Off-chain storage is utilized to reduce on-chain storage costs, and further enhance the scalability of blockchain technology. For such mechanisms, transaction data of large size is stored in external centralized databases or distributed peer-to-peer storage, instead of blockchain nodes themselves. However, in emerging blockchain application areas, such as healthcare and the Internet of Things (IoT), off-chain data should be located close to the users with the right privileges, yet it is currently challenging to locate data close to such users and limit data transfers accordingly. To meet these challenges, we design a user-centric in-network caching mechanism for off-chain storage (UCINC) with information-centric networking (ICN) approach to regulate the data caching to the off-chain storage in the network where users are located. With UCINC, data is cached at off-chain storage based on location attributes, retrieved through the interest/data ICN paradigm, and users’ access privileges are determined based on their attributes including locations. We furthermore conduct simulation experiments to confirm that the proposed UCINC achieves higher download performance and traffic efficiency, compared with the existing off-chain storage mechanisms.

We present the LASK protocol and its platform implementation that supports distributed k-Nearest Service Discovery. LASK achieves scalable and locality-aware name-based service discovery and routing for the target nodes avoiding redundant lookup message exchanges across the edge networks.

Edge computing enables application services to leverage low-latency responses. This paper describes the design of infrastructure-as-a-service for edge computing (EC-IaaS) to realize multiple application services on the same edge servers. In the provision of application services, it is important for application service providers (ASPs) to satisfy proximity constraints regarding the edge servers hosting virtual machines (VMs). State-of-the-art methods to provide VMs satisfying these proximity constraints require ASPs to have a large amount of information from an EC-IaaS provider, which incurs large computational and communication overheads. Otherwise, the edge servers will incur the large power draw. In this paper, we propose a virtual region model. The virtual region model abstracts the details of an edge computing infrastructure. We extend the VM request and placement processes based on the virtual region model, to support dynamics of end-user devices. We confirm that the amount of information, which ASPs receive from an EC-IaaS provider, is the same level of the least amount required by the state-of-the-art methods. Additionally, the power draw of edge servers is the same as the minimum in the state-of-the-art methods, and this level is even sustained when the number of end-user devices changes in a city.

This paper presents an overlay network protocol called “LASK: Locality-Aware Service discovery protocol for K-nearest search”, for supporting scalable and locality-aware distributed k-Nearest Service Discovery (kNSD). The kNSD provides a lookup function to find k service nodes located close to the requester. Importance of such function increases under the situation of growth in size and the diversity of the resources in the so-called edge computing environments. In LASK, efficient cooperative routing algorithm is implemented utilizing the two-dimensional structure of key-order preserving structured overlay networks that are constructed according to the network topology and the name of the service. This structure achieves scalable and locality-aware routing of the lookup messages to the matched nodes avoiding redundant data transmissions across the edge networks. Extensive simulation evaluations show that LASK could achieve small discovery latency in the large-scale edge computing environments; a typical kNSD took less than 5 ms in a data center network model and less than 100 ms in the Internet model even when there are 100,000 nodes in the entire network.

In this paper, we propose a novel dynamic data flow platform for Internet of Things (IoT) applications in edge computing environments. To avoid the overloads on network and computational resources that are caused by IoT applications, the proposed platform replicates processes and changes the structure of the data flow dynamically on the distributed computational resources located at network edges and data centers. The proposed platform adds the notion of "index" to the existing distributed topic-based pub/sub (TBPS) messaging method to support flexible data flow definitions and process allocations. In addition, we propose a peer-to-peer-based data stream routing algorithm called "Locality-Aware Stream Routing (LASR)" which can change the data stream destination dynamically on extended TBPS in accordance with structural changes of a data flow, considering network communication localities. By simulation, we confirm that our proposed platform using LASR can change the data stream destination quicker than existing methods with a small overhead in an edge computing environment, even when running applications involving video analysis data flow with a high data rate.

