This paper addresses the system design for virtual network services, particularly focusing on virtual machine (VM) services. The VM service requires users to define a substantial number of specifications, such as CPU resources and latency bounds, as a prerequisite. This process is challenging for nonprofessional users and inefficient even for professional users. Users need to clearly understand the specifications they need and report their requirements to the service manager, which demands a high level of knowledge and experience of a system engineer. To make the system more accessible to users, we propose a framework that enables interaction with the virtual network service through natural language (NL) inputs from the users. The framework employs NL models to interpret user requests in NL format into service specifications to determine the specificationdependent optimal virtual network allocation. We demonstrated the effectiveness of the proposed framework through numerical experiments, which show that the user requests in NL are accurately interpreted and incorporated into the virtual network allocation.

To achieve carbon neutrality in telecommunication networks, this paper focuses on a virtual network (VN) allocation problem for telecommunication networks powered by renewable energy (RE). One of the most critical issues is the fluctuation of RE power caused by uncertain weather conditions. Under the fluctuation of RE power, we must allocate multiple VNs so that RE power is efficiently used while avoiding waste. To deal with the fluctuation problem of RE power, we model RE power as a stochastic variable that follows a certain probability distribution. However, there are still challenges in accurately estimating the true distribution due to the limited size and nonstationarity of RE power data. To this end, we propose a distributionally robust VN allocation model that minimizes the expectational cost of the total excess power for the worst-case probability distribution in the set of all possible distributions that generate actual RE power, i.e., the uncertainty set. The uncertainty set is defined on the basis of the Wasserstein ball, in which the center is an empirical distribution constructed from the accumulated RE data and the radius is a certain Wasserstein metric. Then, the true probability distribution is assumed to be in the uncertainty set. Finally, through numerical experiments, the effectiveness of the proposed method is shown even if the estimation of RE power distribution is uncertain due to the small size of RE power data.

To achieve carbon neutrality in telecommunications networks, the introduction of renewable energy is expected to further advance in the future. However, renewable energy such as solar power generation can experience large fluctuations in power output depending on weather conditions. Therefore, depending on the location and time period, there may be places where renewable energy cannot be fully consumed and surplus power is generated, and there may be places where renewable energy is not sufficient to meet the power demand. Toward network carbon neutrality, new methods need to be established from a network perspective that avoid inefficient power management problems caused by fluctuations in renewable energy. To solve this problem, we have been studying the control of power consumption locations and time periods by allocating workloads using virtualization technology. In this paper, we propose a workload scheduling method to increase renewable energy use. The problem to maximize the renewable energy use is formulated, and to solve it in a short time, a heuristic search method is proposed. The simulation results show that the proposed control method can increase the amount of renewable energy use while having tradeoffs with communication quality and equipment efficiency.

Owing to the critical role of the domain name system (DNS), its query log data are utilized for various network monitoring purposes. With the diversification of network services, these data have become increasingly complex, making mining useful information challenging. DNS query log data can be considered as the superposition of two types of communication patterns: groups of domains accessed simultaneously (e.g., ad servers and content delivery network (CDN) servers) and time-series access patterns based on user behavior characteristics (e.g., access trends during the night). However, previous studies have not focused on extracting both access patterns hidden in the data. This study proposes a method that extracts both patterns of accessed domains and temporal access patterns as user communication behaviors from DNS query log data and predicts future accesses based on these patterns. The proposed method first aggregates similar fully qualified domain names (FQDNs) associated with the same service. We then present temporal regularized nonnegative tensor factorization (TR-NTF) that extracts both access patterns from a third-order tensor expressing DNS query log data and enables prediction. We evaluate the proposed method using synthetic and actual data and demonstrate that it successfully extracts hidden communication patterns and achieves sufficient prediction accuracy.

In this paper, we consider a physical network powered by renewable energy resources and a virtual network (VN) of a client service, which is composed of a client node, a virtual machine (VM) node, and a virtual link. Then, a robust VN allocation problem is formulated for multiple client services: given the location of client nodes, find the allocation of VM nodes and virtual links to maintain robustness for the uncertainty of traffic demands and renewable energy power; i.e., their prediction error. Specifically, we propose two robust allocation models: robust VN allocation model and two-stage robust VN allocation model, which are formulated on the basis of robust optimization and two-stage robust optimization, respectively. To show the effectiveness of two robust proposed models, we conduct numerical experiments under various prediction error patterns of traffic demands and renewable energy power. When prediction errors are large, the two proposed models acquire better average and worst-case performance than a deterministic model that does not handle prediction errors. In addition, we observe some patterns where the two-stage robust VN allocation model acquires better average performance than the robust VN allocation model instead of deteriorating the worst-case performance.

