Software-defined networking (SDN) is increasingly popular in today’s information technology industry, but existing SDN control plane is insufficiently scalable to support on-demand, high-frequency flow requests. Weaknesses along SDN control paths can be exploited by malicious third parties to launch distributed denial-of-service (DDoS) attacks against the SDN control plane. Recently proposed solutions only partially solve the problem, by protecting either the SDN network edges or the centralized controller. We propose SDNShield, a solution based on emerging network function virtualization (NFV) technologies, which enforces more comprehensive defense against potential DDoS attacks on SDN control plane. SDNShield incorporates a three-stage overload control scheme. The first stage statistically identifies legitimate flows with low complexity and performance overhead. The second stage further performs in-depth TCP handshake verification to ensure good flows are eventually served. The third stage intellectually salvages the misclassified legitimate flows that are falsely dropped from the first two stages. Prototype tests and real data-driven simulation results show that SDNShield can achieve high resilience against brute-force attacks, and maintain good flow-level service quality at the same time.

This study examines the characteristics of mobile instant-messaging users' relationships with their social contacts and the effects of both relationship and interruption context on four measures of receptivity: Attentiveness, Responsiveness, Interruptibility, and Opportuneness. Overall, interruption context overshadows relationship characteristics as predictors of all four of these facets of receptivity; this overshadowing was most acute for Interruptibility and Opportuneness, but existed for all factors. In addition, while Mobile Maintenance Expectation and Activity Engagement were negatively correlated with all receptivity measures, each such measure had its own set of predictors, highlighting the conceptual differences among the measures. Finally, delving more deeply into potential relationship effects, we found that a single, simple closeness question was as effective at predicting receptivity as the 12-item Unidimensional Relationship Closeness Scale.

History of conversations through instant messaging (IM) contains abundant information about the communication patterns of the dyad, including conversation partners' mutual responsiveness to messages. We have, however, not seen many examinations of using such information in modeling mobile users' responsiveness in IM communication. In this paper, we present an in-the-wild study, in which we leverage participants' IM messaging logs to build models predicting their general responsiveness. Our models based on data from 33 IM user achieved an accuracy of up to 71% (AUROC). In particular, we show that 90-day IM-communication patterns, in general, outperformed their 14-day equivalent in our prediction models, indicating better coherence between long-term IM patterns with their general communication experience.

An important challenge of running large-scale cloud services in a geo-distributed cloud system is to minimize the overall operating cost. The operating cost of such a system includes two major components: electricity cost and wide-area-network (WAN) communication cost. While the WAN communication cost is minimized when all virtual machines (VMs) are placed in one datacenter, the high workload at one location requires extra power for cooling facility and results in worse power usage effectiveness (PUE). In this paper, we develop a model to capture the intrinsic trade-off between electricity and WAN communication costs, and formulate the optimal VM placement problem, which is NP-hard due to its binary and quadratic nature. While exhaustive search is not feasible for large-scale scenarios, heuristics which only minimize one of the two cost terms yield less optimized results. We propose a cost-aware two-phase metaheuristic algorithm, Cut-and-Search, that approximates the best trade-off point between the two cost terms. We evaluate Cut-and-Search by simulating it over multiple cloud service patterns. The results show that the operating cost has great potential of improvement via optimal VM placement. Cut-and-Search achieves a highly optimized trade-off point within reasonable computation time, and outperforms random placement by 50%, and the partial-optimizing heuristics by 10-20%.