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Network Functions Virtualization (NFV) enables telecommunications infrastructure providers to replace specialpurpose networking equipment with commodity servers running virtualized network functions (VNFs). A provider utilizing NFV faces the Service Function Chain (SFC) provisioning problem of assigning VNF instances to nodes in the physical infrastructure (e.g. datacenters), and routing Service Function Chains (sequences of functions required by customers, a.k.a. SFCs) in the physical network. The provider must balance competing goals of performance and resource usage. We present an approach to SFC provisioning, consisting of three elements. The first element is a fast and scalable round-robin heuristic. The second element is a Mixed Integer Programming (MIP) based approach. The third element is a queueing-theoretic model to estimate the average latency associated with any SFC provisioning solution. Our SFC provisioning system, called Stringer, allows providers to balance the conflicting goals of minimizing infrastructure resources and end-to-end latency for meeting their respective SLAs. # nodes expected latency max utilizaton # nodes Present results to user Input data Figure 1: Flowchart of System: The input data, consisting of the service chains, their requirements and the data center network topology, are given to the heuristic. The basic solution from the heuristic provides an initial solution for the mixed integer program. Expected latency is then computed for each of the solutions from the optimizer. The user can then choose from a menu of solutions differing in expected latency and number of servers used. the specified order. For example, a service chain may require packets to follow the VNF sequence: load balancer, network address translator, and firewall. In the SFC provisioning problem, one must place (possibly multiple) instances of each VNF on servers, and choose the route(s) for each service chain, in such a way that the network can accommodate the traffic for as many service chains according to their priorities. Service chains may share VNF instances. Moreover, the traffic for a given service chain may be split among multiple paths in the network when multiple instances of a specific VNF are used.Our work differs from prior work in VNF placement in several important ways. One key difference is in the placement objective. Operators have multiple competing goals to consider when placing VNFs. A service provider may want to use as few servers as possible in order to minimize operating costs and leave open servers for future needs ([1, 2]). At the same time, the operator must ensure low end-to-end network latency for his customers. These objectives are in direct conflict. While some prior work proposes multiple alternative objectives ([3, 4, 5]), ours is the first, to our knowledge, that provides a flexible way to trade-off these competing goals in SFC provisioning. Moreover, unlike [5], our optimization model employs only linear constraints to model maximum utilization.Another important difference is...

Users face many choices on the web when it comes to choosing which product to buy, which video to watch, and so on. In making adoption decisions, users rely not only on their own preferences, but also on friends. We call the latter social correlation, which may be caused by the homophily and social influence effects. In this paper, we focus on modeling social correlation on users item adoptions. Given a user-user social graph and an item-user adoption graph, our research seeks to answer the following questions: Whether the items adopted by a user correlate with items adopted by her friends, and how to model item adoptions using social correlation. We propose a social correlation framework that considers a social correlation matrix representing the degrees of correlation from every user to the users friends, in addition to a set of latent factors representing topics of interests of individual users. Based on the framework, we develop two generative models, namely sequential and unified, and the corresponding parameter estimation approaches. From each model, we devise the social correlation only and hybrid methods for predicting missing adoption links. Experiments on LiveJournal and Epinions data sets show that our proposed models outperform the approach based on latent factors only (LDA).

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