Machine learning (ML) has been used for various tasks in network operations in recent years. However, since the scale of networks has grown and the amount of data generated has increased, it has been increasingly difficult for network operators to conduct their tasks with a single server using ML. Thus, ML with edge-cloud cooperation has been attracting attention for efficiently processing and analyzing a large amount of data. In the edge-cloud cooperation setting, although transmission latency, bandwidth congestion, and accuracy of tasks using ML depend on the load balance of processing data with edge servers and a cloud server in edge-cloud cooperation, the relationship is too complex to estimate. In this paper, we focus on monitoring anomalous traffic as an example of ML tasks for network operations and formulate transmission latency, bandwidth congestion, and the accuracy of the task with edge-cloud cooperation considering the ratio of the amount of data preprocessed in edge servers to that in a cloud server. Moreover, we formulate an optimization problem under constraints for transmission latency and bandwidth congestion to select the proper ratio by using our formulation. By solving our optimization problem, the optimal load balance between edge servers and a cloud server can be selected, and the accuracy of anomalous traffic monitoring can be estimated. Our formulation and optimization framework can be used for other ML tasks by considering the generating distribution of data and the type of an ML model. In accordance with our formulation, we simulated the optimal load balance of edge-cloud cooperation in a topology that mimicked a Japanese network and conducted an anomalous traffic detection experiment by using real traffic data to compare the estimated accuracy based on our formulation and the actual accuracy based on the experiment.

In this paper, we propose a new rate-based congestion control method for Named Data Networking (NDN) using additive increase multiplicative decrease (AIMD) and explicit rate notification. In the proposed method, routers notify a corresponding consumer of bottleneck bandwidth by use of Data packets, in a relatively long interval. In addition, routers monitor outgoing faces using the leaky bucket mechanism. When congestion is detected, the routers report this to corresponding consumers using negative-acknowledgment (NACK) packets. A consumer sets its Interest sending rate to the reported rate when a new value is reported. In addition, the consumer adjusts the sending rate to be around the reported rate based on the AIMD mechanism at Data/NACK packet reception. Computer simulations show that the proposed method achieves a high throughput performance and max-min fairness thanks to the effective congestion avoidance.

The road space rationing (RSR) method regulates a period in which a user group can make telephone calls in order to decrease the call attempt rate and induce calling parties to shorten their calls during disaster congestion. This paper investigates what settings of this indirect control induce more self-restraint and how the settings change calling parties' behavior using experimental psychology. Our experiments revealed that the length of the regulated period differently affected calling parties' behavior (call duration and call attempt rate) and indicated that the 60-min RSR method (i.e., 10 six-min periods) is the most effective setting against disaster congestion.

For recent datacenter networks, RDMA (Remote Direct Memory Access) can ease the overhead of the TCP/IP protocol suite. The RoCEv2 (RDMA over Converged Ethernet version 2) standard enables RDMA on widely deployed Ethernet technology. RoCEv2 leverages priority-based flow control (PFC) for realizing the lossless environment required by RDMA. However, PFC is well-known to have the technical weakness of head-of-line blocking. Congestion control for RDMA is a very hot research topic for datacenter networks. In this paper, we propose a novel congestion control algorithm for RoCEv2, TIDD (Timer-based Increase and Delay-based Decrease). TIDD basically combines the timer-based increase of DCQCN and delay-based decrease of TIMELY. Extensive simulation results show that TIDD satisfies the high throughput and low latency required for datacenter networks.

