This paper presents a distributed server allocation model with preventive start-time optimization against a single server failure. The presented model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We prove the NP-completeness of the considered problem. As the number of users and that of servers increase, the size of ILP problem increases; the computation time to solve the ILP problem becomes excessively large. We develop a heuristic approach that applies simulated annealing and the ILP approach in a hybrid manner to obtain the solution. Numerical results reveal that the developed heuristic approach reduces the computation time by 26% compared to the ILP approach while increasing the largest maximum delay by just 3.4% in average. It reduces the largest maximum delay compared with the start-time optimization model; it avoids the instability caused by the unnecessary disconnection permitted by the run-time optimization model.

The energy consumption of the Internet has a huge impact on the world economy and it is likely to increase every year. In present backbone networks, pairs of nodes are connected by “bundles” of multiple physical cables that form one logical link and energy saving can be achieved by shutting down unused network resources. The hose model can support traffic demand variations among node pairs in different time periods because it accommodates multiple traffic matrices unlike the pipe model which supports only one traffic matrix. This paper proposes an OSPF (Open Shortest Path First) link weight optimization scheme to reduce the network resources used for the hose model considering single link failures. The proposed scheme employs a heuristic algorithm based on simulated annealing to determine a suitable set of link weights to reduce the worst-case total network resources used, and considering any single link failure preemptively. It efficiently selects the worst-case performance link-failure topology and searches for a link weight set that reduces the worst-case total network resources used. Numerical results show that the proposed scheme is more effective in the reduction of worst-case total network resources used than the conventional schemes, Start-time Optimization and minimum hop routing.

This paper proposes a scalable active optical access network using high-speed Plumbum Lanthanum Zirconate Titanate (PLZT) optical switch/splitter. The Active Optical Network, called ActiON, using PLZT switching technology has been presented to increase the number of subscribers and the maximum transmission distance, compared to the Passive Optical Network (PON). ActiON supports the multicast slot allocation realized by running the PLZT switch elements in the splitter mode, which forces the switch to behave as an optical splitter. However, the previous ActiON creates a tradeoff between the network scalability and the power loss experienced by the optical signal to each user. It does not use the optical power efficiently because the optical power is simply divided into 0.5 to 0.5 without considering transmission distance from OLT to each ONU. The proposed network adopts PLZT switch elements in the variable splitter mode, which controls the split ratio of the optical power considering the transmission distance from OLT to each ONU, in addition to PLZT switch elements in existing two modes, the switching mode and the splitter mode. The proposed network introduces the flexible multicast slot allocation according to the transmission distance from OLT to each user and the number of required users using three modes, while keeping the advantages of ActiON, which are to support scalable and secure access services. Numerical results show that the proposed network dramatically reduces the required number of slots and supports high bandwidth efficiency services and extends the coverage of access network, compared to the previous ActiON, and the required computation time for selecting multicast users is less than 30 msec, which is acceptable for on-demand broadcast services.

This paper proposes a migration model for distributed server allocation. In distributed server allocation, each user is assigned to a server to minimize the communication delay. In the conventional model, a user cannot migrate to another server to avoid instability. We develop a model where each user can migrate to another server while receiving services. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-complete. We introduce a heuristic algorithm. Numerical result shows that the proposed model reduces the average communication delay by 59% compared to the conventional model at most.

This letter proposes a hierarchical label-switched path (LSP) setup scheme, called ConSet, for multi-layer generalized multi-protocol label switching (GMPLS) networks. ConSet allows a Path message to be transmitted to the downstream neighbor node without waiting for the establishment of the higher-order LSP. Confirmation of the establishment of the higher-order LSP is performed at the ingress node of the higher-order LSP before a Resv message of the lower-order LSP is transmitted to the upstream neighbor node. ConSet is able to set up hierarchical LSPs faster than the sequential scheme.

This paper proposes an algorithm for calculating routes that considers the include route constraint while minimizing cost. A route with include route constraint has to traverse a group of assigned nodes. The trouble when calculating a route that satisfies an include route constraint is that routes set in different sections may traverse the same link. In order to prevent this violation (overlap), we introduce an alternate route selection policy. Numerical results show that the probability of finding appropriate routes (no overlap) is more than 95% with the proposed algorithm while only 35% with the conventional algorithm.

