The following master thesis is focused on creation of methodology for optimal usage of load balancing in data center environment. Thesis is divided into eight chapters. The first chapter describes the reasons why to deal with this topic further. The second chapter summarizes the state of load balancing. This chapter is based on research of already elaborated thesis which were focused on load balancing in different ways. The third chapter summarizes load balancing including its key principles. The fourth chapter describes an actual state of load balancing in data center environment. An observation of real usage of load balancing in selected data center was used for the main purpose of this chapter. The fifth chapter consists of analysis of the currently existing methodologies which are used from the infrastructure projects purpose. The sixth chapter deals with creation of methodology for optimal usage of load balancing in data center. The seventh chapter evaluates usage of methodology by applying of this methodology to real practical example of implementation of load balancing. The eighth chapter summarizes all detected conclusions.

L’analyse de flux de données est utilisée dans beaucoup de contexte où la masse des données et/ou le débit auquel elles sont générées, excluent d’autres approches (par exemple le traitement par lots). Le modèle flux fourni des solutions aléatoires et/ou fondées sur des approximations pour calculer des fonctions d’intérêt sur des flux (repartis) de n-uplets, en considérant le pire cas, et en essayant de minimiser l’utilisation des ressources. En particulier, nous nous intéressons à deux problèmes classiques : l’estimation de fréquence et les poids lourds. Un champ d’application moins courant est le traitement de flux qui est d’une certaine façon un champ complémentaire aux modèle flux. Celui-ci fournis des systèmes pour effectuer des calculs génériques sur les flux en temps réel souple, qui passent à l’échèle. Cette dualité nous permet d’appliquer des solutions du modèle flux pour optimiser des systèmes de traitement de flux. Dans cette thèse, nous proposons un nouvel algorithme pour la détection d’éléments surabondants dans des flux repartis, ainsi que deux extensions d’un algorithme classique pour l’estimation des fréquences des items. Nous nous intéressons également à deux problèmes : construire un partitionnement équitable de l’univers des n-uplets par rapport à leurs poids et l’estimation des valeurs de ces n-uplets. Nous utilisons ces algorithmes pour équilibrer et/ou délester la charge dans les systèmes de traitement de flux. / Nowadays stream analysis is used in many context where the amount of data and/or the rate at which it is generated rules out other approaches (e.g., batch processing). The data streaming model provides randomized and/or approximated solutions to compute specific functions over (distributed) stream(s) of data-items in worst case scenarios, while striving for small resources usage. In particular, we look into two classical and related data streaming problems: frequency estimation and (distributed) heavy hitters. A less common field of application is stream processing which is somehow complementary and more practical, providing efficient and highly scalable frameworks to perform soft real-time generic computation on streams, relying on cloud computing. This duality allows us to apply data streaming solutions to optimize stream processing systems. In this thesis, we provide a novel algorithm to track heavy hitters in distributed streams and two extensions of a well-known algorithm to estimate the frequencies of data items. We also tackle two related problems and their solution: provide even partitioning of the item universe based on their weights and provide an estimation of the values carried by the items of the stream. We then apply these results to both network monitoring and stream processing. In particular, we leverage these solutions to perform load shedding as well as to load balance parallelized operators in stream processing systems.

Este trabalho apresenta o projeto e a implementação da política de escalonamento com suporte à migração de processos JUMP. A migração de processos é uma ferramenta importante que complementa a alocação inicial realizada pela política de escalonamento em um ambiente paralelo distribuído, permitindo um balanceamento de carga dinâmico e mais refinado, resultando em um melhor desempenho do ambiente e menor tempo de resposta das aplicações paralelas distribuídas. A nova política unifica a alocação inicial e migração de processos em um único algoritmo, de forma a compartilhar decisões para o objetivo comum de prover um melhor desempenho para aplicações de uso intensivo de processamento em clusters heterogêneos. A política é implementada sobre o ambiente de escalonamento flexível e dinâmico AMIGO, adaptado para o suporte à migração de processos. A avaliação de desempenho mostrou que a nova política oferece ganhos expressivos nos tempos de resposta quando comparada às outras duas políticas de escalonamento implementadas no AMIGO, em quase todos os cenários, para diversas aplicações e diversas situações de carga do ambiente / This work presents the project and implementation of the scheduling policy with process migration support JUMP. Process migration is an important tool that complements the initial placement performed by the scheduling policy in a distributed parallel environment, allowing for dynamic and more refined load balancing, resulting in better performance of the environment and shorter response time for distributed parallel applications. The new policy unifies initial placement and process migration in a single algorithm, enabling the sharing of decisions for the common goal of providing a better performance for CPU-bound applications in heterogeneous clusters. The policy is implemented over the dynamical and flexible environment AMIGO, adapted in order to support process migration. Performance evaluation showed that the new policy offers expressive gains in response times when compared to other two scheduling policies implemented in AMIGO in almost all scenarios, for different applications and different environment load situations

