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.
This paper extends our recent studies on network migration. In @cite_5 , we proposed an agent-based model to study benefits of joint migration to multiple technologies, on a case study of Path Computation Element (PCE) and SDN. In @cite_7 , we defined the SDN , and found optimal solution. In this paper, however, our focus is entirely on devising computationally inexpensive but effective heuristics to schedule migration to a single technology, with consideration of techno-economic factors, such as limitations on CapEx investment. To this end, we propose novel greedy algorithms and establish their practical impact in terms of computational complexity, run times, network capacity gains, etc.
@cite_5 @cite_7
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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. Today's trend toward network virtualization and software-defined networking enables flexible new distributed systems where resources can be dynamically allocated and migrated to locations where they are most useful. This paper proposes a competitive analysis approach to design and reason about online algorithms that find a good tradeoff between the benefits and costs of a migratable service. A competitive online algorithm provides worst-case performance guarantees under any demand dynamics, and without any information or statistical assumptions on the demand in the future. This is attractive especially in scenarios where the demand is hard to predict and can be subject to unexpected events. As a case study, we describe a service (e.g., an SAP server or a gaming application) that uses network virtualization to improve the quality of service (QoS) experienced by thin client applications running on mobile devices. By decoupling the service from the underlying resource infrastructure, it can be migrated closer to the current client locations while taking into account migration costs. We identify the major cost factors in such a system and formalize the wide-area service migration problem. Our main contributions are a randomized and a deterministic online algorithm that achieve a competitive ratio of @math in a simplified scenario, where @math is the size of the substrate network. This is almost optimal. We complement our worst-case analysis with simulations in different specific scenarios and also sketch a migration demonstrator. In this paper, we analyze the question of network migration to Software Defined Networking (SDN) from the perspective of Traffic Engineering (TE). For a given network topology and migration planning horizon, we ask the question of which routers in the IP network should migrate to SDN-enabled operation to reduce the need for network capacity upgrades over a given time horizon. We propose an algorithm to determine the optimum schedule for node substitution within a network, which shows that already a few SDN routers, when strategically located, provide a stably large number of path alternatives to be used in TE, thus substantially reducing the need for large network capacity upgrades. We study the dynamic service migration problem in mobile edge-clouds that host cloud-based services at the network edge. This offers the benefits of reduction in network overhead and latency but requires service migrations as user locations change over time. It is challenging to make these decisions in an optimal manner because of the uncertainty in node mobility as well as possible non-linearity of the migration and transmission costs. In this paper, we formulate a sequential decision making problem for service migration using the framework of Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal policy, we approximate the underlying state space by the distance between the user and service locations. We show that the resulting MDP is exact for uniform one-dimensional mobility while it provides a close approximation for uniform two-dimensional mobility with a constant additive error term. We also propose a new algorithm and a numerical technique for computing the optimal solution which is significantly faster in computation than traditional methods based on value or policy iteration. We illustrate the effectiveness of our approach by simulation using real-world mobility traces of taxis in San Francisco.