A computer network serves distributed applications by communicating messages between their remote ends. Many such applications desire minimal delay for their messages. Beside this efficiency objective, allocation of t...
详细信息
A computer network serves distributed applications by communicating messages between their remote ends. Many such applications desire minimal delay for their messages. Beside this efficiency objective, allocation of the network capacity is also subject to the fairness constraint of not shutting off communication for any individual message. Processor Sharing (PS) is a de facto standard of fairness but provides significantly higher average delay than Shortest Remaining Processing Time (SRPT), which is an optimally efficient but unfair algorithm. In this paper, we explore efficient fair algorithms for message communication where fairness means that no message is delivered later than under PS. First, we introduce a slack system to characterize fair algorithms completely and develop efficient fair algorithms called Pessimistic fair Sojourn Protocol (PFSP), Optimistic fair Sojourn Protocol (OFSP), and Shortest fair Sojourn (SFS). Then. we prove that a fair online algorithm does not assure minimal average delay attainable with fairness. Our analysis also reveals lower bounds on worst-case inefficiency of fair algorithms. We conduct extensive simulations for various distributions of message sizes and arrival times. During either temporary overload or steady-state operation, SFS and other newly proposed fair algorithms support SRPT-like efficiency and consistently provide much smaller average delay than PS. (C) 2008 Elsevier B.V. All rights reserved.
Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only consid...
详细信息
Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two-layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi-variational problem, indicating that the MPEC is solvable. Combining the two-layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing.
This paper presents a procedure to select equitable stable allocations in two-sided matching markets without side payments. The Equitable set is computed using the Equitable algorithm. The algorithm limits the set of ...
详细信息
This paper presents a procedure to select equitable stable allocations in two-sided matching markets without side payments. The Equitable set is computed using the Equitable algorithm. The algorithm limits the set of options available for each agent throughout the procedure. The stable matchings selected are generally not extreme, form a lattice and satisfy the condition of being "Ralwsian" in each partition of the market. The Equitable algorithm can also be used to select a particular matching from the Equitable Set favoring particular agents independent of the side of the market to which they belong.
We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of...
详细信息
We develop a fully Bayesian tracking algorithm with the purpose of providing classification prediction results that are unbiased when applied uniformly to individuals with differing sensitive variable values, e.g., of different races, sexes, etc. Here, we consider bias in the form of group-level differences in false prediction rates between the different sensitive variable groups. Given that the method is fully Bayesian, it is well suited for situations where group parameters or regression coefficients are dynamic quantities. We illustrate our method, in comparison to others, on simulated datasets and two real-world datasets.& COPY;2022 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
暂无评论