The environmental load of telecommunication service provision is increasing due to the increase in communication traffic. Virtual networks have recently begun to spread, and flexible virtual network control is expected to reduce the operating costs of telecommunication services. This paper proposes a control method to achieve both reduced power bills and stable network operation when the electricity unit price differs among areas and time periods. To begin with, the problem to minimize power bills is formulated, and to solve it quickly, a heuristic search method is proposed that utilizes network centrality. In addition, we formulate a multi-objective optimization problem characterized by parameter normalization, and simulation results show that the proposed control method can reduce power bills while suppressing the number of network reconfigurations.

Network traffic and computing demand have been changing dramatically due to the growth of various types of network services, e.g., high-quality video delivery and operating system (OS) updates. To maximize the utilization efficiency of limited network resources, network resource control technology is required for smooth and quick operation when network demands change. Therefore, we propose a dynamic virtual network (VN) allocation method based on cooperative multi-agent deep reinforcement learning (Coop-MADRL). This method can quickly optimize network resources even while network demands are drastically changing by learning the relationship between network demand patterns and optimal allocation by using deep reinforcement learning (DRL) in advance. The key idea is to use a multi-agent technique for a reinforcement learning (RL) based dynamic VN allocation method, which can reduce the number of candidate actions per agent and can improve the performance for VN allocation. Moreover, a cooperation technique improves the efficiency of VN allocation. From results of a simulation evaluation, Coop-MADRL can calculate effective allocation within 1 s, which reduces the maximum server and link utilization and drastically reduces the constraint violations compared with that of the static VN allocation method. Furthermore, we revealed that the learning with various mixed traffic models could achieve a high generalization performance for all traffic patterns.

Network traffic and computing demand have been changing dramatically due to the growth of various types of network services, e.g., high-quality video delivery and operating system (OS) updates. To maximize the utilization efficiency of limited network resources, network resource control technology is required for smooth and quick operation when network demands change. We propose a dynamic virtual network (VN) allocation method based on cooperative multi-agent deep reinforcement learning (Coop-MADRL). This method can quickly optimize network resources even while network demands are drastically changing by learning the relationship between network demand patterns and optimal allocation by using deep reinforcement learning (DRL) in advance. The key idea is to use a multi-agent technique for a reinforcement learning (RL) based dynamic VN allocation method, which can reduce the number of candidate actions per agent and can improve the performance for VN allocation. Moreover, a cooperation technique improves the efficiency of VN allocation. From results of a simulation evaluation, Coop-MADRL can calculate effective allocation within 1 s, which reduces the maximum server and link utilization and drastically reduces the average constraint violation compared with that of the static VN allocation method.

Network traffic and computing demand have been changing dramatically due to the growth of various types of network services, e.g., high-quality video delivery and OS update. To maximize the utilization efficiency of limited network resources, network resource control technology is required for smooth and quick operation when the network demands change. We propose a dynamic virtual network allocation method based on safe multi-agent deep reinforcement learning (safe MA-DRL). This method can quickly optimize network resources even while network demands are drastically changing by learning the relationship between network demand patterns and optimal allocation by using the DRL algorithm in advance. We developed two techniques to be used with our method; safety-considerations and multi-agent. Our safety-considerations technique reduces the degree of constraint violations, such as network congestion and server overload, and our multi-agent technique improves the scalability of virtual network allocation by dividing demands into groups and assigning each group's allocation to each agent. As a result of a simulation evaluation, safe MA-DRL can calculate effective allocation within 1 s that doubles the link utilization efficiency without any constraint violations compared to the static virtual network allocation method.

Network functions virtualization (NFV) enables telecommunications service providers to provide various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services with carrier-grade quality, an NFV controller must optimally allocate such VNFs into physical networks and servers, taking into account combination(s) of objective functions and constraints for each metric defined for each VNF type. The NFV controller should also be extendable, i.e., new metrics should be able to be added. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this algorithm is not extendable because the problem formulation needs to be rebuilt every time, e.g., a new metric is added. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed to optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.

Due to integrated high-speed networks accommodating various types of services and applications, the quality of service (QoS) requirements for those networks have also become diverse. The network resources are shared by the individual service traffic in the integrated network. Thus, the QoS of all the services may be degraded indiscriminately when the network becomes congested due to a sudden increase in traffic for a particular service if there is no traffic engineering taking into account each service's QoS requirement. To resolve this problem, we present a method of controlling individual service traffic by using an overlay network, which makes it possible to flexibly add various functionalities. The overlay network provides functionalities to control individual service traffic, such as constructing an overlay network topology for each service, calculating the optimal route for the service's QoS, and caching the content to reduce traffic. Specifically, we present a method of overlay routing that is based on the Hedge algorithm, an online learning algorithm to guarantee an upper bound in the difference from the optimal performance. We show the effectiveness of our overlay routing through simulation analysis for various network topologies.

