With the introduction of Network Function Virtualization (NFV) technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a Cloud Service Provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called Network Function Center (NFC), to study research issues related to Virtualized Network Functions (VNFs). In a NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on Genetic Algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: (1) the initial placement of VNFs, and (2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional Integer Linear Programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.

Recent studies reveal that the routing structures of operational networks are much more complex than a simple BGP/IGP hierarchy, highlighted by the presence of many distinct instances of routing protocols. However, the glue (how routing protocol instances interact and exchange routes among themselves) is still little understood or studied. For example, although Route Redistribution (RR), the implementation of the glue in router software, has been used in the Internet for more than a decade, it was only recently shown that RR is extremely vulnerable to anomalies similar to the permanent route oscillations in BGP. This paper takes an important step toward understanding how RR is used and how fundamental the role RR plays in practice. We developed a complete model and associated tools for characterizing interconnections between routing instances based on analysis of router configuration data. We analyzed and characterized the RR usage in more than 1600 operational networks. The findings are: (i) RR is indeed widely used; (ii) operators use RR to achieve important design objectives not realizable with existing routing protocols alone; (iii) RR configurations can be very diverse and complex. These empirical discoveries not only confirm that the RR glue constitutes a critical component of the current Internet routing architecture, but also emphasize the urgent need for more research to improve its safety and flexibility to support important design objectives.

Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical timevarying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: (1) A regularizationbased approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot; (2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.

Abstract-Route redistribution (RR) has become an integral part of IP network design as the result of a growing need for disseminating certain routes across routing protocol boundaries. While RR is widely used and resembles BGP in several nontrivial aspects, surprisingly, the safety of RR has not been systematically studied by the networking community. This paper presents the first analytical model for understanding the effect of RR on network wide routing dynamics and evaluating the safety of a specific RR configuration. We first illustrate how easily inaccurate configurations of RR may cause severe routing instabilities, including route oscillations and persistent routing loops. At the same time, general observations regarding the root causes of these instabilities are provided. We then introduce a formal model based on the general observations to represent and study the safety of route redistribution. Using the model, we prove that given a RR configuration, determining whether the redistributions result in a cycle is NP-hard. Given this complexity, we present a sufficient condition, which can be checked in polynomial time with the proposed analytical model, for ensuring the safety of a RR configuration. Finally, the paper proposes potential changes to the current RR protocol to guarantee safety.

Abstract-By allowing network functions to be virtualized and run on commodity hardware, NFV enables new properties (e.g., elastic scaling), and new service models for Service Providers, Enterprises, and Telecommunication Service Providers. However, for NFV to be offered as a service, several research problems still need to be addressed. In this paper, we focus and propose a new service chaining algorithm. Existing solutions suffer two main limitations: First, existing proposals often rely on mixed Integer Linear Programming to optimize VM allocation and network management, but our experiments show that such approach is too slow taking hours to find a solution. Second, although existing proposals have considered the VM placement and network configuration jointly, they frequently assume the network configuration cannot be changed. Instead, we believe that both computing and network resources should be able to be updated concurrently for increased flexibility and to satisfy SLA and Qos requirements. As such, we formulate and propose a Genetic Algorithm based approach to solve the VM allocation and network management problem. We built an experimental NFV platform, and run a set of experiments. The results show that our proposed GA approach can compute configurations to to three orders of magnitude faster than traditional solutions.