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. 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. Distributed controller architectures have been proposed for Software-Defined Networking (SDN) to ensure scalability and reliability. One major drawback of the existing architectures is the uneven load distribution among controllers stemming from the static binding between controllers and switches. To address this issue, several existing studies introduce dynamic binding by adopting some switch migration mechanisms that re-associate switches from overloaded controllers to underutilized controllers. However, the migration process adds a considerable amount of complexity to the system and may incur significant network latency. In this paper, we propose BLAC, a novel BindingLess Architecture for distributed Controllers (BLAC), in which load balance is achieved with the help of the proposed scheduling layer, which intercepts flow requests from switches and dispatches them to different controllers as determined by selected scheduling algorithms. The process is proceeded transparently with no extra modification required for off-the-shelf SDN switches. Besides, the scheduling layer can flexibly support various scheduling algorithms and causes neither disruption of service nor significant network delay. We build a prototype that can work with various distributed controller systems and conduct experiments to demonstrate its efficacy. The results show that our design outperforms the static-binding controller system in terms of both system throughput and response time without the complexity of the dynamic-binding controller system. Virtualizing network functions is becoming a major trend in today's research on cloud computing. Among networking elements, the Home Gateway appears to be one with the most diverse functions to handle and thus, with great potential for virtualization. To this end, the paper proposes a solution to ease adoption by Service Providers of the latest breakthroughs in cloud computing technologies towards a virtualized Home Gateway. Although the NFV approach globally pretends bringing operational advantages in terms of CAPEX and OPEX, it is essential to prove them for Home Gateways scenarios where compatibility and versatility are strong requirements. To achieve this goal, we introduce the concept of Surrogate vNF, which makes Home Gateways NFV aware. The paper highlights a migration path towards full Home Gateway virtualization and proves its concept through a real implementation and a simulated evaluation on a practical use case related to video content distribution. Today's integrated circuit technologies allow the design of complete systems on a single chip which execute complex applications specified as a set of tasks. The tasks are managed by an Operating System whose main role consists in defining the resource allocation and the temporal scheduling. One of the main characteristics of these architectures is the heterogeneity of their execution resources which makes this scheduling complex. In this paper, we propose a neural network based model for the design of heterogeneous multiprocessor architectures scheduler. Previous works have shown that neural networks using the Hopfield model can be defined to schedule tasks on an homogeneous architecture. This approach was extended to take platform heterogeneity into account. The work presented in this paper is based on a new neural network structure using inhibitor neurons. These neurons allow to limit the number of additional neurons and the number of network re-initialisations to reach convergence. We compare our network to the classic solutions based on the Hopfield neural network. These comparisons show that the number of neurons is reduced by a factor of more than two, which reduces the time convergence. We also compare our approach in terms of number of migrations with the PFair algorithm which is known as an optimal solution in the context of homogeneous architecture. The results show that our solution significantly limit the number of task migrations. Finally, we present results in the context of heterogeneous multiprocessor architectures, which is representative of complex system-on-chip. Elastic scaling and load balancing with efficient switch migration are critical to enable the elasticity of software-defined networking (SDN) controllers, but learning how to improve migration efficiency remains a difficult problem. To address this issue, a switch migration-based decision-making (SMDM) scheme is put forward that could be made aware of the load imbalance by a switch migration trigger metric; the migration efficiency model for this scheme is built to make a tradeoff between migration costs and the load balance rate. An efficiency-aware switch migration algorithm based on greedy method is designed to utilize the migration efficiency model and thus guide the choice of possible migration actions. We implement a proof of the scheme and present a numerical evaluation using Mininet emulator to demonstrate the effectiveness of our proposal. A novel paradigm that changes the scene for the modern communication and computation systems is the Edge Computing. It is not a coincidence that terms like Mobile Cloud Computing, Cloudlets, Fog Computing, and Mobile-Edge Computing are gaining popularity both in academia and industry. In this paper, we embrace all these terms under the umbrella concept of “Edge Computing” to name the trend where computational infrastructures hence the services themselves are getting closer to the end user. However, we observe that bringing computational infrastructures to the proximity of the user does not magically solve all technical challenges. Moreover, it creates complexities of its own when not carefully handled. In this paper, these challenges are discussed in depth and categorically analyzed. As a solution direction, we propose that another major trend in networking, namely software-defined networking (SDN), should be taken into account. SDN, which is not proposed specifically for Edge Computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of Edge Computing be achieved. To fully demonstrate our ideas, initially, we put forward a clear collaboration model for the SDN-Edge Computing interaction through practical architectures and show that SDN related mechanisms can feasibly operate within the Edge Computing infrastructures. Then, we provide a detailed survey of the approaches that comprise the Edge Computing domain. A comparative discussion elaborates on where these technologies meet as well as how they differ. Later, we discuss the capabilities of SDN and align them with the technical shortcomings of Edge Computing implementations. We thoroughly investigate the possible modes of operation and interaction between the aforementioned technologies in all directions and technically deduce