A Token-Based Mutual Exclusion Approach to Improve Collaboration in Distributed Environments

Paletta, Mauricio; Herrero, Pilar

doi:10.1007/978-3-642-04441-0_10

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…The writings by Camarinha‐Matos (2007, 2009), Camarinha‐Matos and Afsarmanesh (2005, 2006, 2007) and Camarinha‐Matos et al (2009) serve as examples. It could also be that work is interrelated or represents different aspects of the same project, with instances being Paletta and Herrero (2009a, b, c), Wang and Shen (2003) and Wang et al (2006a). No matter these reservations, the results indicate the increasing attention by academic researchers for collaborative networks and the multi‐disciplinary character of this domain and they are sufficient to address the questions that direct our quest.…”

Section: Results Of Literature Reviewmentioning

confidence: 92%

Appraising interdisciplinary contributions to theory for collaborative (manufacturing) networks

Dekkers

Kühnle

2012

Journal of Manufacturing Technology Management

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PurposeProgress in theory building in the field of collaborative networks in manufacturing is preponderantly seen in contributions from disciplines outside manufacturing science. Interdisciplinary research is one way of accelerating the development of appropriate theory for this emerging domain where industrial practice has moved beyond the state of the art of scientific knowledge for establishing workable, competitive solutions. The purpose of this paper is to examine to what extent interdisciplinary research has contributed to a better understanding of collaborative (manufacturing) networks.Design/methodology/approachTo find out more about provenances of on‐going studies, to identify clusters of contributions and to provide direction for future work of researchers in this domain, publications of the past 22 years have been evaluated. To retrieve these contributions, a structured literature review has been undertaken by applying keywords to selected databases and using a strictly defined stepwise procedure. In total, 202 publications of all kinds have been evaluated.FindingsFrom the analysis of the results, it appears that most interdisciplinary contributions to collaborative (manufacturing) networks rely on one original outside discipline for either developing solutions or advancing theoretical insight. Consequently, and after further analysis, it seems that researchers in collaborative networks hardly resort to multi‐disciplinary approaches, unless “natural”; further advances might arrive from stimulating these multi‐disciplinary avenues rather than sticking to more mono‐disciplinary, and less risky, takes on both applications and theoretical insight. A more detailed investigation of the value of contributions reveals that efforts to make interdisciplinary advances are either difficult or limited. Also, the findings indicate that researchers tend to follow a more “technical” approach to decision making by actors in networks rather than searching for a shift in paradigm.Originality/valueWhile setting out these directions for future research and guiding research, this first‐of‐its‐kind review introduces the collaboration model as a systematic approach to collaborative (manufacturing) networks. This model might serve as a reference model to integrate disciplines for addressing the characteristics of Collaborative Networks. Its use in the review led to the finding that typical traits of networks, such as changeability, supplementary assets and decentralisation of decision making, are under‐researched.

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Section: Results Of Literature Reviewmentioning

confidence: 92%

Appraising interdisciplinary contributions to theory for collaborative (manufacturing) networks

Dekkers

Kühnle

2012

Journal of Manufacturing Technology Management

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“…The strategy used in AMBAR is a variation of the NaimiTréhel's token-based algorithm (Naimi et al, 1996). In the AMBAR token-based approach (Paletta & Herrero, 2009b), the token travels with a queue Q which has the nodes that require the exclusive use of the critical section and haven't been able to satisfy that need.…”

Section: The Mutual Exclusion Mechanismmentioning

confidence: 99%

Artificial Neural Network for Cooperative Distributed Environments

Paletta¹

2011

Artificial Neural Networks - Application

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“…The strategy used in AMBAR is a variation of the Naimi-Tréhel's token-based algorithm [23]. In the AM-BAR token-based approach [24], the token travels with a queue Q which has the nodes that require the exclusive use of the critical section and haven't been able to satisfy that need.…”

Section: The Mutual Exclusion Mechanismmentioning

confidence: 99%

An Awareness-Based Learning Model to Deal with Service Collaboration in Cloud Computing

Paletta

Herrero

2010

Lecture Notes in Computer Science

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Abstract.Cloud computing addresses the use of scalable and often virtualized resources. It is based on service-level agreements that provide external users with services under request. Cloud computing is still evolving. New specific collaboration models among service providers are needed for enabling effective service collaboration, allowing the process of serving consumers to be more efficient. This paper presents AMBAR-C, an adaptation of AMBAR (Awarenessbased learning Model for distriButive collAborative enviRonment) designed to allow nodes in a distributed environment to accomplish an effective collaboration among service providers in a "cloud" by means of a multi-agent architecture in which agents are aware of its surroundings throughout a parametrical and flexible use of this information. As occurs in AMBAR, AMBAR-C makes use of heuristic strategies to improve effectiveness and efficiency in collaborations of these particular environments.

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A Token-Based Mutual Exclusion Approach to Improve Collaboration in Distributed Environments

Cited by 7 publications

References 18 publications

Appraising interdisciplinary contributions to theory for collaborative (manufacturing) networks

Appraising interdisciplinary contributions to theory for collaborative (manufacturing) networks

Artificial Neural Network for Cooperative Distributed Environments

An Awareness-Based Learning Model to Deal with Service Collaboration in Cloud Computing

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