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We propose a new algorithmic framework for traffic-optimal virtual network function (VNF) placement and migration for policy-preserving data centers (PPDCs). As dy- namic virtual machine (VM) traffic must traverse a sequence of VNFs in PPDCs, it generates more network traffic, consumes higher bandwidth, and causes additional traffic delays than a traditional data center. We design optimal, approximation, and heuristic traffic-aware VNF placement and migration algorithms to minimize the total network traffic in the PPDC. In particular, we propose the first traffic-aware constant-factor approximation algorithm for VNF placement, a Pareto-optimal solution for VNF migration, and a suite of efficient dynamic-programming (DP)-based heuristics that further improves the approximation solution. At the core of our framework are two new graph- theoretical problems that have not been studied. Using flow characteristics found in production data centers and realistic traffic patterns, we show that a) our VNF migration techniques are effective in mitigating dynamic traffic in PPDCs, reducing the total traffic cost by up to 73%, b) our VNF placement algorithms yield traffic costs 56% to 64% smaller than those by existing techniques, and c) our VNF migration algorithms outperform the state-of-the-art VM migration algorithms by up to 63% in reducing dynamic network traffic. 

Measuring the Available Bandwidth (ABW) is an important function for traffic engineering, and in software-defined metro and wide-area network (SD-WAN) applications. Because network speeds are increasing, it is timely to re-visit the effectiveness of ABW measurement again. A significant challenge arises because of Interrupt Coalescence (IC), that network interface drivers use to mitigate the overhead when processing packets at high speed, but introduce packet batching. IC distorts receiver timing and decreases the ABW estimation. This effect is further exacerbated with software-based forwarding platforms that exploit network function virtualization (NFV) and the lower-cost and flexibility that NFV offers, and with the increased use of poll-mode packet processing popularized by the Data Plane Development Kit (DPDK) library. We examine the effectiveness of the ABW estimation with the popular probe rate models (PRM) such as PathChirp and PathCos++, and show that there is a need to improve upon them. We propose a modular packet batching mitigation that can be adopted to improve both the increasing PRM models like PathChirp and decreasing models like PathCos++. Our mitigation techniques improve the accuracy of ABW estimation substantially when packet batching occurs either at the receiver due to IC, DPDK based processing or intermediate NFV-based forwarding nodes. We also show that our technique helps improve estimation significantly in the presence of cross-traffic.