The main goal of this thesis is to create a monitoring platform and library that can be used to monitor distributed Java-based applications. This work is inspired by Google Dapper and shares a concept called "Span" with the aforementioned project. Spans represent a small specific part of the computation and are used to capture state among multiple communicating nodes. In order to be able to col- lect spans without recompiling the original application's code, instrumentation techniques are highly used in the thesis. The monitoring tool, which is called Distrace, consists of two parts: the native agent and the instrumentation server. Users of the Distrace tool are supposed to extend the instrumentation server and specify the points in their application's code where new spans should be created and closed. In order to achieve high performance and affect the running appli- cation at least as possible, the instrumentation server is used for instrumenting the code. The Distrace tool is aimed to have a small foot-print on the monitored applications, should be easy to deploy and is transparent to target applications from the point of view of the final user. 1

Dynamic adaptivity of a computer system is its ability to modify the behavior according to the environment in which it is executed. It allows the system to achieve better performance, but usually requires specialized architecture and brings more complexity. The thesis presents an analysis and design of a framework that allows simple and fluent performance-based adaptive development at the level of functions and methods. It closely examines the API requirements and possibilities of integrating such a framework into the Scala programming language using its advanced syntactical constructs. On theoretical level, it deals with the problem of selecting the most appropriate function to execute with given input based on measurements of previous executions. In the provided framework implementation, the main stress is laid on modularity and extensibility, as many possible future extensions are outlined. The solution is evaluated on a variety of development scenarios, ranging from input adaptation of algorithms to environment adaptations of complex distributed computations in Apache Spark.

In this paper, we introduce a framework for the adaptive control of universal mobile telecommunications system (UMTS) networks in order to improve bandwidth utilization of the radio channels. The key contribution of the paper constitutes the introduction of a performance management information base for dynamically adjusting the packet scheduler and admission controller. Thus, the adaptive control framework closes the loop between network operation and network control. Furthermore, the adaptive control framework can effectively deal with the different time scales of packet scheduling and admission control. Moreover, we present a traffic model for non-real-time UMTS traffic based on measured trace data. The analysis and scaling process of the measured trace data with respect to different UMTS bandwidth classes constitutes the basic concept of this traffic characterization. Using this traffic model and simulation on the IP level, the gain of employing the adaptive control framework is illustrated by performance curves for various quality of service measures.

Background – Over the years, management control has become an agenda in the research field, and new frameworks have been developed to study organizational control and measure company performance. Management Control Systems (MCS) development has gained attention with the trend of young, technology-driven companies becoming substantial contributors to the economy. Because of the sudden growth of communication channels, employee numbers, and unguided processes around products and services, informal control becomes inefficient, causing communication problems and eventually limiting company growth. Research indicates that adaptation of a well-functioning MCS overcomes these problems. Purpose – The purpose of this study is to connect observations of Venture Capital effects on MCS to the existing SME and MCS literature. Method – Empirical data was collected by interviewing participants within four European SMEs that operate in different sectors. Semi-structured interviews were set up with the guidance of the theoretical framework. Thematic analysis was used to analyze the interview recordings. Findings – The study finds that case study companies commonly use Objectives and Key Results (OKRs), a practical implementation framework for Balance Scorecards (BSC). The short-sighted characteristics of BSC are in line with the nature of the SME environment, reactively responding to short-term threats and the financial-measure-focused VC interest. Agency Theory characteristics can label VC and SME relations. The research encourages addressing conflicting interests by redirecting focus to company growth. Since the adoption of well-functioning MCS assists company growth, this research suggests motivating and assisting MCS development at SMEs.

Cloud datacenters comprise hundreds or thousands of disparate application services, each having stringent performance and availability requirements, sharing a finite set of heterogeneous hardware and software resources. The implication of such complex environment is that the occurrence of performance problems, such as slow application response and unplanned downtimes, has become a norm rather than exception resulting in decreased revenue, damaged reputation, and huge human-effort in diagnosis. Though causes can be as varied as application issues (e.g. bugs), machine-level failures (e.g. faulty server), and operator errors (e.g. mis-configurations), recent studies have attributed capacity-related issues, such as resource shortage and contention, as the cause of most performance problems on the Internet today. As cloud datacenters become increasingly autonomous there is need for automated performance diagnosis systems that can adapt their operation to reflect the changing workload and topology in the infrastructure. In particular, such systems should be able to detect anomalous performance events, uncover manifestations of capacity bottlenecks, localize actual root-cause(s), and possibly suggest or actuate corrections. This thesis investigates approaches for diagnosing performance problems in cloud infrastructures. We present the outcome of an extensive survey of existing research contributions addressing performance diagnosis in diverse systems domains. We also present models and algorithms for detecting anomalies in real-time application performance and identification of anomalous datacenter resources based on operational metrics and spatial dependency across datacenter components. Empirical evaluations of our approaches shows how they can be used to improve end-user experience, service assurance and support root-cause analysis. / Cloud Control (C0590801)