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.

This paper proposes a robust optimization model for probabilistic protection under uncertain capacity demands to minimize the total required capacity against multiple simultaneous failures of physical machines. The proposed model determines both primary and backup virtual machine allocations simultaneously under the probabilistic protection guarantee. To express the uncertainty of capacity demands, we introduce an uncertainty set that considers the upper bound of the total demand and the upper and lower bounds of each demand. The robust optimization technique is applied to the optimization model to deal with two uncertainties: failure event and capacity demand. With this technique, the model is formulated as a mixed integer linear programming (MILP) problem. To solve larger sized problems, a simulated annealing (SA) heuristic is introduced. In SA, we obtain the capacity demands by solving maximum flow problems. Numerical results show that our proposed model reduces the total required capacity compared with the conventional model by determining both primary and backup virtual machine allocations simultaneously. We also compare the results of MILP, SA, and a baseline greedy algorithm. For a larger sized problem, we obtain approximate solutions in a practical time by using SA and the greedy algorithm.

This proposes a scalable QoS control scheme, called Elephant Flow Control Scheme (EFCS) for high-speed large-capacity networks; it controls congestion and provides appropriate bandwidth to normal users' flows by controlling just the elephant flows. EFCS introduces a sampling packet threshold and drops packets considering flow size. EFCS also adopts a compensation parameter to control elephant flows to an appropriate level. Numerical results show that the sampling threshold increases control accuracy by 20% while reducing the amount of memory needed for packet sampling by 60% amount of memory by packet sampling; the elephant flows are controlled as intended by the compensation parameter. As a result, EFCS provides sufficient bandwidth to normal TCP flows in a scalable manner.

This paper proposes the Path Computation Element (PCE)-based backbone network architecture and verifies its feasibility through implementation and experiments. PCE communication Protocol (PCEP) is implemented for communication between the PCE and the management system to control and manage Generalized Multi-Protocol Label Switching (GMPLS)-based backbone networks.

Generalized Multi-Protocol Label Switching (GMPLS) is being developed in the Internet Engineering Task Force (IETF). In GMPLS-based wavelength-division-multiplexing (WDM) optical networks, a wavelength in a fiber is used as a label. In the existing GMPLS signaling protocol for bidirectional paths in WDM networks with the wavelength continuity constraint, bidirectional path setup fails with high probability because the upstream label allocated by the previous hop node may not be accepted at the transit node. To solve this problem, this paper proposes an efficient bidirectional label switched path (LSP) setup scheme based on an upstream label set. Called the Upstream Label Set (ULS) scheme, it is an extension of the existing GMPLS signaling protocol. The ULS scheme is consistent with the existing GMPLS signaling procedure and so offers backward compatibility. The numerical results suggest that when the number of the LSP setup retries is limited, the ULS scheme offers lower blocking probability than the existing GMPLS signaling scheme which uses only with the upstream label (UL). In addition, under the condition that the constraint of the number of LSP setup retries is relaxed, the LSP setup time of the ULS scheme is faster than that of the existing scheme. Furthermore, by using our developed prototype of the GMPLS control system, in which the ULS scheme was installed, we demonstrated that the ULS scheme successfully setup bidirectional LSPs.

We survey traffic matrix models, whose elements represent the traffic demand between source-destination pair nodes. Modeling the traffic matrix is useful for multilayer Traffic Engineering (TE) in IP optical networks. Multilayer TE techniques make the network so designed flexible and reliable. This is because it allows reconfiguration of the virtual network topology (VNT), which consists of a set of several lower-layer (optical) paths and is provided to the higher layer, in response to fluctuations (diurnal) in traffic demand. It is, therefore, important to synthetically generate traffic matrices as close to the real ones as possible to maximize the performance of multilayer TE. We compare several models and clarify their applicability to VNT design and control. We find that it is difficult in practice to make an accurate traffic matrix with conventional schemes because of the high cost for data measurement and the complicated calculations involved. To overcome these problems, we newly introduce a simplified traffic matrix model that is practical; it well mirrors real networks. Next, this paper presents our developed server, the IP Optical TE server. It performs multilayer TE in IP optical networks. We evaluate the effectiveness of multilayer TE using our developed IP Optical server and the simplified traffic matrix. We confirm that multilayer TE offers significant CAPEX savings. Similarly, we demonstrate basic traffic control in IP optical networks, and confirm the dynamic control of the network and the feasibility of the IP Optical TE server.

