摘要：The overall burst loss probability is the primary metric of interest in an optical burst switching network with sparse wavelength conversion capabilities (SWCC-OBS). With the overall burst loss probability as the optimization objective, one has to solve an integer nonlinear programming (INLP) problem. In order to overcome the computational difficulties, we propose a fictitious play method to approximately solve the INLP. Consequently, a randomized routing strategy can be achieved. The simulation results show that the proposed strategy can give a near-optimal route that avoids the conflict of burst data effectively and decreases the overall burst loss probability. At the same time it also performs well in balancing loads throughout the whole network under different kinds of burst traffic pattern.

摘要：The identical parallel machine scheduling problem with the objective of minimizing total weighted completion time is considered in the online setting where jobs arrive over time. An online algorithm is proposed and is proven to be (2.5−1/2m) -competitive based on the idea of instances reduction. Further computational experiments show the superiority over other algorithms in the average performance.

摘要：We consider the classical online scheduling problem over single and parallel machines with the objective of minimizing total weighted flow time. We employ an intuitive and systematic analysis method and show that the Weighted Shortest Processing Time (WSPT) is an optimal online algorithm with the competitive ratio of P+1 for the case of single machine, and it is (P+32−12m) -competitive for the case of parallel machines ( m>1 ), where P is the ratio of the longest to the shortest processing time.