a set of “Benefit Areas” which is discussed in detail. Lastly, as SDN is an evolving technology, we give the future directions for enhancing the SDN development so that it can take this collaboration to a further level. Virtualization can deliver significant benefits for cloud computing by enabling VM migration to improve utilization, balance load and alleviate hotspots. While several mechanisms exist to migrate VMs, few efforts have focused on optimizing migration policies in a multi-rooted tree datacenter network. The general problem has multiple facets, two of which map to generalizations of well-studied problems: (1) Migration of VMs in a bandwidth-oversubscribed tree network generalizes the maximum multicommodity flow problem in a tree, and (2) Migrations must meet load constraints at the servers, mapping to variants of the matching problem --- generalized assignment and demand matching. While these problems have been individually studied, a new fundamental challenge is to simultaneously handle the packing constraints of server load and tree edge capacities. We give approximation algorithms for several versions of this problem, where the objective is to alleviate a maximal number of hot servers. In the full version of this work [5], we empirically demonstrate the effectiveness of these algorithms through large scale simulations on real data. Abstract The energy efficiency of wired networks has received considerable attention over the past decade due to its economic and environmental impacts. However, because of the vertical integration of the control and data planes in conventional networks, optimizing energy consumption in such networks is challenging. Software-defined networking (SDN) is an emerging networking paradigm that decouples the control plane from the data plane and introduces network programmability for the development of network applications. In this work, we propose an energy-aware integral flow-routing solution to improve the energy efficiency of the SDN routing application. We consider discreteness of link rates and pose the routing problem as a mixed integer linear programming (MILP) problem, which is known to be NP complete. The proposed solution is a heuristic implementation of the Benders decomposition method that routes additional single and multiple flows without resolving the routing problem. Performance evaluations demonstrate that the proposed solution achieves a close-to-optimal performance (within 3.27 error) compared to CPLEX on various topologies with less than 0.056 of CPLEX average computation time. Furthermore, our solution outperforms the shortest path algorithm by 24.12 to 54.35 in power savings. 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. Driven by the increasing number of connected vehicles and related services, powerful communication and computation capabilities are needed for vehicular communications, especially for real-time and safety-related applications. A cellular network consists of radio access technologies, including the current long-term evolution (LTE), the LTE advanced, and the forthcoming 5th generation mobile communication systems. It covers large areas and has the ability to provide high data rate and low latency communication services to mobile users. It is considered the most promising access technology to support real-time vehicular communications. Meanwhile, fog is an emerging architecture for computing, storage, and networking, in which fog nodes can be deployed at base stations to deliver cloud services close to vehicular users. In fog computing-enabled cellular networks, mobility is one of the most critical challenges for vehicular communications to maintain the service continuity and to satisfy the stringent service requirements, especially when the computing and storage resources are limited at the fog nodes. Service migration, relocating services from one fog server to another in a dynamic manner, has been proposed as an effective solution to the mobility problem. To support service migration, both computation and communication techniques need to be considered. Given the importance of protocol design to support the mobility of the vehicles and maintain high network performance, in this paper, we investigate the service migration in the fog computing-enabled cellular networks. We propose a quality-of-service aware scheme based on the existing handover procedures to support the real-time vehicular services. A case study based on a realistic vehicle mobility pattern for Luxembourg scenario is carried out, where the proposed scheme, as well as the benchmarks, are compared by analyzing latency and reliability as well as migration cost. 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. 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. Software-Defined Networking and Network Functions Virtualization have initiated a new landscape within the telecom market landscape. Initial proof-of-concept prototypes for NFV-enabled solutions are being developed at the same time SDN models are identified as the futures solutions within the telecom realm. In this article, we provide a brief overview of the application and state-of-the-art of SDN and NFV technologies over optical networks. At the same time, we provide the first formalisation model for the VNF complex scheduling problem, using the complex job formalisation. The article aims at being used as starting point in order to optimally solve the scheduling problem of virtual network functions that compose network services to be provisioned within the SDN paradigm. Finally, we also provide an example of the virtualization of the routing function over an SDN-enabled domain. Optical transport networks typically deploy dynamic restoration mechanisms in order to automatically recover optical connections disrupted by network failures. Elastic optical networks (EONs), currently emerging as the next-generation technology to be adopted in optical transport, introduce new challenges for traditional generic multiprotocol label-switching (GMPLS)-based restoration that may seriously impact the achievable recovery time. At the same time, the software-defined networking (SDN) framework is emerging as an alternative control plane. It is therefore important to investigate possible benefits provided by SDN in the implementation of restoration mechanisms for EONs. This paper proposes a dynamic restoration scheme for EONs based on the SDN framework. The proposed scheme contemporarily exploits centralized path computation and node configuration to avoid contentions during the recovery procedure with the final aim of minimizing the recovery time. The performance of the proposed scheme is evaluated by means of simulations in terms of recovery time and restoration blocking probability and compared against three reference schemes based on GMPLS and SDN. To cope with the explosive