Connection setup on various computer networks is now achieved by GMPLS. This technology is based on the source-routing approach, which requires the source node to store metric information of the entire network prior to computing a route. Thus all metric information must be distributed to all network nodes and kept up-to-date. However, as metric information become more diverse and generalized, it is hard to update all information due to the huge update overhead. Emerging network services and applications require the network to support diverse metrics for achieving various communication qualities. Increasing the number of metrics supported by the network causes excessive processing of metric update messages. To reduce the number of metric update messages, another scheme is required. This paper proposes a connection setup scheme that uses flooding-based signaling rather than the distribution of metric information. The proposed scheme requires only flooding of signaling messages with requested metric information, no routing protocol is required. Evaluations confirm that the proposed scheme achieves connection establishment without excessive overhead. Our analysis shows that the proposed scheme greatly reduces the number of control messages compared to the conventional scheme, while their blocking probabilities are comparable.

This article proposes active-ATM, a flexible, simple and cost-effective ATM-WAN architecture that can handle multiple user-customized ATM-layer protocols, such as ABR and ABT, by using a simple universal ATM transit network. The proposed active-ATM architecture enables the construction of flexible networks that can evolve easily. With active-ATM and the ATM multi-protocol emulation network architecture called ALPEN, it is easy to implement new ATM-layer protocols by using user-created programs called active-program capsules that modify only the edge nodes. Because these user-sent program capsules can be used to quickly customize the edge nodes, there is no waiting for standardization and implementation of new services. The ATM-layer protocols are emulated only at the edge nodes, making the transit network independent of customer ATM-layer protocols. The active-ATM edge node is based on the flexible programmable node architecture called PUN(programmable unified node). The PUN is a platform for user-programmable ATM-layer services; it is achieved by using programmable devices, such as FPGAs and DSPs. An prototype system has demonstrated the flexibility of the resulting ATM network. The active-ATM architecture is an efficient approach to implementing multimedia, multi-protocol ATM services in an ATM WAN.

A call admission control (CAC) scheme based on statistical information is proposed, called the statistical CAC scheme. A conventional scheme needs to manage session information for each link to update the residual bandwidth of a network in real time. This scheme has a scalability problem in terms of network size. The statistical CAC rejects session setup requests in accordance to a pre-computed ratio, called the rejection ratio. The rejection ratio is computed by using statistical information about the bandwidth requested for each link so that the congestion probability is less than an upper bound specified by a network operator. The statistical CAC is more scalable in terms of network size than the conventional scheme because it does not need to keep accommodated session state information. Numerical results show that the statistical CAC, even without exact session state information, only slightly degrades network utilization compared with the conventional scheme.

One of the features of a software-defined network (SDN) is a logically centralized control plane hosting one or more SDN controllers. As SDN controller placement can impact network performance, it is widely studied as the controller placement problem (CPP). For a cost-effective network design, network providers need to minimize the number of SDN controllers used in the network since each SDN controller incurs installation and maintenance costs. Moreover, the network providers need to deal with the failure of SDN controllers. Existing studies that consider SDN controller failures use the scheme of connecting each SDN switch to one Master controller and one or more Slave controllers. The problem with this scheme is that the computing capacity of each SDN controller cannot be used efficiently since one SDN controller handles the load of all SDN switches connected to it. The number of SDN controllers required can be reduced by distributing the load of each SDN switch among multiple SDN controllers. This paper proposes a controller placement model that allows the distribution against SDN controller failures. The proposed model determines the ratios of computing capacity demanded by each SDN switch on the SDN controllers connected to it. The proposed model also determines the number and placement of SDN controllers and the assignment of each SDN switch to SDN controllers. Controller placement is determined so that a network provider can continue to manage all SDN switches if no more than a certain number of SDN controller failures occur. We develop two load distribution methods: split and even-split. We formulate the proposed model with each method as integer linear programming problems. Numerical results show that the proposed model reduces the number of SDN controllers compared to a benchmark model; the maximum reduction ratio is 38.8% when the system latency requirement between an SDN switch and an SDN controller is 100[ms], the computing capacity of each SDN controller is 6 × 106[packets/s], and the maximum number of SDN controllers that can fail at the same time is one.

