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
1977145679 2056646448 1992737336 2951655197 135016661 2092340542 2557890845 2600324614 2792336262 2962948535 2001761119 2033020629 1889296357 2583260348 2170178059 2963492437 2169599358 2110932231 2164571150 2498207281
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. 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. This paper is concerned with the page migration (or file migration) problem (Black and Sleator, Technical Report CMU-CS-89-201, Department of Computer Science, Carnegie-Mellon University, 1989) as part of a large class of on-line problems. The page migration problem deals with the management of pages residing in a network of processors. In the classical problem there is only one copy of each page which is accessed by different processors over time. The page is allowed to be migrated between processors. However a migration incurs higher communication cost than an access (proportionally to the page size). The problem is that of deciding when and where to migrate the page in order to lower access costs. A more general setting is the k-page migration problem where we wish to maintain k copies of the page. The page migration problems are concerned with a dilemma common to many on-line problems: determining when it is beneficial to make configuration changes. We deal with the relaxed task systems model which captures a large class of problems of this type, that can be described as the generalization of some original task system problem (, J. ACM 39(4) (1992) 745-763). Given a c-competitive algorithm for a task system we show how to obtain a deterministic O(c2) and randomized O(c) competitive algorithms for the corresponding relaxed task system. The result implies deterministic algorithms for k-page migration by using k-server (, J. Algorithms 11(2) (1990) 208-230) algorithms, and for network leasing by using generalized Steiner tree algorithms (, Proc 7th Ann. ACM-SIAM Symp. on Discrete Algorithms, January 1996, pp. 68-74), as well as providing solutions for natural generalizations of other problems (e.g. storage rearrangement (, Proc. 36th Ann. IEEE Symp. on Foundations of Computer Science, October 1995, pp. 392-403)). We further study some special cases of the k-page migration problem and get optimal deterministic algorithms. For the classical page migration problem we present a deterministic algorithm that achieves a competitive ratio of 4:086, improving upon the previously best competitive ratio of 7 (, Proc. 25th ACM Symp. on Theory of Computing, May 1993, pp. 164-173). (The current lower bound on the problem is 3:148 (, J. Algorithms 24(1) (1997) 124-157). Copyright 2001 Elsevier Science B.V. 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. 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 key challenge in network virtualization is to efficiently map a virtual network (VN) on a substrate network (SN), while accounting for possible substrate failures. This is known as the survivable VN embedding (SVNE) problem. The state-of-the-art literature has studied the SVNE problem from infrastructure providers’ (InPs’) perspective, i.e., provisioning backup resources in the SN. A rather unexplored solution spectrum is to augment the VN with sufficient spare backup capacity to survive substrate failures and embed the resulting VN accordingly. Such augmentation enables InPs to offload failure recovery decisions to the VN operator, thus providing more flexible VN management. In this paper, we study the problem of jointly optimizing spare capacity allocation in a VN and embedding the VN to guarantee full bandwidth in the presence of multiple substrate link failures. We formulate the optimal solution to this problem as a quadratic integer program that we transform into an integer linear program. We also propose a heuristic algorithm to solve larger instances of the problem. Based on analytical study and simulation, our key findings are: 1) provisioning shared backup resources in the VN can yield 33 more resource efficient embedding compared to doing the same at the SN level and 2) our heuristic allocates 21 extra resources compared to the optimal, while executing several orders of magnitude faster. Service function chaining (SFC) allows the forwarding of traffic flows along a chain of virtual network functions (VNFs). Software defined networking (SDN) solutions can be used to support SFC to reduce both the management complexity and the operational costs. One of the most critical issues for the service and network providers is the reduction of energy consumption, which should be achieved without impacting the Quality of Service. In this paper, we propose a novel resource allocation architecture which enables energy-aware SFC for SDN-based networks, considering also constraints on delay, link utilization, server utilization. To this end, we formulate the problems of VNF placement, allocation of VNFs to flows, and flow routing as integer linear programming (ILP) optimization problems. Since the formulated problems cannot be solved (using ILP solvers) in acceptable timescales for realistic problem dimensions, we design a set of heuristic to find near-optimal solutions in timescales suitable for practical applications. We numerically