京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第18406号 / 情博第521号 / 新制||情||92(附属図書館) / 31264 / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 太田 快人, 教授 梅野 健, 教授 大塚 敏之 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Proteins provide evidence that a given gene is expressed, and machine learning algorithms can be applied to various proteomics problems in order to gain information about the underlying biology. This dissertation applies machine learning algorithms to proteomics data in order to predict whether or not a given peptide is observable by mass spectrometry, whether a given peptide can serve as a cell penetrating peptide, and then utilizes the peptides observed through mass spectrometry to aid in the structural annotation of the chicken genome. Peptides observed by mass spectrometry are used to identify proteins, and being able to accurately predict which peptides will be seen can allow researchers to analyze to what extent a given protein is observable. Cell penetrating peptides can possibly be utilized to allow targeted small molecule delivery across cellular membranes and possibly serve a role as drug delivery peptides. Peptides and proteins identified through mass spectrometry can help refine computational gene models and improve structural genome annotations.

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<p>Microservices have emerged as a popular pattern for developing large-scale applications in cloud environments for their flexibility, scalability, and agility benefits. However, microservices make management more complex due to their scale, multiple languages, and distributed nature. Orchestration and automation tools like Kubernetes help deploy microservices running simultaneously, but it can be difficult for an operator to understand their behaviors, interdependencies, and interactions. In such a complex and dynamic environment, performance problems (e.g., slow application responses and high resource usage)  require significant human effort spent on diagnosis and recovery. Moreover, manual diagnosis of cloud microservices tends to be tedious, time-consuming, and impractical. Effective and automated performance analysis and anomaly detection require an observable system, which means an application's internal state can be inferred by observing and tracking metrics, traces, and logs. Traditional APM uses libraries and SDKs to improve application monitoring and tracing but has additional overheads of rewriting, recompiling, and redeploying the applications' code base. Therefore, there is a critical need for a standardized automated microservices observability solution that does not require rewriting or redeploying the application to keep up with the agility of microservices.</p> <p><br></p> <p>This thesis studies observability for microservices and implements an automated Extended Berkeley Packet Filter (eBPF) based observability solution. eBPF is a Linux feature that allows us to write extensions to the Linux kernel for security and observability use cases. eBPF does not require modifying the application layer and instrumenting the individual microservices. Instead, it instruments the kernel-level API calls, which are common across all hosts in the cluster. eBPF programs provide observability information from the lowest-level system calls and can export data without additional performance overhead. The Prometheus time-series database is leveraged to store all the captured metrics and traces for analysis. With the help of our tool, a DevOps engineer can easily identify abnormal behavior of microservices and enforce appropriate countermeasures. Using Chaos Mesh, we inject anomalies at the network and host layer, which we can identify with root cause identification using the proposed solution. The Chameleon cloud testbed is used to deploy our solution and test its capabilities and limitations.</p>

Large scale Internet services are increasingly implemented as distributed systems in order to achieve fault tolerance, availability, and scalability. When requests traverse multiple services, end-to-end metrics no longer tell a clear picture. Distributed tracing emerged to break down end-to-end latency on a per service basis, but only answers where a problem occurs, not why. From user research we found that root-cause analysis of performance problems is often still done by manually correlating information from logs, stack traces, and monitoring tools. Profilers provide fine-grained information, but we found they are rarely used in production systems because of the required changes to existing applications, the substantial storage requirements they introduce, and because it is difficult to correlate profiling data with information from other sources. The proliferation of modern low-overhead profilers opens up possibilities to do online always-on profiling in production environments. We propose Protractor as the missing link that exploits these possibilities to provide distributed profiling. It features a novel approach that leverages service meshes for application-level transparency, and uses anomaly detection to selectively store relevant profiling information. Profiling information is correlated with distributed traces to provide contextual information for root-cause analysis. Protractor has support for different profilers, and experimental work shows impact on end-to-end request latency is less than 3%. The utility of Protractor is further substantiated with a survey showing the majority of the participants would use it frequently / Storskaliga Internettjänster implementeras allt oftare som distribuerade system för att uppnå feltolerans, tillgänglighet och skalbarhet. När en request spänner över flera tjänster ger inte längre end-to-end övervakning en tydlig bild av orsaken till felet. Distribuerad tracing utvecklades för att spåra end-to-end request latency per tjänst och för att ge en indikation vart problemet kan ligger med visar oftas inte orsaken. Genom user research fann vi att root-cause-analys av prestandaproblem ofta fortfarande görs genom att manuellt korrelera information från loggar, stack traces och övervakningsverktyg. Kod-profilering tillhandahåller detaljerad information, men vi fann att den sällan används i produktionssystem på grund av att de kräver ändringar i den befintliga koden, de stora lagringskraven som de introducerar och eftersom det är svårt att korrelera profilerings data med information från andra källor. Utbredning av moderna kodprofilerare med låg overhead öppnar upp möjligheten att kontinuerligt köra dem i produktionsmiljöer. Vi introducerar Protractor som kombinerar kodprofilering och distribuerad tracing. Genom att utnyttja och bygga på koncept så som service meshes uppnår vi transparens på applikationsnivå och använder anomalitetsdetektering för att selektivt lagra relevant profileringsinformation. Den informationen korreleras med distribuerade traces för att ge kontext för root-cause-analys. Protractor har stöd för olika kodprofilerare och experiment har visat att påverkan på end-to-end request latency är mindre än 3Användbarheten av Protractor är ytterligare underbyggd med en undersökning som visar att majoriteten av deltagarna skulle använda den ofta.

