An Agressive Strategy for an Artificial Hormone System to Minimize the Task Allocation Time

Brinkschulte, Uwe; Pacher, Mathias

doi:10.1109/isorcw.2012.40

Cited by 22 publications

(21 citation statements)

References 11 publications

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“…Therefore,

h {falsefalse}_{\max}^{i = 1} n_{i}

tasks are allocated per cluster at most. According to , the maximal task allocation time per cluster is

h {falsefalse}_{\max}^{i = 1} n_{i}

hormone cycles that proves the theorem.…”

Section: The Hierarchical Artificial Hormone Systemsupporting

confidence: 59%

“…Let n i ( n i >0) be the number of tasks in organ i (corresponding to organ task O i ), and the hormones of each task be chosen so that a single allocation might occur at most. Then, the worst case execution time (WCET) t S C (in hormone cycles) for self‐configuration in the HAHS is

t_{SC} = m + h \max_{i = 1}^{m} n_{i} + 1 hormone cycles .

Proof According to , the HAHS needs m hormone cycles to allocate the m organ tasks. The cluster heads running the organs tasks send notification hormones to their clusters that costs an additional hormone cycle.…”

Section: The Hierarchical Artificial Hormone Systemmentioning

confidence: 99%

“…Proof According to [8], the HAHS needs m hormone cycles to allocate the m organ tasks. The cluster heads running the organs tasks send notification hormones to their clusters that costs an additional hormone cycle.…”

Section: The Hierarchical Artificial Hormone Systemmentioning

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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“…Therefore,

h {falsefalse}_{\max}^{i = 1} n_{i}

tasks are allocated per cluster at most. According to , the maximal task allocation time per cluster is

h {falsefalse}_{\max}^{i = 1} n_{i}

hormone cycles that proves the theorem.…”

Section: The Hierarchical Artificial Hormone Systemsupporting

confidence: 59%

t_{SC} = m + h \max_{i = 1}^{m} n_{i} + 1 hormone cycles .

Section: The Hierarchical Artificial Hormone Systemmentioning

confidence: 99%

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Two‐level extensions of an artificial hormone system

Pacher

2015

Concurrency and Computation

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“…To maintain an equilibrium, the production of accelerator hormones needs to be countered with the production of suppressors ones. AHSs have been applied to solve resource allocation and management problems in different scenarios, such as [23] and [24], and the analogy could also be applied in Clouds.…”

Section: Discussionmentioning

confidence: 99%

Self-organizing strategies for resource management in Cloud Computing: State-of-the-art and challenges

Endo

Batista

Gonçalves

et al. 2013

2nd IEEE Latin American Conference on Cloud Computing and Communications

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Due to the growth of Cloud Computing, the supporting infrastructure has become more complex, and the centralized solutions suffer resource management difficulties due to the large scale and the dynamicity of the scenario. Consequently, distributed solutions have been proposed in the literature and the self-organizing ones have attracted particular interest due to their robustness and adaptability characteristics. Techniques, such as bio-inspired computing, multi-agent systems, and evolutionary techniques are used to manage resources. The main goal of this paper is to present a start study about how selforganizing solutions are applied in resource management of Cloud providers, as well as to highlight the main research challenges in this area.

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“…Furthermore, hard real-time bounds for the self-X features can be guaranteed. As published in [3], self-configuration and self-optimization of m tasks is finished in m cycles latest, while self-healing requires at maximum m + e cycles, where e denotes the expiration time of a hormone.…”

Section: Artificial Hormone System For Decentralized Task Controlmentioning

confidence: 99%

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

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Brinkschulte

Pacher

2013

16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC 2013)

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This article presents our concept of an artificial hormone system for realizing a completely decentralized selforganizing and real-time capable task control mechanism using self-X properties. Besides the fundamentals of the prior hormone concept and the implementation model, we present latest results of our research: evaluation and superiority analysis of a AHS-controlled SoC towards other approaches in centralized or partly decentralized manner like feedback controllers and complex multi-agents. Furthermore we validate and compare the overheads for size, communication and computation in relation to the improvement in system reliability.

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An Agressive Strategy for an Artificial Hormone System to Minimize the Task Allocation Time

Cited by 22 publications

References 11 publications

Two‐level extensions of an artificial hormone system

Two‐level extensions of an artificial hormone system

Self-organizing strategies for resource management in Cloud Computing: State-of-the-art and challenges

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

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