With the recent development of mobile computing technologies, mobile terminals such as smartphones and tablet PCs are rapidly and explosively spread over the world. In wireless network technology, long-term evolution (LTE) services become popular, which enables wireless data communication faster than ever before. In the future, it is expected that information and contents services for mobile terminals will be diversifying, and that the resulting amount of data communications from and to mobile terminals will massively increase. In computing service, scale-out oriented cloud computing becomes popular, in which big data with the order of Tera/Peta-byte size is simultaneously processed in parallel by a large number of commodity servers.
In the future, it is expected that large-scale data processing and virtulization services over cloud computing environments are provided to a large number of mobile termininals. Expected application services are high-speed massive-data storage service, fast content-delivery service, streaming services of high-quality audio and high-definition TV, and interactive 3D communicatoin service. In such mobile-cloud information services environments, more sophisticated design of large-scale systems and more effective resource management are indispensable.
Large-Scale Systems Management Lab. research is aimed at developing mathematical modeling and simulation techniques for design, control and architecture of large-scale systems such as large-scale data centers and computer/communication networks, with which the resulting systems achieve high performance, low vulnerability and highly efficient energy saving. Our research focus is on network-science oriented design framework, fundamental technologies and highly-qualified services, in particularly for large-scale computer and/or communication network systems.
Blockchain-based IoT Access Control
Thanks to the rapid advance of communication and networking technologies (e.g., Wi-Fi, Zigbee, Bluetooth), a growing number of objects (e.g., sensors, smart user devices, servers) are being interconnected nowadays via unique addressing schemes (e.g., the Internet), leading to the concept of the Internet of things (IoT). Such interconnection significantly accelerates the data collection, aggregation and sharing among all peers in the IoT, whereas it incurs crucial security issues at the same time, as adversaries can illegally access the resources and services provided in the system by compromising the vulnerable IoT devices. As a result, access control has been regarded as a critical research issue in the IoT. Existing centralized access control schemes, which usually rely on a single node to control the access requests from a subject to an object, may suffer from two significant shortcomings. One is that the access control node may be compromised by an adversary, leading to untrustworthy access control. The other is that the access control node may be destroyed in natural or man-made disasters, which can easily destroy the access control scheme. Thus, distributed and trustworthy access control schemes are in urgent demand to prevent unauthorized access in IoT systems. Recently, blockchain, which is the key enabler behind modern cryptocurrency platforms (e.g., BitCoin and Ethereum) and can implement distributed trustworthy computation in an untrustworthy peer-to-peer system, may provide us a promising solution to the access control problem for the IoT. Therefore, the goal of this research is to implement distributed and trustworthy access control for IoT systems by exploiting the emerging blockchain technology. In particular, we will on the blockchain-based smart contract technology.
Selfish yet Optimal Control based on Game Theoretic Approaches
We have been supported by various social systems, e.g., the Internet and transportation networks. It is natural for individuals to desire better quality of experience (QoE), e.g., watching high quality video without delay and jitter, and faster travel to the destination. On the other hand, the resources of systems are limited. For example, in case of content distribution services, such resources can be the computation capacity of servers and network capacity. From the viewpoint of the overall systems, global optimization, e.g., improving the total QoE among all users, under the resource constraint is important. In this research, we aim to achieve the global optimization under the mutual interactions among users’ selfish decision making.
Network Optimization for Large-Scale Content Distribution
Software update and video streaming require massive content and/or long-term content distribution. Specifically, new release of content causes sudden increase of requests, and thus the distribution server tends to be a bottleneck. To tackle this problem, several systems, e.g., Windows update, recently apply Peer-to-Peer (P2P) file distribution where clients called peers upload retrieved fragments of the whole content, i.e., pieces, to other peers. However, some peers will not be willing to upload pieces to others, which are called free riders, due to communication overhead. Tit-for-Tat (TFT) strategy in game theory can alleviate such free riding behavior by encouraging equivalent exchange of pieces among each pair of peers. In this research, the determination problem of optimal piece flow in the TFT-based P2P file distribution is formulated as integer linear programming (ILP). With the help of knowledge obtained by analyzing the optimal piece flow, we aim to achieve distributed optimal content distribution.