Algoritmos de balanceamento de carga são utilizados em sistemas distribuídos para homogeneizar a ocupação dos recursos computacionais disponíveis. A homogeneidade na ocupação do ambiente permite otimizar a alocação de recursos e, conseqüentemente, aumentar o desempenho das aplicações. Com o advento dos sistemas distribuídos de alta escala, fazem-se necessárias pesquisas para a construção de algoritmos de balanceamento de carga que sejam capazes de gerir com eficiência esses sistemas. Essa eficiência é medida através do número de mensagens geradas no ambiente, do suporte a ambientes heterogêneos, do uso de políticas que consomem poucos recursos do sistema, da estabilidade em alta carga, da escalabilidade do sistema e dos baixos tempos de resposta. Com o objetivo de atender as necessidades dos sistemas distribuídos de alta escala, este doutorado propõe, apresenta e avalia um novo algoritmo de balanceamento de carga denominado TLBA (Tree Load Balancing Algorithm). Esse algoritmo organiza os computadores do sistema em uma topologia lógica na forma de árvore, sobre a qual são executadas operações de balanceamento de carga. Para validar o TLBA foi construído um simulador que, submetido a testes, permitiu comprovar suas contribuições, que incluem: o baixo número de mensagens geradas pelas operações de balanceamento de carga; a estabilidade em altas cargas; os baixos tempos médios de resposta de processos. Para validar os resultados de simulação, foi construído um protótipo do TLBA. Esse protótipo confirmou os resultados de simulação e, conseqüentemente, as contribuições do algoritmo. / Load balancing algorithms are applied in distributed systems to homogenize the occupation of the available computational resources. The homogeneity of the environment occupation allows optimising the resource allocation and consequently, increasing the application performance. With the advent of the large-scale distributed systems, it was necessary to start researching the construction of load balancing algorithms which are able to manage these systems with efficiency. This efficiency is measured through the number of messages generated on the environment; the support to heterogeneous environments and the load balance policies which should spend the minimal resources time; the stability in overloaded situations; the system scalability; and the processes average response times, that should be small. With the aim to achieve the large-scale distributed systems requirements, this Ph.D. proposes, presents and evaluates a new load balancing algorithm named TLBA (Tree Load Balancing Algorithm). This algorithm arranges the computers on a logical network topology with a tree format. The load balancing operations are executed over this tree. To evaluate the TLBA algorithm, a simulator was built that was submitted to tests that confirmed the following characteristics: the small number of messages generated by the load balancing operations; the stability in overloaded situations; the small average processes response times. To validate the simulation results a TLBA prototype was implemented. This prototype confirmed the simulation results and consequently the contributions of the proposed algorithm.

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This thesis deals with the background of the design of a highly available, scalable and modular content management system based on the Java EE platform and the OSGi framework and with the implementation of the designed system. It describes the design and implementation of the application decomposition to modules, their communication and bindings. Finally, it presents the results of the application testing and proposes available extensions of the application.

Shared-disk database systems offer a high degree of freedom in the allocation of workload compared to shared-nothing architectures. This creates a great potential for load balancing but also introduces additional complexity into the process of query scheduling. This report surveys the problems and opportunities faced in scan processing in a shared-disk environment. We list the parameters to tune and the decisions to make, as well as some known solutions and commonsense considerations, in order to identify the most promising areas of future research.

Research in the area of load balancing in distributed systems has not yet come with an optimal load balancing technique. Existing approaches work primarily with replication and sharding. This thesis overviews existing knowledge in this area with focus on shard- ing, and provides an experiment comparing a state-of-the-art load balancing technique called Weighed-Move with a random baseline and an existing domain-specific balancing implementation. As a significant part of the project, we engineered a generic and scal- able load balancer that may be used in any distributed system and deployed it into an existing ad system called Sklik. The major challenges appeared to be tackling various problems related to data consistency, performance and synchronization, together with solving compatibility issues with the rest of the still-evolving ad system. Our experiment shows that the domain-specific load balancing implementation produces data distribution that enables better performance, but Weighed-Move proved to have a great potential and its results are expected to be enhanced by further work on our implementation. 1

Systems for automated evaluation of assignments are a valuable aid for both teachers of programming courses and their students. The objective of this thesis is to examine the possibilities of deploying such systems in a large-scale distributed environment and the challenges of such endeavors. A sizable part of the requirements comes from experience with ReCodEx - an assignment evaluation system developed at the department of the supervisor. Modern server multi-core processors provide considerable computing power that can be used for assignment evaluation. However, parallel measurements can interfere with each other. This causes unstable results, which detriments the fairness of grading. Isolation (sandboxing) technologies can cause similar effects. We measure both of these influences and use the results to determine to what degree can multi-core processors be exploited. The problem of efficient distribution of work between multiple evaluation workers is complementary to that of utilizing multi-core machines. We survey scheduling algorithms and design an experiment to compare their performance. Additionally, we examine the possibility of leveraging container technologies to simplify the deployment of software required for evaluation. This leads to both a smaller administration overhead and a less complex...

<div>The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of spatial data in real-time with high-throughput and low response time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. The performance of distributed streaming systems relies on how even the workload is distributed among their machines. However, the real-time streamed spatial data and query follow non-uniform spatial distributions that are continuously changing over time. Therefore, Distributed spatial streaming systems need to track the changes in the distribution of spatial data and queries and redistribute their workload accordingly. This thesis addresses the challenges of adapting to workload changes in distributed spatial streaming systems to improve the performance while preserving the system's security. </div><div>The thesis proposes TrioStat, an online workload estimation technique that relies on a probabilistic model for estimating the cost of partitions and machines of distributed spatial streaming systems. TrioStat has a decentralised technique to collect and maintain the required statistics in real-time with minimal overhead. In addition, this thesis introduces SWARM, a light-weight adaptive load-balancing protocol that continuously monitors the data and query workloads across the distributed processes of spatial data streaming systems, and redistribute the workloads soon as performance bottlenecks get detected. SWARM uses TrioStat to estimate the workload of the system's machines. Although using adaptive load-balancing techniques significantly improves the performance of distributed streaming systems, they make the system vulnerable to attacks. In this thesis, we introduce a novel attack model that targets adaptive load-balancing mechanisms of distributed streaming systems. The attack reduces the throughput and the availability of the system by making it stay in a continuous state of rebalancing. The thesis proposes Guard, a component that detects and blocks attacks that target the adaptive load balancing of distributed streaming systems. Guard is deployed in SWARM to develop an attack-resilient adaptive load balancing mechanism for Distributed spatial streaming systems.<br></div>