Recently, information centric network (ICN) has attracted attention because cached content delivery from router's cache storage improves quality of service (QoS) by reducing redundant traffic. Then, adaptive video streaming is applied to ICN to improve client's quality of experience (QoE). However, in the previous approaches for the cache control, the router implicitly caches the content requested by a user for the other users who may request the same content subsequently. As a result, these approaches are not able to use the cache effectively to improve client's QoE because the cached contents are not always requested by the other users. In addition, since the previous cache control does not consider network congestion state, the adaptive bitrate (ABR) algorithm works incorrectly and causes congestion, and then QoE degrades due to unnecessary congestion. In this paper, we propose an explicit cache placement notification for congestion-aware adaptive video streaming over ICN (CASwECPN) to mitigate congestion. CASwECPN encourages explicit feedback according to the congestion detection in the router on the communication path. While congestion is detected, the router caches the requested content to its cache storage and explicitly notifies the client that the requested content is cached (explicit cache placement and notification) to mitigate congestion quickly. Then the client retrieve the explicitly cached content in the router detecting congestion according to the general procedures of ICN. The simulation experiments show that CASwECPN improves both QoS and client's QoE in adaptive video streaming that adjusts the bitrate adaptively every video segment download. As a result, CASwECPN effectively uses router's cache storage as compared to the conventional cache control policies.

In this paper, we used the data set of domain names Global Top 1M provided by Alexa to analyze the effectiveness of Fallback in ECN. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable. By extracting the header fields of the TCP/IP packets, we confirmed that in most regions, connectivity will be slightly improved after Fallback is enabled and Fallback has a positive effect on the total time of the whole access process. Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers.

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.

Recently, adaptive streaming over information centric network (ICN) has attracted attention. In adaptive streaming over ICN, the bitrate adaptation of the client often overestimates a bitrate for available bandwidth due to congestion because the client implicitly estimates congestion status from the content download procedures of ICN. As a result, streaming overestimated bitrate results in QoE degradation of clients such as cause of a stall time and frequent variation of the bitrate. In this paper, we propose a congestion-aware adaptive streaming over ICN combined with the explicit congestion notification (CAAS with ECN) to avoid QoE degradation. CAAS with ECN encourages explicit feedback of congestion detected in the router on the communication path, and introduces the upper band of the selectable bitrate (bitrate-cap) based on explicit feedback from the router to the bitrate adaptation of the clients. We evaluate the effectiveness of CAAS with ECN for client's QoE degradation due to congestion and behavior on the QoS metrics based on throughput. The simulation experiments show that the bitrate adjustment for all the clients improves QoE degradation and QoE fairness due to effective congestion avoidance.

High level synthesis (HLS) is a source-code-driven Register Transfer Level (RTL) design tool, and the performance, the power consumption, and the area of a generated RTL are limited partly by the description of a HLS input source code. In order to break through such kind of limitation and to get a further optimized RTL, the optimization of the input source code is indispensable. Routing congestion is one of such problems we need to consider the refinement of a HLS input source code. In this paper, we propose a novel HLS flow that performs code improvements by detecting congested parts directly from HLS input source code without using physical logic synthesis, and regenerating the input source code for HLS. In our approach, the origin of the wire congestion is detected from the HLS input source code by applying pattern matching on Program-Dependence Graph (PDG) constructed from the HLS input source code, the possibility of wire congestion is reported.

On the Internet, end hosts and network nodes interdependently work to smoothly transfer traffic. Observed traffic dynamics are the result of those interactions among those entities. To manage Internet traffic to provide satisfactory quality services, such dynamics need to be well understood to predict traffic patterns. In particular, some nodes have a function that sends back-pressure signals to backward nodes to reduce their sending rate and mitigate congestion. Transmission Control Protocol (TCP) congestion control in end-hosts also mitigates traffic deviation to eliminate temporary congestion by reducing the TCP sending rate. How these congestion controls mitigate congestion has been extensively investigated. However, these controls only throttle their sending rate but do not reduce traffic volume. Such congestion control fails if congestion is persistent, e.g., for hours, because unsent traffic demand will infinitely accumulate. However, on the actual Internet, even with persistent congestion, such accumulation does not seem to occur. During congestion, users and/or applications tend to reduce their traffic demand in reaction to quality of service (QoS) degradation to avoid negative service experience. We previously estimated that 2% packet loss results in 23% traffic reduction because of this upper-layer reaction [1]. We view this reduction as an upper-layer congestion-avoidance mechanism and construct a closed-loop model of this mechanism, which we call the Upper-Layer Closed-Loop (ULCL) model. We also show that by using ULCL, we can predict the degree of QoS degradation and traffic reduction as an equilibrium of the feedback loop. We applied our model to traffic and packet-loss ratio time series data gathered in an actual network and demonstrate that it effectively estimates actual traffic and packet-loss ratio.

