Novel Approaches to Challenges in Emerging Network Paradigms
SDN (Software defined networking) and NFV (Network Function Virtualization) are two emerging network paradigms that enable simplification, flexibility and cost-reduction in network management. We believe that the new paradigms will lead to many interesting research questions. We study how to rely on them for dealing with two common network challenges. We consider switches that imply network policies in SDN through rule matching tables of limited size. We study the applicability of rule caching and lossy compression to create packet classifiers requiring much less memory than the theoretical size limits of semantically-equivalent representations. We would like to find limited-size classifiers that can correctly classify a high portion of the traffic. We address different goals with unique settings and explain how to deal with the traffic that cannot be classified correctly. We also demonstrate how to take advantage of possible flexibility in the address allocation. Network functions such as load balancing and deep packet inspection are often implemented in dedicated hardware called middleboxes. Those can suffer from temporary unavailability due to misconfiguration or software and hardware malfunction. We suggest to rely on virtualization for planning and deploying backup schemes for network functions. The schemes guarantee high levels of survivability with significant reduction in resource consumption. We discuss different goals that network designers should take into account. We describe a graph theoretical model for finding properties of efficient solutions and developing algorithms that can build them. Joint work with Jennifer Rexford (Princeton University), Sanjay G. Rao (Purdue University), Janos Tapolcai (BME University), Yossi Kanizo (Tel-Hai College), Nanxi Kang (Databricks), Jose Yallouz (Intel) and Itai Segall (Bell Labs, Nokia).