Benjamin; Brinkschulte, Uwe; Pacher, Mathias

doi:10.1109/isorc.2013.6913207

Cited by 5 publications

(3 citation statements)

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“…Thus, in the HAHS approach, the PEs are partitioned into clusters, and communication between a cluster's PEs is performed only within the cluster and between the cluster heads. In [13], we showed that the needed bandwidth of the AHS and central as well as multi-agent oriented approaches are about the same. As a result, the increase of the number of PEs and tasks leads to an evenly increase of the needed bandwidth both in the AHS and these approaches.…”

Section: Related Workmentioning

confidence: 95%

“…We also evaluated strategies to secure the AHS against malicious attacks within an open network in . In , essential properties of the AHS such as communication overhead were compared to other task allocation systems (task allocation by a central instance, multi‐agent systems, etc. ).…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, an observer/controller architecture was implemented using a learning classifier system, and two kinds of covering algorithms were proposed to generate new rules. A main difference to the task allocation algorithms mentioned previously and presented in is that they use (partly) central instances for task allocation: in the central approach, there is exactly one broker, and in the multi agent approaches, there are several broker for task allocation. In each case, they know how much task instances are allocated in the system and where they are allocated.…”

Section: Related Workmentioning

confidence: 99%

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Two‐level extensions of an artificial hormone system

Pacher

2015

Concurrency and Computation

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Summary The ARTIFICIAL HORMONE SYSTEM (AHS) is a decentralized software that is able to allocate tasks in a system of heterogeneous processing elements (PEs). Tasks are allocated according to their suitability for the heterogeneous PEs, the current PE load, and task relationships. The AHS also provides properties like self‐configuration, self‐optimization, and self‐healing in the context of task allocation. In addition, it is able to guarantee real‐time bounds for such self‐X properties. However, using self‐organization principles introduces increased system complexity such as control of system parameters for self‐organization and additional communication effort. In this contribution, we address these problems by using two different two‐level extensions of the AHS: we consider the choice and control of hormone parameters by an observer/controller architecture as first extension of the AHS and a HIERARCHICAL AHS (HAHS) to save communication bandwidth as second extension of the AHS. For the first extension, we use a machine learning approach for gradually learning the hormone values of different tasks. This is a major advance because expert knowledge is needed to configure the AHS up to now. We present an observer/controller architecture as an extension of the AHS to monitor and control its behavior. The user has to provide a simple set of initial rules, and the observer/controller is able to generate new rules if needed. The evaluation of our approach using a benchmark (containing six different types of tasks) shows that the observer/controller is able to match the goals provided by the user, and we discuss it in detail. The second extension is the hierarchical AHS where the PEs of the system consist of several different clusters each of them having its own communication infrastructure, for example, a bus system. The HAHS is able to save communication effort because the broadcast communication of the hormones is limited to the clusters. Nevertheless, it provides the same self‐X properties as the AHS, even the time for self‐configuration is shorter than for the AHS. We present evaluations that show that the HAHS performs better for applications with large numbers of PEs than the AHS. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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