In this paper, we focus on finding out the parameter setting of the non-decreasing sponsored search auction (NDSSA) such that the produced outcome is satisfied by both the search engine provider (SEP) and advertisers. Six satisfaction factors are included in our problem formulation. The outcome satisfactions of the SEP and advertisers are modeled as the multi-objective optimization problem based on the satisfaction factors. The weighted versions of L2-norm, cosine similarity, and Euclidean distance are proposed to solve the multi-objective problem. We observe the outcome without bias for both participants in our simulation. Both cosine similarity and Euclidean distance show that SEP and advertisers prefer fixed-MIP to AIMD. Moreover, the initial MIP value should be set as small enough such that each advertiser has at least one opportunity to update his/her bid price.

The IEEE 802.16 standard is defined to meet the high-speed and wide-range data transmission in an efficient way. This paper studies the scheduling problem on IEEE 802.16e mesh networks to minimize the energy consumption of mobile stations so as to maximize the network lifetime. We consider the real-time connections and focus on the resource scheduling which includes time slots allocation, QoS guarantee of real-time flows, interference-free on each link, and spectral reuse for the channel. The proposed energy-aware link scheduling algorithms construct a mathematical model of the energy consumption problem and provide a computationally efficient scheme to solve the optimization problem. Comparing with traditional scheduling schemes for real-time flows, simulation results show that the proposed scheme significantly improves the life cycle of mobile stations.

Determining winners in combinatorial auctions is an NP-complete problem. Based on the idea of searching Nash Equilibria (NE), this paper presents a local search procedure to determine winners. To improve the solution quality calculated by the local search, we propose Nash Equilibrium Search Approach (NESA) to probe various NE solutions. According to the simulation results, auctioneer¡¦s revenue in NESA is at least 1.08% and 9.10% more than that in a general GA and Casanova, respectively. A stable solution based on NESA will be obtained within 400 seconds. Moreover, simulation results show that the solution quality of NESA is near-optimal.

For the current technique, the tuning latency ¿ on each transmitter is more than the packet transmission time, and it cannot be ignored in packet scheduling. So in order to reduce the serious affection of tuning operations, we propose the multi-hop model for the All-to-All Broadcast (AAB) problem in WDM optical star networks with N nodes and w available wavelengths, which the number of hop distance for packet transmissions is limited to a constant ¿. Our strategy can overlap the tuning latency in multi-hop scheduling, and reduce the number of tuning operations on each transmitter to 2¿(w - 1)/¿¿. If ¿ can be completely covered in scheduling, the schedule length is k(k - 1) + ¿ + 2¿(w-1)/¿¿[k2¿(¿+1)/2] time slots, where k = N/w. Otherwise, it is k(k - 1) + ¿ + 2¿(w-1)/¿¿[k¿(¿+1)/2 + ¿] slots. Our proposed method is very suitable for the scalable WDM networks if the number of wavelength is fixed, and its multi-hop schedule length is shorter than that of other scheduling approaches if k is small or ¿ is larger.

The problem of minimum congestion hypergraph embedding in a cycle (MCHEC) is to embed the hyperedges of a hypergraph as adjacent paths around a cycle, such that the maximum congestion over any physical link in the cycle is minimized. The problem is NP-complete in general, but solvable in polynomial time when all hyperedges contain exactly two vertices. In this paper, we first formulate the problem as an integer linear program (ILP). Then, a solution with approximation bound of 1.5(opt + 1) is presented by using a clockwise (2/3)-rounding algorithm, where opt denotes the optimal value of maximum congestion. To our knowledge, this is the best approximation bound known for the MCHEC problem.