In today’s scenario, energy efficiency has become one of the most crucial issues for Data Center Networks (DCN). This paper analyses the energy saving capability of a Data center network using Segment Routing (SR) based model within a Software Defined Network (SDN) architecture. Energy efficiency is measured in terms of number of links turned off and for how long the links remain in sleep mode. Apart from saving the energy by turning off links, our work further efficiently manages the traffic within the available links by using Per-packet based load balancing approach. Aiming to avoid congestion within DCN’s and increase the sleeping time of inactive links. An algorithm for deciding the particular set of links to be turned off within a network is presented. With the introduction of per-packet approach within SR/SDN model, we have successfully saved 21 % of energy within DCN topology. Results show that the proposed Per-packet SR model using Random Packet Spraying (RPS) saves more energy and provides better performance as compared to Per-flow based SR model, which uses Equal Cost Multiple Path (ECMP) for load balancing. But, certain problems also come into picture using per-packet approach, like out of order packets and longer end to end delay. To further solidify the effect of SR in saving energy within DCN and avoid previously introduced problems, we have used per-flow based Flow Reservation approach along with a proposed Flow Scheduling Algorithm. Flow rate of all incoming flows can be deduced using Flow reservation approach, which is further used by Flow Scheduling Algorithm to increase Bandwidth utilization Ratio of links. Ultimately, managing the traffic more efficiently and increasing the sleeping time of links, leading to more energy savings. Results show that, the energy savings are almost similar in per-packet based approach and per-flow based approach with bandwidth reservation. Except, the average sleeping time of links in per-flow based approach with bandwidth reservation decreases less severely as compared to per-packet based approach, as overall traffic load increases.

Over a period of time, it has been studied that a mobile “edge-cloud” formed by hand-held devices could be a productive resource entity for providing a service in the mobile cloud landscape. The ease of access to a pool of devices is much more arbitrary and based purely on the needs of the user. This pool can act as a provider of an infrastructure for various services that can be processed with volunteer node participation, where the node in the vicinity is itself a service provider. This representation of cloud formation to engender a constellation of devices in turn providing a service is the basis for the concept of Mobile Ad-hoc Cloud Computing. In this thesis, an architecture is designed for providing an Infrastructure as a service in Mobile Ad-hoc Cloud Computing. The performance evaluation reveals the gain in execution time while offloading to the mobile ad-hoc cloud. Further, this novel architecture enables discovering a dedicated pool of volunteer devices for computation. An optimized task scheduling algorithm is proposed that provides a coordinated resource allocation. However, failure to maintain the service between heterogeneous networks shows the inability of the present day networks to adapt to frequent changes in a network. Thus, owing to the heavy dependence on the centralized mobile network, the service related issues in a mobile ad-hoc cloud needs to be addressed. As a result, using the principles of Software Defined Networking (SDN), a disruption tolerant Mobile Ad-hoc Cloud framework is proposed. To evaluate this framework a comprehensive case study is provided in this work that shows a round trip time improvement using an SDN controller.

In this thesis, we address the problem of service function chaining in a network. Currently, problems of chaining services in a network (i.e. service function chaining) can be broadly categorised into middlebox placement in a network and packet steering through middleboxes. In this work, we present a packet steering approach - One Pass Packet Steering (OPPS) - for use in multi-subscriber environments, with the aim that subscribers having similar policy chain composition should experience the same network performance. We develop and show algorithms with a proof of concept implementation using emulations performed with Mininet. We identify challenges and examine how OPPS could benefit from the Software Defined Data Center architecture to overcome these challenges. Our results show that, given a fixed topology and different sets of policy chains containing the same middleboxes, the end-to-end delay and throughput performance of subscribers using similar policy chains remains approximately the same. Also, we show how OPPS can use a smaller number of middleboxes and yet, achieve the same hop count as that of a reference model described in a previous work as ideal, without violating the subscribers' policy chains.

