Edge computing offers computing capability with ultra-low response times by leveraging servers close to end-user devices. Due to the mobility of end-user devices, the latency between the servers and the end-user devices can become long and the response time might become unacceptable for an application service. Service (container) migration that follows the handover of end-user devices retains the response time. Service migration following the mass movement of people in the same geographic area and at the same time due to an event (e.g., commuting) generates heavy bandwidth usage in the mobile backhaul network. Heavy usage by service migration reduces available bandwidth for ordinary application traffic in the network. Shaping the migration traffic limits the bandwidth usage while delaying service migration and increasing the response time of the container for the moving end-user device. Furthermore, targets of migration decisions increase (i.e., the system load) because delaying a migration process accumulates containers waiting for migration. In this paper, we propose a migration scheduling method to control bandwidth usage for migration in a network and ensure timely processing of service migration. Simulations that compare the proposal with state-of-the-art methods show that the proposal always suppresses the bandwidth usage under the predetermined threshold. The method reduced the number of containers exceeding the acceptable response time up to 40% of the compared state-of-the-art methods. Furthermore, the proposed method minimized the targets of migration decisions.

This paper introduces robust optimization models for minimization of the network congestion ratio that can handle the fluctuation in traffic demands between nodes. The simplest and widely used model to minimize the congestion ratio, called the pipe model, is based on precisely specified traffic demands. However, in practice, network operators are often unable to estimate exact traffic demands as they can fluctuate due to unpredictable factors. To overcome this weakness, we apply robust optimization to the problem of minimizing the network congestion ratio. First, we review existing models as robust counterparts of certain uncertainty sets. Then we consider robust optimization assuming ellipsoidal uncertainty sets, and derive a tractable optimization problem in the form of second-order cone programming (SOCP). Furthermore, we take uncertainty sets to be the intersection of ellipsoid and polyhedral sets, and considering the mirror subproblems inherent in the models, obtain tractable optimization problems, again in SOCP form. Compared to the previous model that assumes an error interval on each coordinate, our models have the advantage of being able to cope with the total amount of errors by setting a parameter that determines the volume of the ellipsoid. We perform numerical experiments to compare our SOCP models with the existing models which are formulated as linear programming problems. The results demonstrate the relevance of our models in terms of congestion ratio and computation time.

In general, the sensor network has a many-to-one communication architecture wherein each node transmits its data to a sink. This leads the congested nodes to die early and nodes nears the sink suffer from significant traffic concentrations. In this paper, we propose a cross-layer based routing and MAC protocol which is compatible with the IEEE 802.15.4 standard without additional overhead. The key mechanism is to provide dynamic route discovery and route maintenance operations to avoid and mitigate the most congested nodes by monitoring link status such as link delay, buffer occupancy and residential energy. In addition, the proposed protocol also provides a dynamic tuning of BE (Binary Exponent) and frame retransmission opportunities according to the hop distance to the sink node to mitigate funnel effects. We conducted simulations, verifying the performance over existing protocols.

This paper compares the performances of optimal routing as yielded by the pipe, hose, and intermediate models. The pipe model, which is specified by the exact traffic matrix, provides the best routing performance, but the traffic matrix is difficult to measure and predict accurately. On the other hand, the hose model is specified by just the total outgoing/incoming traffic from/to each node, but it has a problem in that its routing performance is degraded compared to the pipe model, due to insufficient traffic information. The intermediate model, where the upper and lower bounds of traffic demands for source-destination pairs are added as constraints, is a construction that lies between the pipe and hose models. The intermediate model, which lightens the difficulty of the pipe model, but narrows the range of traffic conditions specified by the hose model, offers better routing performance than the hose model. An optimal-routing formulation extended from the pipe model to the intermediate model can not be solved as a regular linear programming (LP) problem. Our solution, the introduction of a duality theorem, turns our problem into an LP formulation that can be easily solved. Numerical results show that the network congestion ratio for the pipe model is much lower than that of hose model. The differences in network congestion ratios between the pipe and hose models lie in the range from 27% to 45% for the various network topologies examined. The intermediate model offers better routing performance than the hose model. The intermediate model reduces the network congestion ratio by 34% compared to the hose model in an experimental network, when the upper-bound and lower-bound margins are set to 25% and 20%, respectively.

This letter presents the optimal routing by the intermediate model; a construction that lies between the pipe and hose models. We show that it is a practical way of realizing optimal routing. A formulation extended from the pipe model to the intermediate model can not be solved as a regular linear programming (LP) problem. Our solution, the introduction of a duality theorem, successfully turns our problem into an LP formulation that can be easily solved. Numerical results show that the intermediate model has better routing performance than the hose model.

As use of the Internet continues to spread rapidly, Traffic Engineering (TE) is needed to optimize IP network resource utilization. In particular, load balancing with TE can prevent traffic concentration on a single path between ingress and egress routers. To apply TE, we have constructed an MPLS (Multi-Protocol Label Switching) network with TE capability in the JGN (Japan Gigabit Network), and evaluated dynamic load balancing behavior in it from the viewpoint of control stability. We confirmed that with this method, setting appropriate control parameter values enables traffic to be equally distributed over two or more routes in an actual large-scale network. In addition, we verified the method's effectiveness by using a digital cinema application as input traffic.

In the Internet, flow analysis and network monitoring have been studied by various methods. Some methods try to make TCP (Transport Control Protocol) traces more readable by showing them graphically. Others such as MRTG, NetScope, and NetFlow read the traffic counters of the routers and record the data for traffic engineering. Even if all of the above methods are useful, they are made only to perform a single task. This paper describes an improved TCP Protocol Machine, a multipurpose tool that can be used for flow analysis, intrusion detection and link congestion monitoring. It is developed based on a finite state machine (automaton). The machine separates the flows into two main groups. If a flow can be mapped to a set of input symbols of the automaton, it is valid, otherwise it is invalid. It can be observed that intruders' attacks are easily detected by the use of the protocol machine. Also link congestion can be monitored, by measuring the percentage of valid flows to the total number of flows. We demonstrate the capability of this tool through measurement and working examples.