Edge computing enables application services to leverage low-latency responses, e.g., for augmented reality. Infrastructure-as-a-service for edge computing (EC-IaaS) allows multiple application service providers (ASPs) to use a virtualized resource (i.e., virtual machines (VMs) and virtual networks) for various applications services on the common edge computing infrastructure. For ASPs, the proximity of the VMs to the end-user devices (i.e., the location of the host edge servers) is a significant factor in guaranteeing low-latency response times. The existing method allows ASPs to select the edge server locations so that the VMs satisfy the proximity requirement. However, the amount of information transmitted from the EC-IaaS provider to the ASP becomes large, because it has to include the latencies between all edge server locations and all wireless base stations. We propose a method that reduces the amount of information while ensuring that ASPs obtain VMs that satisfy the proximity requirement. An EC-IaaS provider suggests groups of wireless base stations that have VMs within a suitable proximity, which helps the ASPs to estimate the necessary number of VMs. Instead of the ASP, the EC-IaaS provider determines which edge servers should contain the VMs. We confirm that the proposed method reduces the transmitted information. The EC-IaaS provider also has a benefit that detail latency information is kept closed to ASPs and the power draw is sustained compared to the existing method.

Summary The Pacific Rim Application and Grid Middleware Assembly (PRAGMA) is an international community of researchers that actively collaborate to address problems and challenges of common interest in eScience. The PRAGMA Experimental Network Testbed (PRAGMA‐ENT) was established with the goal of constructing an international software‐defined network (SDN) testbed to offer the necessary networking support to the PRAGMA cyberinfrastructure. PRAGMA‐ENT is isolated, and PRAGMA researchers have complete freedom to access network resources to develop, experiment, and evaluate new ideas without the concerns of interfering with production networks. In the first phase, PRAGMA‐ENT focused on establishing an international L2 backbone. With support from the Florida Lambda Rail, Internet2, PacificWave, Japan Gigabit Network, and TaiWan Advanced Research and Education Network, PRAGMA‐ENT backbone connects openflow‐enabled switches at University of Florida, University of California, San Diego, Nara Institute of Science and Technology (Japan), Osaka University (Japan), National Institute of Advanced Industrial Science and Technology (Japan), and National Applied Research Laboratories (Taiwan). The second phase of PRAGMA‐ENT consisted of an evaluation of technologies for the control plane that enables multiple experiments (ie, OpenFlow controllers) to coexist. Preliminary experiments with FlowVisor revealed some limitations leading to the development of a new approach, called AutoVFlow. This paper describes our experience in the establishment of PRAGMA‐ENT backbone (with international L2 links), its current status, and plans for the control plane. Discussion of preliminary application ideas, including optimization of routing control; multipath routing control; extending the backbone using overlay network; and remote visualization are also discussed.

Bandwidth and latency are two major factors that contribute the most to network application performance. Between each pair of switches in a network, there may be multiple paths connecting them. Each path has different properties because of multiple factors. Traditional shortest-path routing does not take this knowledge into consideration and may result in sub-optimal performance of applications and underutilization of network. We proposed a concept of "bandwidth and latency aware routing". The idea is that we could improve overall performance of the network by separating application into bandwidth-oriented and latency-oriented application and allocate different route for each type of application accordingly. We also proposed a design of this network system implemented using Open Flow. Routes are calculated from monitored information using Dijkstra algorithm and its variation. To support our design, we show a use case in which our design performs better than traditional routing as well as evaluation results.

In the era of cloud computing, data centers that accommodate a series of user-requested jobs with a diversity of resource usage pattern need to have the capability of efficiently distributing resources to each user job, based on individual resource usage patterns. In particular, for high-performance computing as a cloud service which allows many users to benefit from a large-scale computing system, a new framework for resource management that treats not only the CPU resources, but also the network resources in the data center is essential. In this paper, an SDN-enhanced JMS that efficiently handles both network and CPU resources and as a result accelerates the execution time of user jobs is introduced as a building block technology for such a HPC cloud. Our evaluation shows that the SDN-enhanced JMS efficiently leverages the fat-tree interconnect of cluster systems running behind the cloud to suppress the collision of communications generated by different jobs.