This thesis examines decentralized meta-reasoning. For a single agent or multiple agents, it may not be enough for agents to compute correct decisions if they do not do so in a timely or resource efficient fashion. The utility of agent decisions typically increases with decision quality, but decreases with computation time. The reasoning about one's computation process is referred to as meta-reasoning. Aspects of meta-reasoning considered in this thesis include the reasoning about how to allocate computational resources, including when to stop one type of computation and begin another, and when to stop all computation and report an answer. Given a computational model, this translates into computing how to schedule the basic computations that solve a problem. This thesis constructs meta-reasoning strategies for the purposes of monitoring and control in multi-agent settings, specifically settings that can be modeled by the Decentralized Partially Observable Markov Decision Process (Dec-POMDP). It uses decision theory to optimize computation for efficiency in time and space in communicative and non-communicative decentralized settings. Whereas base-level reasoning describes the optimization of actual agent behaviors, the meta-reasoning strategies produced by this thesis dynamically optimize the computational resources which lead to the selection of base-level behaviors.

5G is becoming reality with companies rolling out the technology around the world. In 5G,the Radio Access Network (RAN) is moving from a monolithic-based architecture into a cloud-based microservice architecture for the purpose of simplifying deployment and manageability,and explore scalability and flexibility. Thus, the transition of functionalities from a proprietaryhardware-based system into a more distributed and flexible virtualized system is ongoing. Insuch systems, legacy methods performance monitoring is relevant, wheresystem tracingplaysan important role. System tracing is important for the purpose of performance analysis of anygiven system. However, current tools were designed thinking about monolith architectureswhere, therefore, in new distributed architectures, new tracing tools need to be developed. System tracing often requires special permissions to be executed in applications running ina virtualized third-party environment. Unfortunately, not all applications running in a dis-tributed virtualized environment can be given such special access, at the risk of compromis-ing security and stability of the system. However, tracing data needs to be also collected fromapplications running in such environments. This thesis addresses the challenge of remotely configuring and controlling the system tracingtool with the example of LTTng in applications that run as part of a distributed virtualizedenvironment with Kubernetes. We explore the problem of remotely controlling and configuringsystem tracing as well as to optimize data collection. The main outcome is a tool able to re-motely control and configure system tracing tools. In addition, a proof-of-concept is presentedwith working demos for basic system tracing commands. It was discovered that a relay-based solution can be exposed outside the cluster via node-portwhich can relay incoming requests on-wards to any number of microservices. However, dis-covery of these microservices that are running system tracing tools is critial. Service discoverymechanism’s were brought forth and introduced to the system for the purpose of disoveringmicroservices with system tracing tools. Tracing data that is saved locally can be extracted bythe user through the relay-based solution or sent directly to any remote system using LTTngrelay daemon functionality. Comparison between directly executing commands in a bash shelland the remote CLI was measured. It has been concluded that the overall the response timeof both Linux and LTTng commands that are sent through the remote CLI is 1.96 times longerthan directly executing these commands in a bash shell. This was accounted to the fact thatcommands sent over the network traffic within the kubernetes cluster which is the cost ofremotely being able to control and configure system tracing tools. This being said, there arestill many steps that can be taken to improve the solution and to develop a more productionready solution.i