This paper evaluates three inter-domain redundancy path computation methods based on PCE (Path Computation Element). Some inter-domain paths carry traffic that must be assured of high quality and high reliability transfer such as telephony over IP and premium virtual private networks (VPNs). It is, therefore, important to set inter-domain redundancy paths, i.e. primary and secondary paths. The first scheme utilizes an existing protocol and the basic PCE implementation. It does not need any extension or modification. In the second scheme, PCEs make a virtual shortest path tree (VSPT) considering the candidates of primary paths that have corresponding secondary paths. The goal is to reduce blocking probability; corresponding secondary paths may be found more often after a primary path is decided; no protocol extension is necessary. In the third scheme, PCEs make a VSPT considering all candidates of primary and secondary paths. Blocking probability is further decreased since all possible candidates are located, and the sum of primary and secondary path cost is reduced by choosing the pair with minimum cost among all path pairs. Numerical evaluations show that the second and third schemes offer only a few percent reduction in blocking probability and path pair total cost, while the overheads imposed by protocol revision and increase of the amount of calculation and information to be exchanged are large. This suggests that the first scheme, the most basic and simple one, is the best choice.

A key traffic engineering problem in the Open Shortest Path First (OSPF)-based network is the determination of optimal link weights. From the network operators' point of view, there are two approaches to determining a set of link weights: Start-time Optimization (SO) and Run-time Optimization (RO). We previously presented a Preventive Start-time Optimization (PSO) scheme that determines an appropriate set of link weights at start time. It can counter both unexpected network congestion and network instability and thus overcomes the drawbacks of SO and RO, respectively. The previous work adopts a preventive start-time optimization algorithm with limited candidates, named PSO-L (PSO for Limited candidates). Although PSO-L relaxes the worst-case congestion, it does not confirm the optimal worst-case performance. To pursue this optimality, this paper proposes a preventive start-time optimization algorithm with a wide range of candidates, named PSO-W (PSO for Wide-range candidates). PSO-W upgrades the objective function of SO that determines the set of link weights at start time by considering all possible single link failures; its goal is to minimize the worst-case congestion. Numerical results via simulations show that PSO-W effectively relaxes the worst-case network congestion compared to SO, while it avoids the network instability caused by the run-time changes of link weights caused by RO. At the same time, PSO-W yields performance superior to that of PSO-L.

Obtaining current traffic matrices is essential to traffic engineering (TE) methods. Because it is difficult to monitor traffic matrices, several methods for estimating them from link loads have been proposed. The models used in these methods, however, are incorrect for some real networks. Thus, methods improving the accuracy of estimation by changing routes also have been proposed. However, existing methods for estimating the traffic matrix by changing routes can only capture long-term variations and cannot obtain current traffic matrices accurately. In this paper, we propose a method for estimating current traffic matrices that uses route changes introduced by a TE method. In this method, we first estimate the long-term variations of traffic by using the link loads monitored at previous times. Then, we adjust the estimated long-term variations so as to fit the current link loads. In addition, when the traffic variation trends change and the estimated long-term variations fail to match the current traffic, our method detects mismatch. Then, so as to capture the current traffic variations, the method re-estimates the long-term variations after removing monitored data corresponding to the end-to-end traffic causing the mismatches. We evaluate our method through simulation. The results show that our method can estimate current traffic matrices accurately even when some end-to-end traffic changes suddenly.

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.