Self-organizing Bandwidth Sharing in Priority-Based Medium Access

Wildermann, Stefan; Ziermann, Tobias; Teich, Jürgen

doi:10.1109/saso.2009.18

Cited by 5 publications

(8 citation statements)

References 14 publications

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“…Another group of studies [28], [33], [38], [49], [53], [73], [77] consider CSAS in which agents share limited information and not their full state. This limited information could take the form of selected agent configurations [73], adaptation tactics [28] or expected rewards [33], [38], [53].…”

Section: A Rq1: Csas Characteristics 1) Application Domain and Agentsmentioning

confidence: 99%

“…An interesting outcome of our review is the following. In several studies [30], [35], [36], [39], [43]- [45], [48], [49], [53], [61], [63], [69], [70], the system is designed in a way that each individual selfish agent learns a "learning task" and is not aware of the fact that it is collaborating. In these circumstances, the emergent behaviour of the system is the result of the agents' unaware collaboration and is often a system-wide collaboration scheme achieving a global goal.…”

Section: ) Behaviourmentioning

confidence: 99%

“…In [59]- [61], agents learn individually how to play the specified games and cooperate to achieve a positive outcome, i.e., win the game. Similarly, within the network domain, agents individually learn to propagate messages [50], predict the loss [48], and other individual tasks [46], [49], [53]. In this context, the emergent CSAS behaviour is the systemwide optimal dissemination of information.…”

Section: B Rq2: Learning Purposementioning

confidence: 99%

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On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework

D'Angelo

Gerasimou

Ghahremani

et al. 2019

2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.

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Section: A Rq1: Csas Characteristics 1) Application Domain and Agentsmentioning

confidence: 99%

Section: ) Behaviourmentioning

confidence: 99%

Section: B Rq2: Learning Purposementioning

confidence: 99%

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On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework

D'Angelo

Gerasimou

Ghahremani

et al. 2019

2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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“…In [11], it is proven that all players i ∈ N are in a Nash equilibrium iff choosing strategiesp that fulfill In such a system state, no player has an intent to switch the strategy because the payoff will not increase if the others do not change their strategy.…”

Section: Enhanced Priority-based Medium Access Gamementioning

confidence: 99%

Distributed self‐organizing bandwidth allocation for priority‐based bus communication

Ziermann

Wildermann

Mühleis

et al. 2011

Concurrency and Computation

Self Cite

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The raising complexity in distributed embedded systems makes it necessary that the communication of such systems organizes itself automatically. In this paper, we tackle the problem of sharing bandwidth on priority-based buses. Based on a game theoretic model, reinforcement learning algorithms are proposed that use simple local rules to establish bandwidth sharing. The algorithms require little computational effort and no additional communication. Extensive experiments show that the proposed algorithms establish the desired properties without global knowledge or a priori information. It is proven that communication nodes using these algorithms can co-exist with nodes using other scheduling techniques. Finally, we propose a procedure that helps to set the learning parameters according to the desired behavior. Therefore, we prefer to approach the goal of a working communication system from a different perspective. Starting with minimal constraints on the individual communication nodes, the nodes themselves should be able to establish communication. This should be done by using simple local rules with as less global information as possible. As the first step in this development process, we want to take a look at bandwidth-constrained communication, where the quality of the function varies with the used amount of bandwidth. In future work, these constraints will be extended to cover a larger field of applications.The remainder of the paper is organized as follows. In Section 2 the problem is defined and placed into the context-related work. In Section 3, the priority-based medium access game is formulated, which represents the fundamentals for the proposed methods. Section 4 presents the distributed reinforcement learning algorithm to establish fair bandwidth distribution. The parameters are analyzed and it is proven that the algorithm also works together with other scheduling methods. In Section 5, the period access scheme is introduced and analyzed, while Section 6 concludes our work and gives outlook on future work.

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“…The priority scheduling can satisfy the required Quality of Service (QoS) for ongoing flows under normal load [2] . Now, the networks based on packet switching can ensure the end-to-end performance for data transmission using the established QoS mechanism.…”

Section: Introductionmentioning

confidence: 99%

Combined admission control and scheduling in multi-service networks

Qiu

Zhang

et al. 2010

J. Electron.(China)

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Providing the required metrics for different service respectively is a basic characteristic in multi-service networks. The different service can be accessed and forwarded differently to provide the different transmission performance. The state information between admission control and scheduling can be exchanged each other by the defined correlation coefficient to adjust the flow distribution in progress. The priority queue length measured by scheduler implicitly can describe the priority flows load. And the fair rate can describe the non-priority flows load. Different admission decision will be made according to the state of scheduler to assure the time-delay upper threshold for the priority flows under heavy load and the fairness for elastic flows in light load, respectively. The stability condition was conduced and proved. Simulation results show the policy can ensure both the delay for the priority flows and the minimal throughput for non-priority flows.

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Self-organizing Bandwidth Sharing in Priority-Based Medium Access

Cited by 5 publications

References 14 publications

On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework

On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework

Distributed self‐organizing bandwidth allocation for priority‐based bus communication

Combined admission control and scheduling in multi-service networks

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