<p dir="ltr">With the increase in planned space missions in cislunar space, it is necessary to study the ability of observers to observe and track objects in this regime. This thesis focuses on creating a sensor tasking algorithm for constellations of optical observers to efficiently observe cislunar objects. The circular restricted three body problem is used for the dynamics of the objects while the bi-circular restricted four body problem is used to approximate the position of the sun.</p><p dir="ltr">A new way of discretizing the field of regard is proposed that respects the observers field of view on the unit sphere. A method for providing feedback to the observer in a delayed feedback environment is applied to mean state, single Gaussian, and particle representations of uncertainty. The method of determining a scaling coefficient for Sanson’s probability of detection is recorded. Sanson’s probability of detection is studied for determining the correct effective aperture dimensions of an optical observer. An approximation is presented for expediting calculations of Sanson’s probability of detection. An uncertainty propagation analysis shows there is an efficient number of particles to use for particle uncertainty far below the required number for a full Monte Carlo particle uncertainty representation. </p><p dir="ltr">Mean state, single Gaussian and particle methods of uncertainty characterization are compared in a cislunar simulation showing the benefits of the particles solution over other forms of uncertainty characterization. Particles are not only an effective uncertainty representation in a delayed feedback environment, they are computationally feasible for the sensor tasking problem. The performance of the particle algorithm for a constellation of observers is evaluated in a simulated small satellite breakup in a Lyapunov orbit and a simulated breakup of the proposed Lunar Gateway. The performance of observers in direct retrograde, low lunar, geosynchronous, and northern Halo orbits are evaluated in the breakup simulations. Results from these simulations show that observers in low lunar and Halo orbits can be valuable observation standpoints in breakups around the near-Moon region of cislunar space.</p>

Real time monitoring of the shipboard power system is a complex task to address. Unlike the terrestrial power system, the shipboard power system is a comparatively smaller system but with more complexity in terms of its system operation. This requires the power system to be continuously monitored to detect any type of fluctuations or disturbances. Planning metering systems in the power system of a ship is a challenging task not only due to the dimensionality of the problem, but also due to the need for reducing redundancy while improving network observability and efficient data collection for a reliable state estimation process. This research is geared towards the use of a Genetic Algorithm for intelligent placement of meters in a shipboard system for real time power system monitoring taking into account different system topologies and critical parameters to be measured from the system. The algorithm predicts the type and location of meters for identification and collection of measurements from the system. The algorithm has been tested with several system topologies.

This thesis focuses on utilizing the persistent voltage vector injections by finite control set model predictive control (FCSMPC) to enable simultaneous estimations of both position and parameters in order to realize robust sensorless interior permanent magnet synchronous machine (IPMSM) drives valid at the entire operating region including no-load standstill without any additional signal injection and switchover. The system (here, IPMSM) needs to meet certain observability conditions to identify the parameters and position. Moreover, each combination of the parameters and/or position involves different observability requirements which cannot be accomplished at every operating point. In particular, meeting the observability for parameters and position at no-load standstill is more challenging. This is overcome by generating persistent excitation in the system with high-frequency signal injection. The FCSMPC scheme inherently features the persistent excitation with voltage vector injection and hence no additional signal injection is required. Moreover, the persistent excitation always exists for FCSMPC except at the standstill where the control applies the null vectors when the reference currents are zero. However, introducing a small negative d axis current at the standstill would be sufficient to overcome this situation.The parameter estimations are investigated at first in this thesis. The observability is analyzed for the combinations of two, three and four parameters and experimentally validated by online identification based on recursive least square (RLS) based adaptive observer. The worst case operating points concerning observability are identified and experimentally proved that the online identification of all the parameter combinations could be accomplished with persistent excitation by FCMPC. Moreover, the effect of estimation error in one parameter on the other known as parameter coupling is reduced with the proposed decoupling technique. The persistent voltage vector injections by FCSMPC help to meet the observability conditions for estimating the position, especially at low speeds. However, the arbitrary nature of the switching ripples and absence of PWM modulator void the possibility of applying the standard demodulation based techniques for FCSMPC. Consequently, a nonlinear optimization based observer is proposed to estimate both the position and speed, and experimentally validated from standstill to maximum speed. Furthermore, a compensator is also proposed that prevents converging to saddle and symmetrical ( ambiguity) solutions. The robustness analysis of the proposed nonlinear optimization based observer shows that estimating the position without co-estimating the speed is more robust and the main influencing parameters on the accuracy of the position estimation are d and q inductances. Subsequently, the proposed nonlinear optimization based observer is extended to simultaneously estimate the position, d and q inductances. The experimental results show the substantial improvements in response time, and reduction in both steady and transient state position errors. In summary, this thesis presents the significance of persistent voltage vector injections in estimating both parameter and position, and also shows that nonlinear optimization based technique is an ideal candidate for robust sensorless FCSMPC. / Thesis / Doctor of Philosophy (PhD)