• ETRI Journal
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• v.32 no.3
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• pp.422-429
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• 2010

We present guaranteed dynamic priority assignment schemes for multiple real-time tasks subject to (m, k)-firm deadlines. The proposed schemes have two scheduling objectives: providing a bounded probability of missing (m, k)-firm constraints and maximizing the probability of deadline satisfactions. The second scheduling objective is especially necessary in order to provide the best quality of service as well as to satisfy the minimum requirements expressed by (m, k)-firm deadlines. We analytically establish that the proposed schemes provide a guarantee on the bounded probability of missing (m, k)-firm constraints. Experimental studies validate our analytical results and confirm the effectiveness and superiority of the proposed schemes with regard to their scheduling objectives.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.10
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• pp.8-15
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• 2003

In this paper, we address how to provide absolute differentiated services for optical burst switching (OBS) networks based on dynamic wavelength assignment. Unlike existing quality of service (QoS), such as buffer-based and offset-time based scheme, our proposed dynamicvirtual lambda partitioning (DVLP) scheme does not mandate any buffer or extra offset time, but can achieve absolute service differentiation between classes. This new DVLP scheme shares wavelength resources based on several different priority of classes in an efficient and QoS guaranteed manner. The performance results show that the proposed scheme effectively guarantees a target blocking probability of each traffic classes both in Poisson and Self-similar traffic environment.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.