Performance management of event processing systems

This thesis is a study of performance management of Complex Event Processing (CEP) systems. Since CEP systems have distinct characteristics from other well-studied computer systems such as batch and online transaction processing systems and database-centric applications, these characteristics introduce new challenges and opportunities to the performance management for CEP systems. Methodologies used in benchmarking CEP systems in many performance studies focus on scaling the load injection, but not considering the impact of the functional capabilities of CEP systems. This thesis proposes the approach of evaluating the performance of CEP engines’ functional behaviours on events and develops a benchmark platform for CEP systems: CEPBen. The CEPBen benchmark platform is developed to explore the fundamental functional performance of event processing systems: filtering, transformation and event pattern detection. It is also designed to provide a flexible environment for exploring new metrics and influential factors for CEP systems and evaluating the performance of CEP systems. Studies on factors and new metrics are carried out using the CEPBen benchmark platform on Esper. Different measurement points of response time in performance management of CEP systems are discussed and response time of targeted event is proposed to be used as a metric for quality of service evaluation combining with the traditional response time in CEP systems. Maximum query load as a capacity indicator regarding to the complexity of queries and number of live objects in memory as a performance indicator regarding to the memory management are proposed in performance management of CEP systems. Query depth is studied as a performance factor that influences CEP system performance

Análise da aplicabilidade das regras de ouro ao tuning de sistemas gerenciadores de bancos de dados relacionais em ambientes de computação em nuvem

Orientador : Prof. Dr. Marcos Sfair SunyeTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 07/03/2014Inclui referênciasResumo: A computação em nuvem oferece um ambiente bastante propício para o provimento de serviços de TI. A virtualização, tecnologia que compõe sua base possibilita simular sobre um computador físico, uma ou mais estações de trabalho chamadas máquinas virtuais, que permitem maior exibilidade e melhor racionalização de sua infraestrutura. A incorporação de sistemas legados aos ambientes em nuvem como forma de contenção de custo é uma demanda frequente e altamente relevante. Para isso, é comum o emprego do modelo multi-inquilino do tipo shared-hardware, no qual o sistema gerenciador de banco de dados e o sistema legado ficam hospedados em máquinas virtuais que competem, junto às demais, por recursos computacionais. Neste ambiente, é vital o emprego de estratégias de tuning que objetivam melhorias no desempenho do banco de dados. Porém, os sistemas gerenciadores de banco de dados relacionais não foram inicialmente projetados para serem executados em ambientes shared-hardware. Consequentemente, seus parâmetros de configuração, comumente alvos de regras de tuning, não consideram o fato de que os recursos disponíveis variam ao longo do tempo, devido ao provisionamento dinâmico comum em ambientes elásticos. Esta tese propõe um método de avaliação que, por meio da simulação de cargas de trabalho de acesso a disco oriundas de máquinas virtuais concorrentes, demonstra a inadequação do emprego das regras de tuning, conhecidas como regras-de-ouro, encontradas na literatura e/ou recomendadas por experts. Nossos resultados apontam para a definição de novas regras-de-ouro, específicas para ambientes virtualizados, além de viabilizar a criação de um modelo para o tuning automático de sistemas gerenciadores de banco de dados relacionais em ambientes de computação em nuvem. Palavras-Chave: sistema gerenciador de banco de dados relacional, virtualização, tuning, sistema legados, computação em nuvemAbstract: Cloud computing currently o_ers a very propitious environment for IT service provision. The virtualization, technology that compose their base enables to simulate in a physical computer one or more workstations called virtual machines that allow greater exibility and better use of its infrastructure. The incorporation of legacy systems to the cloud environments as a means of cost containment is a frequent and highly relevant demand. Therefore, it is common the use the multi-tenant model of shared-hardware type on which the database and legacy system are hosted on virtual machines that compete, with others, for computational resources. In this environment it is vital the use of tuning strategies that aim to improve the performance of the database. However, the relational database management systems were not initially designed to execute on shared-hardware environments. Consequently, its con_guration parameters, commonly targets of tuning rules, do not consider the fact that the available resources vary over time due to the common dynamic provisioning that is common in elastic environments. This thesis proposes an evaluation methodology that, simulates I/O workloads from concurrent virtual machines and demonstrates the inadequacy of the use of tuning rules, known as rules-ofthumb, found in literature and/or recommended by experts. Our results point to the new rules-of-thumb, speci_c to virtualized environments while also make feasible the creation of a model for automatic tuning of database in cloud computing environments. Keywords: relational database management system, virtualization, tuning, legacy systems, cloud computing