abstract: The recent increase in users of cellular networks necessitates the use of new technologies to meet this demand. Massive multiple input multiple output (MIMO) communication systems have great potential for increasing the network capacity of the emerging 5G+ cellular networks. However, leveraging the multiplexing and beamforming gains from these large-scale MIMO systems requires the channel knowlege between each antenna and each user. Obtaining channel information on such a massive scale is not feasible with the current technology available due to the complexity of such large systems. Recent research shows that deep learning methods can lead to interesting gains for massive MIMO systems by mapping the channel information from the uplink frequency band to the channel information for the downlink frequency band as well as between antennas at nearby locations. This thesis presents the research to develop a deep learning based channel mapping proof-of-concept prototype. Due to deep neural networks' need of large training sets for accurate performance, this thesis outlines the design and implementation of an autonomous channel measurement system to analyze the performance of the proposed deep learning based channel mapping concept. This system obtains channel magnitude measurements from eight antennas autonomously using a mobile robot carrying a transmitter which receives wireless commands from the central computer connected to the static receiver system. The developed autonomous channel measurement system is capable of obtaining accurate and repeatable channel magnitude measurements. It is shown that the proposed deep learning based channel mapping system accurately predicts channel information containing few multi-path effects. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2020

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22590号 / 情博第727号 / 新制||情||124(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 橋口 浩之, 教授 山本 衛, 准教授 村田 英一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This study aims to examine the effects of transforming a monolithic server system into a microservice architecture, focusing on the increased latency introduced by using a microservice orchestrator. The microservice orchestrator was implemented using an OpenFlow switch controlled by the Beacon and Ryu OpenFlow controllers. These controllers, along with the round robin, random assign and a server-aware load balancing algorithm, were all compared in order to find the combination resulting in the lowest latency and highest achieved server balance in varying network environments. We show that the OpenFlow switch enforces a client-aware load balancing policy and that only the initial request is handled by the controller, effectively reducing the importance of choosing the optimal OpenFlow controller. In addition, the round robin load balancer was preferred when dealing with homogeneous requests, and a server-aware load balancer was required for heterogeneous requests. For most requests, the system would only slow down by a few microseconds using the proposed architecture. However, for 0.001\% of all requests, the slowdown was much more significant, with each of those requests being at least 100 times slower than when using a monolithic server architecture.

This master's thesis deals with the design and implementation of universal user interface for detection devices used in Laser-induced breakdown spectroscopy. The design and implementation are based on analysis of current state of instrumentation used in laser spectroscopy. The user interface should be able to work with high repetition frequency of measurement. Acquired spectra should be visualised to user and it is also necessary to save this data on hard drive. The resulting application must be universal. It means the application must support various types and vendors of detection devices.

The Internet of Things (IoT) is a technology aimed at developing a global network of machines and devices that can interact and communicate with each other. Supporting IoT, therefore, requires revisiting the Internet’s best effort service model and reviewing its complex communication patterns. In this dissertation, we explore the unique characteristics of IoT traffic and examine IoT systems. Our work is motivated by the new capabilities offered by modern Software Defined Networks (SDN) and blockchain technology. We evaluate IoT Quality of Service (QoS) in traditional networking. We obtain mathematical expressions to calculate end-to-end delay, and dropping. Our results provide insight into the advantages of an intelligent edge serving as a detection mechanism. Subsequently, we propose SADIQ, SDN-based Application-aware Dynamic Internet of things QoS. SADIQ provides context-driven QoS for IoT applications by allowing applications to express their requirements using a high-level SQL-like policy language. Our results show that SADIQ improves the percentage of regions with an error in their reported temperature for the Weather Signal application up to 45 times; and it improves the percentage of incorrect parking statuses for regions with high occupancy for the Smart Parking application up to 30 times under the same network conditions and drop rates. Despite centralization and the control of data, IoT systems are not safe from cyber-crime, privacy issues, and security breaches. Therefore, we explore blockchain technology. In the context of IoT, Byzantine fault tolerance-based consensus protocols are used. However, the blockchain consensus layer contributes to the most remarkable performance overhead especially for IoT applications subject to maximum delay constraints. In order to capture the unique requirements of the IoT, consensus mechanisms and block formation need to be redesigned. To this end, we propose Synopsis, a novel hierarchical blockchain system. Synopsis introduces a wireless-optimized Byzantine chain replication protocol and a new probabilistic data structure. The results show that Synopsis successfully reduces the memory footprint from Megabytes to a few Kilobytes with an improvement of 1000 times. Synopsis also enables reductions in message complexity and commitment delay of 85% and 99.4%, respectively

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22809号 / 情博第739号 / 新制||情||126(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 大木 英司, 教授 守倉 正博, 教授 原田 博司 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This thesis covers utilization of software defined networks for lawful interception purposes. Based on specific implementation of lawful interception system SLIS developed by Sec6Net group, suggests improvements aiming at more precise identification of intercepted users and better effectivity of system resources. First aim is achieved by implementation of a new module for dynamic identification component while the other one alters configuration mechanism for probes and OpenFlow switches.