Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

Many of the video streaming applications in today's Internet involve the distribution of content from a CDN source to a large population of interested clients. However, widespread support of IP multicast is unavailable due to technical and economical reasons, leaving the floor to application layer multicast which introduces excessive delays for the clients and increased traffic load for the network. This paper is concerned with the introduction of an SDN-based framework that allows the network controller to not only deploy IP multicast between a source and subscribers, but also control, via a simple northbound interface, the distributed set of sources where multiple- description coded (MDC) video content is available. We observe that for medium to heavy network loads, relative to the state-of-the-art, the SDN-based streaming multicast video framework increases the PSNR of the received video significantly, from a level that is practically unwatchable to one that has good quality.Comment: 6 pages, 5 figures, 1 table, First Workshop on Software-Defined Internets of the Future - WSDIF 2014. Proceedings of the 11th IEEE International Conference on Mobile Ad hoc and Sensor Systems (MASS) 2014 - Philadelphia, Pennsylvania - October 28 - 30, 201

With the emergence of millimeter-Wave (mmWave) communication technology, the capacity of mobile backhaul networks can be significantly increased. On the other hand, Mobile Edge Computing (MEC) provides an appropriate infrastructure to offload latency-sensitive tasks. However, the amount of resources in MEC servers is typically limited. Therefore, it is important to intelligently manage the MEC task offloading by optimizing the backhaul bandwidth and edge server resource allocation in order to decrease the overall latency of the offloaded tasks. This paper investigates the task allocation problem in MEC environment, where the mmWave technology is used in the backhaul network. We formulate a Mixed Integer NonLinear Programming (MINLP) problem with the goal to minimize the total task serving time. Its objective is to determine an optimized network topology, identify which server is used to process a given offloaded task, find the path of each user task, and determine the allocated bandwidth to each task on mmWave backhaul links. Because the problem is difficult to solve, we develop a two-step approach. First, a Mixed Integer Linear Program (MILP) determining the network topology and the routing paths is optimally solved. Then, the fractions of bandwidth allocated to each user task are optimized by solving a quasi-convex problem. Numerical results illustrate the obtained topology and routing paths for selected scenarios and show that optimizing the bandwidth allocation significantly improves the total serving time, particularly for bandwidth-intensive tasks.Index Terms-Millimeter-wave network, mobile edge computing, resource allocation.• We propose a generic formulation that models the task allocation problem for mmWave backhaul networks. The output of this model determines an optimized network

Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and rerouting corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.

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Automating Ethernet VPN deployment in SDN-based Data Centers.

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