Software-Defined Networking (SDN) enables flexible deployment and innovation of new networking applications by decoupling and abstracting the control and data planes. It has radically changed the concept and way of building and managing networked systems, and reduced the barriers to entry for new players in the service markets. It is considered to be a promising solution providing the scale and versatility necessary for IoT. However, SDN may also face many challenges, i.e., the centralized control plane would be a single point of failure. With the advent of blockchain technology, blockchain-based SDN has become an emerging architecture for securing a distributed network environment. Motivated by this, in this work, we summarize the generic framework of blockchain-based SDN, discuss security challenges and relevant solutions, and provide insights on the future development in this field.

In this paper, we focus on a large-scale ICN (Information-Centric Networking), and reveal the scaling property of ICN. Because of in-network content caching, ICN is a sort of cache networks and expected to be a promising architecture for replacing future Internet. To realize a global-scale (e.g., Internet-scale) ICN, it is crucial to understand the fundamental properties of such large-scale cache networks. However, the scaling property of ICN has not been well understood due to the lack of theoretical foundations and analysis methodologies. For answering research questions regarding the scaling property of ICN, we derive the cache hit probability at each router, the average content delivery delay of each entity, and the average content delivery delay of all entities over a content distribution tree comprised of a single repository (i.e., content provider), multiple routers, and multiple entities (i.e., content consumers). Through several numerical examples, we investigate the effect of the topology and the size of the content distribution tree and the cache size at routers on the average content delivery delay of all entities. Our findings include that the average content delivery delay of ICNs converges to a constant value if the cache size of routers are not small, which implies high scalability of ICNs, and that even when the network size would grow indefinitely, the average content delivery delay is upper-bounded by a constant value if routers in the network are provided with a fair amount of content caches.

Information-Centric Networking (ICN) can offer rich functionalities to the network, e.g, in-network caching, and name-based forwarding. Incremental deployment of ICN is a key challenge that enable smooth migration from current IP network to ICN. We can say that Network Function Virtualization (NFV) must be one of the key technologies to achieve this deployment because of its flexibility to support new network functions. However, when we consider the ICN deployment with NFV, there exist two performance issues, processing delay of name-based forwarding and computational overhead of virtual machine. In this paper we proposed a NFV infrastructure-assisted ICN packet forwarding by integrating the name look-up to the Open vSwitch. The contributions are twofold: 1) First, we provide the novel name look-up scheme that can forward ICN packets without costly longest prefix match searching. 2) Second, we design the ICN packet forwarding scheme that integrates the partial name look-up into the virtualization infrastructure to mitigate computation overhead.

In this paper, we propose a new routing protocol for named data networking applied to ad hoc networks. We suppose a type of ad hoc networks that advertise versatile information in public spaces such as shopping mall and museum. In this kind of networks, information providers prepare fixed nodes, and users are equipped with mobile terminals. So, we adopt a hybrid approach where a proactive routing is used in the producer side network and a reactive routing is used in the consumer side network. Another feature of the proposed protocol is that only the name prefix advertisement is focused on in the proactive routing. The result of performance evaluation focusing on the communication overhead shows that our proposal has a moderate overhead both for routing control messages and Interest packets compared with some of conventional NDN based ad hoc routing mechanisms proposed so far.

In this paper, we propose a packet loss detection mechanism called Interest ACKnowledgement (ACK). Interest ACK provides information on the history of successful Interest packet receptions at a repository (i.e., content provider); this information is conveyed to the corresponding entity (i.e., content consumer) via the header of Data packets. Interest ACKs enable the entity to quickly and accurately detect Interest and Data packet losses in the network. We conduct simulations to investigate the effectiveness of Interest ACKs under several scenarios. Our results show that Interest ACKs are effective for improving the adaptability and stability of CCN with window-based flow control and that packet losses at the repository can be reduced by 10%-20%. Moreover, by extending Interest ACK, we propose a lossy link detection mechanism called LLD-IA (Lossy Link Detection with Interest ACKs), which is a mechanism for an entity to estimate the link where the packet was discarded in a network. Also, we show that LLD-IA can effectively detect links where packets were discarded under moderate packet loss ratios through simulation.