evaluate the performance of the proposed algorithms over a real-world topology under various network traffic patterns. Our results confirm that the proposed heuristic algorithms provide near-optimal solutions (at most 14 optimality-gap) while their execution time makes them usable for real-life networks. This paper studies the coverage problem in an unknown environment by a Mobile Sensor Network (MSN). Each agent in the MSN has sensing, communication, computation and moving capabilities to complete sensing tasks. Here the agents need to relocate themselves, from their initial random locations, to their optimal configuration. The proposed algorithm is based on game theory control where a collection of distributed agents use their local information to make decisions. A state-based potential game is defined in which each agent's utility function is designed to consider the trade off between the worth of the covered area and the energy consumption. The agents employ binary log-linear learning to update their actions in each iteration in order to converge to the Nash equilibrium. As the agents do not have the knowledge of the sensing area, a Maximum Likelihood estimation scheme is used to estimate the unknown parameters of a Gaussian Mixture Model (GMM). Then in order to feed the estimation algorithm with more informative data, a mutual information term is added to the agents' utility functions. The mutual information is utilized to determine which observation can improve the agent's knowledge of the unobserved area more. Simulation results are provided to verify the performance of the proposed algorithm. Analytical models of IEEE 802.11-based WLANs are invariably based on approximations, such as the well-known mean-field approximations proposed by Bianchi for saturated nodes. In this paper, we provide a new approach for modeling the situation when the nodes are not saturated. We study a State Dependent Attempt Rate (SDAR) approximation to model M queues (one queue per node) served by the CSMA CA protocol as standardized in the IEEE 802.11 DCF. The approximation is that, when n of the M queues are non-empty, the attempt probability of the n non-empty nodes is given by the long-term attempt probability of n saturated nodes as provided by Bianchi's model. This yields a coupled queue system. When packets arrive to the M queues according to independent Poisson processes, we provide an exact model for the coupled queue system with SDAR service. The main contribution of this paper is to provide an analysis of the coupled queue process by studying a lower dimensional process and by introducing a certain conditional independence approximation. We show that the numerical results obtained from our finite buffer analysis are in excellent agreement with the corresponding results obtained from ns-2 simulations. We replace the CSMA CA protocol as implemented in the ns-2 simulator with the SDAR service model to show that the SDAR approximation provides an accurate model for the CSMA CA protocol. We also report the simulation speed-ups thus obtained by our model-based simulation. In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well. This paper proposes a group-based framework with dedicated path planning for emergency guiding based on Internet of Things (IoT) technologies. The proposed framework can model the spatiotemporal mobility of indoor people to determine and relieve the congestion of corridors and exits. A dedicated path can be determined to provide the shortest evacuation time for each group of nearby people. The corridor and exit capacities, corridor lengths, clustering motion of a group, concurrent moving of different groups, and up-to-date distribution of group people are considered together to accurately estimate the evacuation time for each group. Based on the estimated evacuation time, evacuation load can be evenly distributed among corridors and exits to alleviate the congestion of all corridors and exits for minimizing total evacuation time. The performance of the proposed framework is evaluated by conducting mathematical analysis and computer simulations, which outperforms existing schemes and can achieve the shortest evacuation time for group-based emergency guiding. In addition, an Android-based prototype with indoor IoT localization technologies is implemented to verify the feasibility of our framework. This paper considers jointly optimal design of crosslayer congestion control, routing and scheduling for ad hoc wireless networks. We first formulate the rate constraint and scheduling constraint using multicommodity flow variables, and formulate resource allocation in networks with fixed wireless channels (or single-rate wireless devices that can mask channel variations) as a utility maximization problem with these constraints. By dual decomposition, the resource allocation problem naturally decomposes into three subproblems: congestion control, routing and scheduling that interact through congestion price. The global convergence property of this algorithm is proved. We next extend the dual algorithm to handle networks with timevarying channels and adaptive multi-rate devices. The stability of the resulting system is established, and its performance is characterized with respect to an