Tomoya Tanjo

A first step towards resilient graph partitioning for electrical grids

We study a graph partitioning problem for electrical grids such that a given grid is partitioned into multiple ones that are self-contained concerning electricity balance. Our goal is to find a resilient partition against time-changing power demand and supply over the year. In this paper, we investigate two graph partitioning algorithms applying them to a synthesized dataset based on realistic assumptions about Yokohama, Japan. Our initial results show that a simple algorithm, which only considers horizontal or vertical partitions, possibly produces more resilient partitions than a more general algorithm whose partitions divide a graph into subgraphs of any topology.

Flexible graph partitioning of power grids with peer-to-peer electricity exchange

We study a clustering problem for electrical grids. Our goal is to find an optimal partition that minimizes the cost of constructing a set of self-sufficient microgrids. To obtain a better solution accommodating smaller microgrids, we develop a verification algorithm that determines whether microgrids can balance their electricity surplus through electricity exchange with each other. Our preliminary results show that our proposed method can effectively reduce the construction cost of decentralized microgrids.

Resilient Community Clustering: A Graph Theoretical Approach

Many complex systems can be modeled as a graph consisting of nodes and connecting edges. Such a graph-based model is useful to study the resilience of decentralized systems that handle a system failure by isolating a subsystem with failed components. In this chapter, we study a graph clustering problem for electrical grids where a given grid is partitioned into multiple microgrids that are self-contained in terms of electricity balance. Our goal is to find an optimal partition that minimizes the cost of constructing a set of self-sufficient microgrids. To obtain a better solution accommodating smaller microgrids, we develop an efficient verification algorithm that determines whether microgrids can balance their electricity surplus through electricity exchange among them. Our experimental results with a dataset about Yokohama city in Japan show that our proposed method can effectively reduce the construction cost of decentralized microgrids.

On Safety of Pseudonym-Based Location Data in the Context of Constraint Satisfation Problems

Pseudonymization is a promising technique for publishing a trajectory location data set in a privacy-preserving way. However, it is not trivial to determine whether a given data set is safely publishable against an adversary with partial knowledge about users’ movements. We therefore formulate this safety decision problem based on the framework of constraint satisfaction problems (CSPs) and evaluate its performance with a real location data set. We show that our approach with an existing CSP solver outperforms a polynomial-time verification algorithm, which is designed particularly for this safety problem.

A Portable Load Balancer for Kubernetes Cluster

Linux containers have become very popular these days due to their lightweight nature and portability. Numerous web services are now deployed as clusters of containers. Kubernetes is a popular container management system that enables users to deploy such web services easily, and hence, it facilitates web service migration to the other side of the world. However, since Kubernetes relies on external load balancers provided by cloud providers, it is difficult to use in environments where there are no supported load balancers. This is particularly true for on-premise data centers, or for all but the largest cloud providers. In this paper, we proposed a portable load balancer that was usable in any environment, and hence facilitated web services migration. We implemented a containerized software load balancer that is run by Kubernetes as a part of container cluster, using Linux kernels Internet Protocol Virtual Server(IPVS). Then we compared the performance of our proposed load balancer with existing iptables Destination Network Address Translation (DNAT) and the Nginx load balancers. During our experiments, we also clarified the importance of two network conditions to derive the best performance: the first was the choice of the overlay network operation mode, and the second was distributing packet processing to multiple cores. The results indicated that our proposed IPVS load balancer improved portability of web services without sacrificing the performance.

Implementation of Self-adaptive Middleware for Mobile Vehicle Tracking Applications on Edge Computing

Unstructured data gathered from various IoT sensors is rapidly increasing due to inexpensive electronic devices and high-speed networks. On the other hand, mobile edge computing (MEC) is an attractive data processing method that can shorten the communication distance and reduce the latency of computation-intensive tasks by distributing data to the edge servers close to the users, unlike processing data on clouds that are located far from users. In the present paper, we propose a specialized self-adaptive middleware for reconfiguration of image/video contents for adaptation to changes with the movement of a vehicle. The key concept behind this approach is to introduce the rule-based relocation of objects among sensor devices, edge servers, and existing clouds as a basic adaptation mechanism to recognize and track mobile vehicles. Experimental results show that tracking precision with a state-of-the-art tracker is up to 89% for MEC.

Dynamic Framework for Reconfiguring Computing Resources in the Inter-cloud and Its Application to Genome Analysis Workflows

This paper proposes a framework that dynamically reconfigures an application environment by adding and removing computing resources during runtime. The main idea is that the conditions for the resources used for reconfiguration can be translated into constraints on specifications, such as the number of cores, memory size, and resource location. Our framework consists of two subsystems: an application scheduler, which determines the constraints on specifications for each application, and a resource allocator, which finds resources that satisfy the constraints established by the application scheduler. This structure enables us to apply various reconfiguration strategies by replacing the application scheduler, and also enables us to investigate new allocation strategies for the resource allocator.

Architecture-Independent Cloud Computation for Sensor Environment and Its Applications.

Recently, due to the low cost of electronic devices and the spread of networks, researches on self-adaptive systems has become a boom by utilizing advanced Internet of Things (IoT) technology. However, many researches designed and built their systems on the basis of specific targets or specific applications using a fixed architecture. With the operation of such systems, various usage situations always change. This paper proposes a novel approach to dynamically changing the system architectures when its environments are changed. The key idea behind the proposed approach is to introduce a relocation of software components between various sensors as a basic mechanism for geographically inter-cloud systems. It is constructed as a middleware system, based on Docker for Java-based general-purposed self-adaptive sensor applications. This paper describes the design of our approach with several scenarios, e.g., dynamically adaptive the vehicle tracking system architecture among Peer-to-Peer, Client/Server and Ad-Hoc.