Location-based forwarding is a key driver for location-based services. This paper designs forwarding information data structures for location-based forwarding in Internet Service Provider (ISP) scale networks based on Named Data Networking (NDN). Its important feature is a naming scheme which represents locations by leveraging space-filling curves.

In content oriented networks (CON), routers in a network are generally equipped with local cache storages and store incoming contents temporarily. Efficient utilization of total cache storage in networks is one of the most important technical issues in CON, as it can reduce content server load, content download latency and network traffic. Performance of networked cache is reported to strongly depend on both cache decision and content request routing. In this paper, we evaluate several combinations of these two strategies. Especially for routing, we take up off-path cache routing, Breadcrumbs, as one of the content request routing proposals. Our performance evaluation results show that off-path cache routing, Breadcrumbs, suffers low performance with cache decisions which generally has high performance with shortest path routing (SPR), and obtains excellent performance with TERC (Transparent En-Route Cache) which is well-known to have low performance with widely used SPR. Our detailed evaluation results in two network environments, emerging CONs and conventional IP, show these insights hold in both of these two network environments.

Low Power Wide Area Network (LPWAN) is designed for low-bandwidth, low-power, long-distance, large-scale connected IoT applications and realistic for networking in an emergency or restricted situation, so it has been proposed as an attractive communication technology to handle unexpected situations that occur during and/or after a disaster. However, the traditional LPWAN with its default protocol will reduce the communication efficiency in disaster situation because a large number of users will send and receive emergency information result in communication jams and soaring error rates. In this paper, we proposed a LPWAN based decentralized network structure as an extension of our previous Disaster Information Sharing System (DISS). Our network structure is powered by Named Node Networking (3N) which is based on the Information-Centric Networking (ICN). This network structure optimizes the excessive useless packet forwarding and path optimization problems with node name routing (NNR). To verify our proposal, we conduct a field experiment to evaluate the efficiency of packet path forwarding between 3N+LPWA structure and ICN+LPWA structure. Experimental results confirm that the load of the entire data transmission network is significantly reduced after NNR optimized the transmission path.

Software-defined networking (SDN) technology enables us to flexibly configure switches in a network. Previously, distributed SDN control methods have been discussed to improve their scalability and robustness. Distributed placement of controllers and backing up each other enhance robustness. However, these techniques do not include an emergency measure against large-scale failures such as network separation induced by disasters. In this study, we first propose a network partitioning method to create a robust control plane (C-Plane) against large-scale failures. In our approach, networks are partitioned into multiple sub-networks based on robust topology coefficient (RTC). RTC denotes the probability that nodes in a sub-network isolate from controllers when a large-scale failure occurs. By placing a local controller onto each sub-network, 6%-10% of larger controller-switch connections will be retained after failure as compared to other approaches. Furthermore, we discuss reactive emergency reconstruction of a distributed SDN C-plane. Each node detects a disconnection to its controller. Then, C-plane will be reconstructed by isolated switches and managed by the other substitute controller. Meanwhile, our approach reconstructs C-plane when network connectivity recovers. The main and substitute controllers detect network restoration and merge their C-planes without conflict. Simulation results reveal that our proposed method recovers C-plane logical connectivity with a probability of approximately 90% when failure occurs in 100 node networks. Furthermore, we demonstrate that the convergence time of our reconstruction mechanism is proportional to the network size.

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.

Socially aware networking is an emerging research field that aims to improve the current networking technologies and realize novel network services by applying social network analysis (SNA) techniques. Conducting socially aware networking studies requires knowledge of both SNA and communication networking, but it is not easy for communication networking researchers who are unfamiliar with SNA to obtain comprehensive knowledge of SNA due to its interdisciplinary nature. This paper therefore aims to fill the knowledge gap for networking researchers who are interested in socially aware networking but are not familiar with SNA. This paper surveys three types of important SNA techniques for socially aware networking: identification of influential nodes, link prediction, and community detection. Then, this paper introduces how SNA techniques are used in socially aware networking and discusses research trends in socially aware networking.