CCN/NDN (Content-Centric Networking/Named-Data Networking) is one of the most promising content-oriented network architectures. In CCN/NDN, forwarding information base (FIB) might have multiple entries for a same content name prefix, which means CCN/NDN potentially supports multi-source download. When a content is obtained from multiple sources, the technical knowledge obtained for congestion control in the current Internet cannot be simply applied. This is because in the current Internet, FIB is restricted to have only one entry for each IP address prefix, which causes quite different path feature from CCN/NDN. This paper proposes a new congestion control for CCN/NDN with multi-source content retrieval. The proposed congestion control is composed of end-to-end window flow control and router assisted Interest forwarding control, and enables transmission rate regulation only on a congested branch.

ECN, as a decisive approach for TCP congestion control, has been proposed for many years. However, its deployment on the Internet is much slower than expected. In this paper, we investigate the state of the deployment of ECN (Explicit Congestion Notification) on the Internet from a different viewpoint. We use the data set of web domains published by Alexa as the hosts to be tested. We negotiate an ECN-Capable and a Not ECN-Capable connections with each host and collect all packets belonging to the connections. By analyzing the header fields of the TCP/IP packets, we dig out the deployment rate, connectivity, variation of round-trip time and time to live between the Not ECN-Capable and ECN-Capable connections as well as the rate of IPv6-Capable web servers. Especially, it is clear that the connectivity is different from the domains (regions on the Internet). We hope that the findings acquired from this study would incentivize ISPs and administrators to enable ECN in their network systems.

Packet losses significantly degrade TCP performance in high-latency environments. This is because TCP needs at least one round-trip time (RTT) to recover lost packets. The recovery time will grow longer, especially in high-latency environments. TCP keeps transmission rate low while lost packets are recovered, thereby degrading throughput. To prevent this performance degradation, the number of retransmissions must be kept as low as possible. Therefore, we propose a scheme to apply a technology called “forward error correction” (FEC) to the entire TCP operation in order to improve throughput. Since simply applying FEC might not work effectively, three function, namely, controlling redundancy level and transmission rate, suppressing the return of duplicate ACKs, interleaving redundant packets, were devised. The effectiveness of the proposed scheme was demonstrated by simulation evaluations in high-latency environments.

Congestion control is a hot topic in named data networking (NDN). Congestion control methods for NDN are classified into two approaches: the rate-based approach and the window-based approach. In the window-based approach, the optimum window size cannot be determined due to the largely changing round-trip time. Therefore, the rate-based approach is considered to be suitable for NDN and has been studied actively. However, there is still room for improvement in the window-based approach because hop-by-hop control in this approach has not been explored. In this paper, we propose a hop-by-hop widow-based congestion control method for NDN (HWCC). The proposed method introduces a window-size control for per-hop Interest transmission using hop-by-hop acknowledgment. In addition, we extend HWCC so that it can support multipath forwarding (M-HWCC) in order to increase the network resources utilization. The simulation results show that both of HWCC and M-HWCC achieve high throughput performance, as well as the max-min fairness, while effectively avoiding congestion.

Ring-based topology is popular for optical network-on-chip. However, the network congestion is serious for ring topology, especially when optical circuit-switching is employed. In this paper, we proposed an algorithm to build a low congestion multi-ring architecture for optical network-on-chip without additional wavelength or scheduling overhead. A network congestion model is established with new network congestion factor defined. An algorithm is developed to optimize the low congestion multi-ring topology. Finally, a case study is shown and the simulation results by OPNET verify the superiority over the traditional ONoC architecture.