ideal reference system which has the best feasible rate region at link layer. We then generalize the aforementioned results to a general model of queueing network served by a set of interdependent parallel servers with time-varying service capabilities, which models many design problems in communication networks. We show that for a general convex optimization problem where a subset of variables lie in a polytope and the rest in a convex set, the dual-based algorithm remains stable and optimal when the constraint set is modulated by an irreducible finite-state Markov chain. This paper thus presents a step toward a systematic way to carry out cross-layer design in the framework of “layering as optimization decomposition” for time-varying channel models. In Software Defined Networks (SDN), intensive traffic monitoring is used to optimize the Quality-of-Service (QoS) of the network paths which are selected. Thus, we introduce the use of the Cognitive Packet Network (CPN) algorithm to SDN in order to optimize the search for new high-QoS paths. We install the CPN algorithm in the Cognitive Routing Engine (CRE), a new application software for SDN, and show that with limited monitoring overhead we are able to determine the near-optimal paths for given QoS metrics that may be proposed by the end users. Measurements that we have conducted on an experimental replica of the GEANT network that our approach uses close to 10 times less monitoring data than conventional SDN, but that we are able to approach the optimal paths within 2 . 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. This paper describes the basic concepts of the network migration problem, as well as its cost optimal solution with an ant colony algorithm. Generally there are many possible approaches to calculate network migration strategies. One of those is to consider the migration as a Shortest Path Problem, which can be solved using meta heuristics. In this paper it will be analyzed how ant colony optimization can be used to calculate migration sequences with minimized costs. Results of our investigation are information about the speed of convergence of ant colony algorithms towards an optimal cost value. The used resource utilization to cover increasing network demands will be presented also. One central issue in practically deploying network coding is the adaptive and economic allocation of network resource. We cast this as an optimization, where the net-utility-the difference between a utility derived from the attainable multicast throughput and the total cost of resource provisioning-is maximized. By employing the MAX of flows characterization of the admissible rate region for multicasting, this paper gives a novel reformulation of the optimization problem, which has a separable structure. The Lagrangian relaxation method is applied to decompose the problem into subproblems involving one destination each. Our specific formulation of the primal problem results in two key properties. First, the resulting subproblem after decomposition amounts to the problem of finding a shortest path from the source to each destination. Second, assuming the net-utility function is strictly concave, our proposed method enables a near-optimal primal variable to be uniquely recovered from a near-optimal dual variable. A numerical robustness analysis of the primal recovery method is also conducted. For ill-conditioned problems that arise, for instance, when the cost functions are linear, we propose to use the proximal method, which solves a sequence of well-conditioned problems obtained from the original problem by adding quadratic regularization terms. Furthermore, the simulation results confirm the numerical robustness of the proposed algorithms. Finally, the proximal method and the dual subgradient method can be naturally extended to provide an effective solution for applications with multiple multicast sessions Due to the growing demand of cloud services, allocation of energy efficient resources (CPU, memory, storage, etc.) and resources utilization are the major challenging issues of a large cloud data center. In this paper, we propose an Euclidean distance based multi-objective resources allocation in the form of virtual machines (VMs) and designed the VM migration policy at the data center. Further the allocation of VMs to Physical Machines (PMs) is carried out by our proposed hybrid approach of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) referred to as HGAPSO. The proposed HGAPSO based resources allocation and VMs migration not only saves the energy consumption and minimizes the wastage of resources but also avoids SLA violation at the cloud data center. To check the performance of the proposed HGAPSO algorithm and VMs migration technique in the form of energy consumption, resources utilization and SLA violation, we performed the extended amount of experiment in both heterogeneous and homogeneous data center environments. To check the performance of proposed HGAPSO with VM migration, we compared our proposed work with branch-and-bound based exact algorithm. The experimental results show the superiority of HGAPSO and VMs migration technique over exact algorithm in terms of energy efficiency, optimal resources utilization, and SLA violation.