Traffic caused by P2P services dominates a large part of traffic on the Internet and imposes significant loads on the Internet, so reducing P2P traffic within networks is an important issue for ISPs. In particular, a huge amount of traffic is transferred within backbone networks; therefore reducing P2P traffic is important for transit ISPs to improve the efficiency of network resource usage and reduce network capital cost. To reduce P2P traffic, it is effective for ISPs to implement cache devices at some router ports and reduce the hop length of P2P flows by delivering the required content from caches. However, the design problem of cache locations and capacities has not been well investigated, although the effect of caches strongly depends on the cache locations and capacities. We propose an optimum design method of cache capacity and location for minimizing the total amount of P2P traffic based on dynamic programming, assuming that transit ISPs provide caches at transit links to access ISP networks. We apply the proposed design method to 31 actual ISP backbone networks.

Video streaming with HDTV or UHDV quality will be provided and widely demanded in the future. However, the transmission bit-rate of high-quality video streaming is quite large, so generated traffic flows will cause link congestion. Therefore, when providing streaming services of rich content, it is important to flatten the link utilization, i.e., reduce the maximum link utilization. To achieve this goal, parallel video streaming in which ISPs use multiple servers to deliver rich content is effective. However, the effect of parallel video streaming depends on the network topology and link capacities. In this paper, we investigate the impact of network topologies on the effect of parallel video streaming using 23 actual commercial ISP networks, when optimally designing server locations and optimally selecting servers.

As a promising solution to manage the huge workload of large-scale VoD services, managed peer-assisted CDN systems, such as P4P has attracted attention. Although the approach works well in theory or in a controlled environment, to our best knowledge, there have been no general studies that address how actual peers can be incentivized in the wild Internet; thus, deployablity of the system with respect to incentives to users has been an open issue. With this background in mind, we propose a new business model that aims to make peer-assisted approaches more feasible. The key idea of the model is that users sell their idle resources back to ISPs. In other words, ISPs can leverage resources of cooperative users by giving them explicit incentives, e.g., virtual currency. We show the high-level framework of designing optimal incentive amount to users. We also analyze how incentives and other external factors affect the efficiency of the system through simulation. Finally, we discuss other fundamental factors that are essential for the deployability of managed peer-assisted model. We believe that the new business model and the insights obtained through this work are useful for assessing the practical design and deployment of managed peer-assisted CDNs.

We describe a method of adaptively controlling bandwidth allocation to flows for reducing the file transfer time of short flows without decreasing throughput of long-duration large flows. According to the rapid increase in Internet traffic volume, effective traffic engineering is increasingly required. Specifically, the traffic of long-duration large flows due to the use of peer-to-peer applications, for example, is a problem. Most conventional QoS controls allocate a fair-share bandwidth to each flow regardless of its duration. Thus, a long-duration large flow (such as a P2P flow) is allocated the same bandwidth as a short- duration flow (such as data from a Web page) in which the user is more sensitive to response time, i.e., file transfer time. As a result, long-duration large flows consume bandwidth over a long period and increase response times of short-duration flows, and conventional QoS methods do nothing to prevent this. In this paper, we therefore investigate a different approach, that is, a new form of bandwidth control that enables us to achieve better performance when handling short-duration flows while maintaining performance when handling long-duration flows. The basic idea is to tag packets of long-duration large flows according to traffic conditions and to give temporarily higher priority to non-tagged packets during network congestion. We also show the effectiveness of our method through simulation.

We present a method of detecting network anomalies, such as DDoS (distributed denial of service) attacks and flash crowds, automatically in real time. We evaluated this method using measured traffic data and found that it successfully differentiated suspicious traffic. In this paper, we focus on cyclic traffic, which has a daily and/or weekly cycle, and show that the differentiation accuracy is improved by utilizing such a cyclic tendency in anomaly detection. Our method differentiates suspicious traffic that has different statistical characteristics from normal traffic. At the same time, it learns about cyclic large- volume traffic, such as traffic for network operations, and finally considers it to be legitimate.

We propose a method of identifying anomalous traffic sources using flow statistics. We have investigated a way of detecting whether or not anomalies occur by observing the behavior of several time-series of flow statistics such as the number of flows. After detecting the occurrences of network anomalies, we need to identify the source of the anomalies. In this paper, we describe a method of identifying anomalous traffic sources. For this purpose, we apply data mining approaches such as the K-nearest neighbor method, naive Bayesian classifier, neural network, and support vector machine. We show how to use such approaches to identify anomalous traffic sources by using flow statistics. We also show evaluation results for the effectiveness of our approach using two measurement data sets.

We investigate the detection accuracy of network anomalies when we use flow statistics obtained through packet sampling. We have already shown, through a case study based on measurement data, that network anomalies generating a huge number of small flows, such as network scans or SYN flooding, become hard to detect when we perform packet sampling. In this paper, we first develop an analytical model that enables us to quantitatively evaluate the effect of packet sampling on the detection accuracy and then investigate why detection accuracy worsens when the packet sampling rate decreases. In addition, we show that, even with a low sampling rate, spatially partitioning the monitored traffic into groups makes it possible to increase the detection accuracy. We also develop a method of determining an appropriate number of partitioned groups and show its effectiveness.