As the Software Defined Networking (SDN) paradigm gains momentum, every network operator faces the obvious dilemma: when and how to migrate from existing IP routers to SDN compliant equipments. A single step complete overhaul of a fully functional network is impractical, while at the same time, the immediate benefits of SDN are obvious. A viable solution is thus a gradual migration over time, where questions of which routers should migrate first, and whether the order of migration makes a difference, can be analyzed from techno economic and traffic engineering perspective. In this paper, we address these questions from the techno economic perspective, and establish the importance of migration scheduling. We propose optimization techniques and greedy algorithms to plan an effective migration schedule, based on various techno economic aspects, such as technological gains in combinations with CapEx limitations. We demonstrate the importance of an effective migration sequence through two relevant network management metrics, namely, number of alternative paths availed by a node on migration, and network capacity savings. Our results suggest that the sequence of migration plays a vital role, especially in the early stages of network migration to SDN.
Network migration has been studied using and models. In the system dynamics approach, the migration problem is treated as a dynamic system @cite_13 , where the rate of migration depends on number of migrated agents in the system. On the other hand, in an agent-based approach @cite_9 , the system consists of an ensemble of agents, each trying to increase its own utility. Such studies have mostly been conducted for IPv6 @cite_11 and secure BGP @cite_6 .
@cite_9 @cite_13 @cite_6 @cite_11
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Conventionally, network migration models study competition between emerging and incumbent technologies by considering the revenue increase and migration cost. We propose to extend the existing network migration models with new critical factors, including (i) synergistic relationships across multiple technologies, (ii) reduction in operation expenditures (OpEx), and, (iii) effect of social factors on human decisions. To this end, we propose a novel agent-based migration model considering these factors. Based on the model, we analyze the case study of optimal path computation with joint migration to two emerging networking paradigms, i.e., IETF Path Computation Element (PCE) and Software-Defined Networking (SDN). Our results demonstrate the synergistic effects of migration to multiple complementary technologies, and shows that a technology migration may be eased by the joint migration to multiple technologies. The rate of deployment and adoption issues of new network technologies, IPv6 in particular, have recently been hotly debated in the research community. However, the question of how protocols migrate, especially the dynamics of migration, to new paradigms is still largely open. In this paper, we address the issue from a game theoretic point of view. We model and analyze the profit maximizing strategies of Autonomous Systems (ASes); both the properties of ASes and the topology of the Internet is considered. The contribution of our work is threefold. First, we propose an economic model of the ASes and their relations from the IPv4-IPv6 migration viewpoint. Second, we apply the findings of evolutionary dynamics on the problem of migration by incorporating Internet-specific properties to the evolutionary model, namely the size of the ASes and the cost of migration. The analyses show that even if IPv6 has higher payoff than IPv4, the whole migration does not happen always fast. Finally, extensive simulations are carried out based on the proposed models to illustrate the impacts of different parameters on the IPv6 migration dynamics in realistic scenarios. Based on the empirical analysis of the dependency network in 18 Java projects, we develop a novel model of network growth which considersboth: an attachment mechanism and the addition of new nodes with a heterogeneous distribution of their initial degree, k0. Empirically we find that the cumulative degree distributions of initial degrees and of the final network, follow power-law behaviors: P(k0) ∝ k01−α, and P(k) ∝ k1−γ, respectively. For the total number of links as a function of the network size, we find empirically K(N) ∝ Nβ, where β is (at the beginning of thenetwork evolution) between 1.25 and 2, while converging to ∼ 1 for large N. This indicates a transition from a growth regime with increasing network density towards a sustainable regime, which revents a collapse because of ever increasing dependencies. Our theoretical framework is able to predict relations between the exponents α, β, γ, which also link issues of software engineering and developer activity. These relations are verified by means of computer simulations and empirical investigations. They indicate that the growth of real Open Source Software networks occurs on the edge between two regimes, which are either dominated by the initial degree distribution of added nodes, or by the preferential attachment mechanism. Hence, the heterogeneous degree distribution of newly added nodes, found empirically, is essential to describe the laws of sustainable growth in networks. We study the problem of exploration by a mobile entity (agent) of a class of dynamic networks, namely the periodically-varying graphs (the PV-graphs, modeling public transportation systems, among others). These are defined by a set of carriers following infinitely their prescribed route along the stations of the network. Flocchini, Mans, and Santoro [FMS09] (ISAAC 2009) studied this problem in the case when the agent must always travel on the carriers and thus cannot wait on a station. They described the necessary and sufficient conditions for the problem to be solvable and proved that the optimal number of steps (and thus of moves) to explore a n-node PV-graph of k carriers and maximal period p is in Θ(k·p2) in the general case. In this paper, we study the impact of the ability to wait at the stations. We exhibit the necessary and sufficient conditions for the problem to be solvable in this context, and we prove that waiting at the stations allows the agent to reduce the worst-case optimal number of moves by a multiplicative factor of at least Θ(p), while the time complexity is reduced to Θ(n·p). (In any connected PV-graph, we have n≤k·p.) We also show some complementary optimal results in specific cases (same period for all carriers, highly connected PV-graphs). Finally this new ability allows the agent to completely map the PV-graph, in addition to just explore it.