traffic demands and limited capacity provided by the current cellular networks, Delay Tolerant Networking (DTN) is used to migrate traffic from the cellular networks to the free and high capacity device-to-device networks. The current DTN-based mobile data offloading models do not address the heterogeneity of mobile traffic and are based on simple network assumptions. In this paper, we establish a mathematical framework to study the problem of multiple mobile data offloading under realistic network assumptions, where 1) mobile data is heterogeneous in terms of size and lifetime, 2) mobile users have different data subscribing interests, and 3) the storage of offloading helpers is limited. We formulate the maximum mobile data offloading as a Submodular Function Maximization problem with multiple linear constraints of limited storage and propose greedy, approximated and optimal algorithms for different offloading scenarios. We show that our algorithms can effectively offload data to DTNs by extensive simulations which employ real traces of both humans and vehicles. Network virtualization is a powerful way to run multiple architectures or experiments simultaneously on a shared infrastructure. However, making efficient use of the underlying resources requires effective techniques for virtual network embedding--mapping each virtual network to specific nodes and links in the substrate network. Since the general embedding problem is computationally intractable, past research restricted the problem space to allow efficient solutions, or focused on designing heuristic algorithms. In this paper, we advocate a different approach: rethinking the design of the substrate network to enable simpler embedding algorithms and more efficient use of resources, without restricting the problem space. In particular, we simplify virtual link embedding by: i) allowing the substrate network to split a virtual link over multiple substrate paths and ii) employing path migration to periodically re-optimize the utilization of the substrate network. We also explore node-mapping algorithms that are customized to common classes of virtual-network topologies. Our simulation experiments show that path splitting, path migration,and customized embedding algorithms enable a substrate network to satisfy a much larger mix of virtual networks Getting new security features and protocols to be widely adopted and deployed in the Internet has been a continuing challenge. There are several reasons for this, in particular economic reasons arising from the presence of network externalities. Indeed, like the Internet itself, the technologies to secure it exhibit network effects: their value to individual users changes as other users decide to adopt them or not. In particular, the benefits felt by early adopters of security solutions might fall significantly below the cost of adoption, making it difficult for those solutions to gain attraction and get deployed at a large scale. Our goal in this paper is to model and quantify the impact of such externalities on the adoptability and deployment of security features and protocols in the Internet. We study a network of interconnected agents, which are subject to epidemic risks such as those caused by propagating viruses and worms, and which can decide whether or not to invest some amount to deploy security solutions. Agents experience negative externalities from other agents, as the risks faced by an agent depend not only on the choices of that agent (whether or not to invest in self-protection), but also on those of the other agents. Expectations about choices made by other agents then influence investments in self-protection, resulting in a possibly suboptimal outcome overall. We present and solve an analytical model where the agents are connected according to a variety of network topologies. Borrowing ideas and techniques used in statistical physics, we derive analytic solutions for sparse random graphs, for which we obtain asymptotic results. We show that we can explicitly identify the impact of network externalities on the adoptability and deployment of security features. In other words, we identify both the economic and network properties that determine the adoption of security technologies. Therefore, we expect our results to provide useful guidance for the design of new economic mechanisms and for the development of network protocols likely to be deployed at a large scale. While early emphasis of Infrastructure as a Service (IaaS) clouds was on providing resource elasticity to end users, providers are increasingly interested in over-committing their resources to maximize the utilization and returns of their capital investments. In principle, over-committing resources hedges that users — on average — only need a small portion of their leased resources. When such hedge fails (i.e., resource demand far exceeds available physical capacity), providers must mitigate this provider-induced overload, typically by migrating virtual machines (VMs) to underutilized physical machines. Recent works on VM placement and migration assume the availability of target physical machines [1], [2]. However, in an over-committed cloud data center, this is not the case. VM migration can even trigger cascading overloads if performed haphazardly. In this paper, we design a new VM migration algorithm (called Scattered) that minimizes VM migrations in over-committed data centers. Compared to a traditional implementation, our algorithm can balance host utilization across all time epochs. Using real-world data traces from an enterprise cloud, we show that our migration algorithm reduces the risk of overload, minimizes the number of needed migrations, and has minimal impact on communication cost between VMs. Wireless networks have evolved from 1G to 4G networks, allowing smart devices to become important tools in daily life. The 5G network is a revolutionary technology that can change consumers' Internet use habits, as it creates a truly wireless environment. It is faster, with better quality, and is more secure. Most importantly, users can truly use network services anytime, anywhere. With increasing demand, the use of bandwidth and frequency spectrum resources is beyond expectations. This paper found that the frequency spectrum and network information have considerable relevance; thus, spectrum utilization and channel flow interactions should be simultaneously considered. We considered that software defined radio (SDR) and software defined networks (SDNs) are the best solution. We propose a cross-layer architecture combining SDR and SDN characteristics. As the simulation evaluation results suggest, the proposed architecture can effectively use the frequency spectrum and considerably enhance network performance. Based on the results, suggestions are proposed for follow-up studies on the proposed architecture.