Mobile-Edge Collaborative Automatic Evacuation Guiding and Risk Analysis
In the 2011 Great East Japan Earthquake, both fixed and mobile communication networks had been unavailable for long time and in wide areas, due to damage of information communication infrastructures. As a result, it has been reported that there were many cases where evacuees and rescuers could not collect and distribute important information, e.g., damage information, evacuation information, and government information. In this research, we aim to achieve an evacuation guiding system that can speedily and safely navigate evacuees to appropriate refuges. Specifically, we tackle this problem by combining several functions, e.g., automatic evacuation under implicit collaboration among evacuees and their mobile devices, road network state information sharing among evacuees, and road network risk analysis using geographical big-data.
Vulnerability Analysis of Competitive Pull-based Broadcast
Broadcast is one of the powerful schemes to speedily distribute information over networks. When the information size is relatively large, pull-based broadcast is used. In the pull-based broadcast, a sending node first transfers small-size meta information of the original information to the neighboring node. Then, the receiver node only requests the original information if it is missing. The cryptocurrency system, Bitcoin, is one of the systems applying the pull-based broadcast for distributing blocks, each of which is a chunk of transactions and becomes up to 1 MB. On the other hand, it has been pointed out that the pull-based broadcast has vulnerability of delaying information propagation, which can be achieved by misuse of timeout mechanisms. In case of the Bitcoin system, delaying information propagation will affect the competition among miners. In this research, we aim to reveal the vulnerability of the pull-based broadcast with the help of epidemiology-inspired mathematical modeling and event-driven simulators.
In cloud computing services, large-scale parallel data processing is realized with so-called scale-out computing environment with a huge number of commodity servers. In large-scale parallel data processing framework, a large-sized job task is divided into a number of small-sized subtasks, and each subtask is processed by its own worker machine, resulting in a high task throughput. When a large number of subtasks are processed with a large number of worker machines in a distributed computing manner, however, hardware failures and software malfunctioning are likely to occur among a not-small number of worker machines, making some subtask processing times extremely large. As a result, the overall task processing time is large and the task-level throughput is degraded significantly. This problem is widely known as the issue of stragglers. In the future, data centers with a huge number of server machines will be connected by each other with ultra high-speed data communication technology. In such a huge-scale data-center computing environment, more sophisticated computing resource management and more elaborate task scheduling are indispensable in order for both high task throughput and efficient energy saving. Here, we focus on scale-out cloud and mobile cloud environments. In order to realize ultimate scale-out cloud computing environment, we study efficient computing framework and energy-efficient task scheduling for processing big data with a huge number of worker machines. For mobile cloud computing, we study dynamic and elastic management schemes for computing and networking resources.
System Analytics Based on Network Science
Network Science (also called Internet Science or Network Science of Complex Systems) is one of emerging interdisciplinary fields for characterizing the nature of information networks. Network Science is based on not only the conventioanl networking theories such as queueing theory and network optimization, but also mathematical and physical engineering, social economics, and cognitive sicence. In LSM laboratory, focusing on network modeling, network performance analytics, and designing methods for high-performance computer/communication networks, we study theoretical approaches based on stochastic analysis and probability models such as Markov chains and extreme value theory, scheduling algorithms for information packet flows, and high-speed simulation techniques for large-scale network systems.
With the recent development of informatin and communication technology (ICT), “service” provided by companies to customers is increasingly diversified. In general, the way of service is developed within a company, and the quality of service depends on empirical rules of thumbs of the company. Service science is an interdisciplinary approach to the design, implementation and improvement of service systems, whose ultimate goal is to create service innovation. Our approach to service science is based on not only conventioanl Operations Research and Management Science, but also the analysis of economical aspects of service, social-scientific analysis of the quality of service, and characterization of management quality. Currently, we are studying the cost-effective design and operator-management of call centers.
- System Analytics
- Mathematical modeling and analytics of large-scale complex systems such as cloud computing environments and network systems
- System Design
- Design, control and architecture for high-performance complex systems, queueing theory, Markov analysis
- Service Science
- Analytics, evaluation and implementation of high-quality service systems, operations research, management science
- Discrete-event simulation
- Modeling and high-speed simulation techniques for large-scale complex systems, Monte-Carlo simulation
- Online Algorithm
- Optimization problems and its algorithms for situations without future knowledge
- Mechanism Design
- Designing mechanism, auction theory, selfish routing