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.

The exchanged hypercube, denoted by EH(s,t), is a graph obtained by systematically removing edges from the corresponding hypercube, while preserving many of the hypercube's attractive properties. Moreover, ring-connected topology is one of the most promising topologies in Wavelength Division Multiplexing (WDM) optical networks. Let Rn denote a ring-connected topology. In this paper, we address the routing and wavelength assignment problem for implementing the EH(s,t) communication pattern on Rn, where n=s+t+1. We design an embedding scheme. Based on the embedding scheme, a near-optimal wavelength assignment algorithm using 2s+t-2+⌊2t/3⌋ wavelengths is proposed. We also show that the wavelength assignment algorithm uses no more than an additional 25 percent of (or ⌊2t-1/3⌋) wavelengths, compared to the optimal wavelength assignment algorithm.

Streaming services that use TCP have increased; however, throughput is unstable due to congestion control caused by packet loss when TCP is used. Thus, TCP control to secure a required transmission rate for streaming communication using Forward Error Correction (FEC) technology (TCP-AFEC) has been proposed. TCP-AFEC can control the appropriate transmission rate according to network conditions using a combination of TCP congestion control and FEC. However, TCP-AFEC was not developed for wireless Local Area Network (LAN) environments; thus, it requires a certain time to set the appropriate redundancy and cannot obtain the required throughput. In this paper, we demonstrate the drawbacks of TCP-AFEC in wireless LAN environments. Then, we propose a redundancy setting method that can secure the required throughput for FEC, i.e., TCP-TFEC. Finally, we show that TCP-TFEC can secure more stable throughput than TCP-AFEC.

This paper proposes a Preventive Start-time Optimization with no penalty (PSO-NP). PSO-NP determines a suitable set of Open Shortest Path First (OSPF) link weights at the network operation start time that can handle any link failure scenario preventively while considering both failure and non failure scenarios. Preventive Start-time Optimization (PSO) was designed to minimize the worst case congestion ratio (maximum link utilization over all the links in the network) in case of link failure. PSO considers all failure patterns to determine a link weight set that counters the worst case failure. Unfortunately, when there is no link failure, that link weight set leads to a higher congestion ratio than that of the conventional start-time optimization scheme. This penalty is perpetual and thus a burden especially in networks with few failures. In this work, we suppress that penalty while reducing the worst congestion ratio by considering both failure and non failure scenarios. Our proposed scheme, PSO-NP, is simple and effective in that regard. We expand PSO-NP into a Generalized Preventive Start-time Optimization (GPSO) to find a link weight set that balances both the penalty under no failure and the congestion ratio under the worst case failure. Simulation results show that PSO-NP achieves substantial congestion reduction for any failure case while suppressing the penalty in case of no failure in the network. In addition, GPSO as framework is effective in determining a suitable link weight set that considers the trade off between the penalty under non failure and the worst case congestion ratio reduction.

State-of-the-art parallel computers, which are growing in parallelism, require a lot of things in their interconnection networks. Although wide spectrum of efforts in research and development for effective and practical interconnection networks are reported, the problem is still open. One of the largest issues is congestion control that intends to maximize the network performance in terms of throughput and latency. Throttling, or injection limitation, is one of the center ideas of congestion control. We have proposed a new class of throttling method, Entropy Throttling, whose foundation is entropy concept of packets. The throttling method is successful in part, however, its potentials are not sufficiently discussed. This paper aims at exploiting capabilities of the Entropy Throttling method via comprehensive evaluation. Major contributions of this paper are to introduce two ideas of hysteresis function and guard time and also to clarify wide performance characteristics in steady and unsteady communication situations. By introducing the new ideas, we extend the Entropy throttling method. The extended methods improve communication performance at most 3.17 times in the best case and 1.47 times in average compared with non-throttling cases in collective communication, while the method can sustain steady communication performance.