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.

Running IoT applications on edge computing infrastructures has the benefits of low response times and efficient bandwidth usage. System verification on a testbed is required to deploy IoT applications in production environments. In a testbed, Docker containers are preferable for a smooth transition of tested application programs to production environments. In addition, the round-trip times (RTT) of Docker containers to clients must be ensured, according to the target application's response time requirements. However, in existing testbed systems, the RTTs between Docker containers and clients are not ensured. Thus, we must undergo a large amount of configuration data including RTTs between all pairs of wireless base station nodes and servers to set up a testbed environment. In this paper, we present an edge computing testbed system with simple application programming interfaces (API) for testbed users that ensures RTTs between Docker containers and clients. The proposed system automatically determines which servers to place Docker containers on according to virtual regions and the RTTs specified by the testbed users through APIs. The virtual regions provide reduced size information about the RTTs in a network. In the proposed system, the configuration data size is reduced to one divided by the number of the servers and the command arguments length is reduced to approximately one-third or less, whereas the increased system running time is 4.3s.

In this paper, we propose a method to construct a scalable sensor data stream delivery system that guarantees the specified delivery quality of service (i.e., total reachability to destinations), even when delivery server resources (nodes) are in a heterogeneous churn situation. A number of P2P-based methods have been proposed for constructing a scalable and efficient sensor data stream system that accommodates different delivery cycles by distributing communication loads of the nodes. However, no existing method can guarantee delivery quality of service when the nodes on the system have a heterogeneous churn rate. As an extension of existing methods, which assign relay nodes based on the distributed hashing of the time-to-deliver, our method specifies the number of replication nodes, based on the churn rate of each node and on the relevant delivery paths. Through simulations, we confirmed that our proposed method can guarantee the required reachability, while avoiding any increase in unnecessary resource assignment costs.

A key-order preserving structured overlay network is a class of structured overlay network that preserves, in its structure, the order of keys to support efficient range queries. This paper presents a novel key-order preserving structured overlay network “Suzaku”. Similar to the conventional Chord#, Suzaku uses a periodically updated finger table as a routing table, but extends its uni-directional finger table to bi-directional, which achieves ⌈log2 n⌉-1 maximum lookup hops in the converged state. Suzaku introduces active and passive bi-directional finger table update algorithms for node insertion and deletion. This method maintains good lookup performance (lookup hops increase nearly logarithmically against n) even in churn situations. As well as its good performance, the algorithms of Suzaku are simple and easy to implement. This paper describes the principles of Suzaku, followed by simulation evaluations, in which it showed better performance than the conventional networks, Chord# and Skip Graph.

In the future Internet of Things/M2M network, enormous amounts of data generated from sensors must be processed and utilized by cloud applications. In recent years, sensor data stream delivery, which collects and sends sensor data periodically, has been attracting great attention. As for sensor data stream delivery, the receivers have different delivery cycle requirements depending on the applications or situations. In this paper, we propose a sensor data stream delivery method to accommodate heterogeneous cycles on the cloud. The proposed method uses distributed hashing to determine relay nodes on the cloud and construct delivery paths autonomously. We evaluate the effectiveness of the proposed method in simulations. The simulation results show that the proposed method halves the maximum load of nodes compared to the baseline methods and achieves high load balancing.