Software-defined networking (SDN) has rapidly emerged as a promising new technology for future networks and gained considerable attention from both academia and industry. However, due to the separation between the control plane and the data plane, the SDN controller can easily become the target of denial-of service (DoS) attacks. To mitigate DoS attacks in OpenFlow networks, our solution, MinDoS, contains two key techniques/modules: the simplified DoS detection module and the priority manager. The proposed architecture sends requests into multiple buffer queues with different priorities and then schedules the processing of these flow requests to ensure better controller protection. The results show that MinDoS is effective and adds only minor overhead to the entire SDN/OpenFlow infrastructure.

This paper argues that a layered approach is more suitable for Information Centric Networking (ICN) than a narrow-waist approach and proposes an ICN mechanism called ZINK. In ZINK, a location-independent content name is resolved to a list of node IDs of content servers in the application layer and a node ID is mapped to a node locator in the network layer, which results in scalable locator-based routing. An ID/Locator split approach in the network layer can efficiently support client/serever mobility. Efficient content transfer is achieved by using sophisticated functions in the transport layer such as multipath transfer for bandwidth aggregation or fault tolerance. Existing well-tuned congestion control in the transport layer achieves fairness not only among ICN flows but also among ICN flows and other flows. A proof-of concept prototype of ZINK is implemented on an IPv6 stack. Evaluation results show that the time for content finding is practical, efficient content transfer is possible by using multipath transfer, and the mobility support mechanism is scalable as shown in a nationwide experiment environment in Japan.

In this paper, we posit that, in future mobile network, network softwarization will be prevalent, and it becomes important to utilize deep machine learning within network to classify mobile traffic into fine grained slices, by identifying application types and devices so that we can apply Quality-of-Service (QoS) control, mobile edge/multi-access computing, and various network function per application and per device. This paper reports our initial attempt to apply deep machine learning for identifying application types from actual mobile network traffic captured from an MVNO, mobile virtual network operator and to design the system for classifying it to application specific slices.

Information-centric networking (ICN) has gained attention from network research communities due to its capability of efficient content dissemination. In-network caching function in ICN plays an important role to achieve the design motivation. However, many researchers on in-network caching due to its ability to efficiently disseminate content. The in-network caching function in ICN plays an important role in realizing the design goals. However, many in-network caching researchers have focused on where to cache rather than how to cache: the former is known as content deployment in the network and the latter is known as cache replacement in an ICN router. Although the cache replacement has been intensively researched in the context of web-caching and content delivery network previously, networks, the conventional approaches cannot be directly applied to ICN due to the fine granularity of chunks in ICN, which eventually changes the access patterns. In this paper, we argue that ICN requires a novel cache replacement algorithm to fulfill the requirements in the design of a high performance ICN router. Then, we propose a novel cache replacement algorithm to satisfy the requirements named Compact CLOCK with Adaptive Replacement (Compact CAR), which can reduce the consumption of cache memory to one-tenth compared to conventional approaches. In this paper, we argue that ICN requires a novel cache replacement algorithm to fulfill the requirements set for high performance ICN routers. Our solution, Compact CLOCK with Adaptive Replacement (Compact CAR), is a novel cache replacement algorithm that satisfies the requirements. The evaluation result shows that the consumption of cache memory required to achieve a desired performance can be reduced by 90% compared to conventional approaches such as FIFO and CLOCK.

Distributed control plane architecture has been employed in software-defined data center networks to improve the scalability of control plane. However, since the flow space is partitioned by assigning switches to different controllers, the network topology is also partitioned and the rule setup process has to invoke multiple controllers. Besides, the control load balancing based on switch migration is heavyweight. In this paper, we propose a lightweight load partition method which decouples the flow space from the network topology. The flow space is partitioned with hosts rather than switches as carriers, which supports fine-grained and lightweight load balancing. Moreover, the switches are no longer needed to be assigned to different controllers and we keep all of them controlled by each controller, thus each flow request can be processed by exactly one controller in a centralized style. Evaluations show that our scheme reduces rule setup costs and achieves lightweight load balancing.

