Social networks are becoming important dissemination platforms, and a large body of works have been performed on viral marketing, but most are to maximize the benefits associated with the number of active nodes. In this paper, we study the benefits related to interactions among activated nodes. Furthermore, due to the uncertainty in edge probability estimates in social networks, we propose the robust profit maximization problem to have the best solution in the worst case of probability settings. We design a double sandwich algorithm to this problem and further improve the algorithm with sampling method such that it increases robustness of the output. Through real data sets, we verify the effectiveness of our proposed algorithm.

Motivated by the need for analyzing the rapidly producing data streams, such as images, videos, sensor data, etc, in a timely manner, the study on the streaming algorithms to extract representative information from massive data to maximize some objective function is therefore important and urgent. Most of previous works are assumed under a noise-free environment, while in many realistic applications obtaining the exact function value is hard or computing the function value may cost much, which brings the noisy version. Hence in this paper, we address a more general problem to select a subset of at most k elements from the stream to maximize a noisy set function (not necessarily submodular). To be specific, we cast our problem as the streaming submodular maximization problem under multiplicative and additive noise models. We develop an efficient thresholding streaming algorithm, which calls several copies of a subroutine in parallel. Therefore, this algorithm only requires two passes over data and has a memory independent of data size. For both of noisy models, its approximation guarantee approaches 2/k. In our numerical experiments, we extensively evaluate the effectiveness of our thresholding streaming algorithm on some applications in real data set.

The energy cost is one of the major concerns for the cloud providers. Virtual machine placement has been demonstrated as an effective method for energy saving. In addition to constraints caused by the physical machine resources such as CPU and memory (PM-constraints), the constraints caused by the network resource such as bandwidth (Net-constraints) are also crucial, since virtual machines are not isolated and require communication with each other to exchange data. However, most current research on data center power optimization only focuses on server resource. As a result, the optimization results are often inferior, because server consolidation without considering the network may cause traffic congestion and thus degraded network performance. We take the traffic demands between virtual machines into consideration and formulate the virtual machine placement problem under both PM-constraints and Net-constraints to minimize the energy cost, and propose an approach based on ant colony optimization to solve the problem. We evaluate the expected performance of our proposed algorithm through a simulation study, providing strong indications to the superiority of our proposed solution.

Software Defined Network (SDN) has been envisioned as the next generation network infrastructure, which simplify network management by decoupling the control plane and data plane. It is becoming the leading technology behind many traffic engineering solutions, since it allows a central controller to globally plan the paths of the flows. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we address the routing rule space occupation problem for multiple unicastSessions with Quality-of-Service (QoS) constraints. To our best knowledge, this is the first work to joint routing rule optimization with traffic engineering for multipath flows. We formulate the problem using Mixed Integer Linear Programing (MILP) and propose an approach based on ant colony algorithm to solve it. Finally, we evaluate the expected performance of our proposed algorithm through a simulation study.

Software Defined Networks (SDNs) allow a centralized controller to globally plan packets forwarding according to the operator's objectives. The realization of global objectives requires more local forwarding rules. However, the forwarding tables in TCAM-based SDN switches are limited resources. In this paper, we concentrate on satisfying global network objectives, such as maximum flow, with the limitation of forwarding table size. We formulate the problem as the Bounded Forwarding-Rules Maximum Flow (BFR-MF) problem. And then, we improve the updating of particles in Particle Swarm Optimization (PSO) by merging particles and propose the PSO-based Maximum Flow (PSO-MF) algorithm to maximize the overall feasible traffic. We maintain fairness among flows to guarantee a certain level of Quality-of-Service (QoS). Extensive simulations show that PSO-MF algorithm performs well in network utilization both for backbone and data center networks.