The centralized controller of SDN enables a global topology view of the underlying network. It is possible for the SDN controller to achieve globally optimized resource composition and utilization, including optimized end-to-end paths. Currently, resource composition in SDN arena is usually conducted in an imperative manner where composition logics are explicitly specified in high level programming languages. It requires strong programming and OpenFlow backgrounds. This paper proposes declarative path composition, namely Compass, which offers a human-friendly user interface similar to natural language. Borrowing methodologies from Semantic Web, Compass models and stores SDN resources using OWL and RDF, respectively, to foster the virtualized and unified management of the network resources regardless of the concrete controller platform. Besides, path composition is conducted in a declarative manner where the user merely specifies the composition goal in the SPARQL query language instead of explicitly specifying concrete composition details in programming languages. Composed paths are also reused based on similarity matching, to reduce the chance of time-consuming path composition. The experiment results reflect the applicability of Compass in path composition and reuse.

To efficiently use network resources, internet service providers need to conduct traffic engineering that dynamically controls traffic routes to accommodate traffic change with limited network resources. The performance of traffic engineering (TE) depends on the accuracy of traffic prediction. However, the size of traffic change has been drastically increasing in recent years due to the growth in various types of network services, which has made traffic prediction difficult. Our approach to tackle this issue is to separate traffic into predictable and unpredictable parts and to apply different control policies. However, there are two challenges to achieving this: dynamically separating traffic according to predictability and dynamically controlling routes for each separated traffic part. In this paper, we propose a macroflow-based TE scheme that uses different routing policies in accordance with traffic predictability. We also propose a traffic-separation algorithm based on real-time traffic analysis and a framework for controlling separated traffic with software-defined networking technology, particularly OpenFlow. An evaluation of actual traffic measured in an Internet2 network shows that compared with current TE schemes the proposed scheme can reduce the maximum link load by 34% (at the most congested time) and the average link load by an average of 11%.

In this paper, we use the MCA (Multi-Cache Approximation) algorithm to numerically determine cache hit probability in a multi-cache network. We then analytically obtain performance metrics for Content-Centric networking (CCN). Our analytical model contains multiple routers, multiple repositories (e.g., storage servers), and multiple entities (e.g., clients). We obtain three performance metrics: content delivery delay (i.e., the average time required for an entity to retrieve a content through a neighboring router), throughput (i.e., number of contents delivered from an entity per unit of time), and availability (i.e., probability that an entity can successfully retrieve a content from a network). Through several numerical examples, we investigate how network topology affects the performance of CCN. A notable finding is that content caching becomes more beneficial in terms of content delivery time and availability (resp., throughput) as distance between the entity and the requesting repository narrows (resp., widens).

Today's enterprise, data-center, and internet-service-provider networks deploy different types of network devices, including switches, routers, and middleboxes such as network address translation and firewalls. These devices are vertically integrated monolithic systems. Software-defined networking (SDN) and network function virtualization (NFV) are promising technologies for dis-aggregating vertically integrated systems into components by using “softwarization”. Software-defined networking separates the control plane from the data plane of switch and router, while NFV decouples high-layer service functions (SFs) or Network Functions (NFs) implemented in the data plane of a middlebox and enables the innovation of policy implementation by using SF chaining. Even though there have been several survey studies in this area, this area is continuing to grow rapidly. In this paper, we present a recent survey of this area. In particular, we survey research activities in the areas of re-architecting middleboxes, state management, high-performance platforms, service chaining, resource management, and trouble shooting. Efforts in these research areas will enable the development of future virtual-network-function platforms and innovation in service management while maintaining acceptable capital and operational expenditure.