Autonomous Database Management at Scale: Automated Tuning, Performance Diagnosis, and Resource Decentralization

Database administration has always been a challenging task, and is becoming even more difficult with the rise of public and private clouds. Today, many enterprises outsource their database operation to cloud service providers (CSPs) in order to reduce operating costs. CSPs, now tasked with managing an extremely large number of database instances, cannot simply rely on database administrators. In fact, humans have become a bottleneck in the scalability and profitability of cloud offerings. This has created a massive demand for building autonomous databases—systems that operate with little or zero human supervision. While autonomous databases have gained much attention in recent years in both academia and industry, many of the existing techniques remain limited to automating parameter tuning, backup/recovery, and monitoring. Consequently, there is much to be done before realizing a fully autonomous database. This dissertation examines and offers new automation techniques for three specific areas of modern database management. 1. Automated Tuning – We propose a new generation of physical database designers that are robust against uncertainty in future workloads. Given the rising popularity of approximate databases, we also develop an optimal, hybrid sampling strategy that enables efficient join processing on offline samples, a long-standing open problem in approximate query processing. 2. Performance Diagnosis – We design practical tools and algorithms for assisting database administrators in quickly and reliably diagnosing performance problems in their transactional databases. 3. Resource Decentralization – To achieve autonomy among database components in a shared environment, we propose a highly efficient, starvation-free, and fully decentralized distributed lock manager for distributed database clusters.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153349/1/dyoon_1.pd

Transactions Chasing Scalability and Instruction Locality on Multicores

For several decades, online transaction processing (OLTP) has been one of the main server applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Recent hardware trends oblige software to overcome two major challenges against systems scalability on modern multicore processors: (1) exploiting the abundant thread-level parallelism across cores and (2) taking advantage of the implicit parallelism within a core. The traditional design of the OLTP systems, however, faces inherent scalability problems due to its tightly coupled components. In addition, OLTP cannot exploit the full capability of the micro-architectural resources of modern processors because of the conventional scheduling decisions that ignore the cache locality for transactions. As a result, today’s commonly used server hardware remains largely underutilized leading to a huge waste of hardware resources and energy. .... In this thesis, we first identify the unbounded critical sections of traditional OLTP systems as the main enemy of thread-level parallelism. We design an alternative shared-everything system based on physiological partitioning (PLP) to eliminate the unbounded critical sections while providing an infrastructure for low-cost dynamic repartitioning and without introducing high-cost distributed transactions. Then, we demonstrate that L1 instruction cache stalls are the dominant factor leading to underutilization in the commodity servers. However, we also observe that independently of their high-level functionality, transactions running in parallel on a multicore system share significant amount of common instructions. By adaptively spreading the execution of a transaction over multiple cores through thread migration or multiplexing transactions on one core, we enable both an ample L1 instruction cache capacity for a transaction and reuse of common instructions across concurrent transactions. .... As the hardware demands more from the software to exploit the complexity and parallelism it offers in the multicore era, this work would change the way we traditionally schedule transactions. Instead of viewing a transaction as a single big task, we split it into smaller parts that can exploit data and instruction locality through careful dynamic scheduling decisions. The methods this thesis presents are not only specific to OLTP systems, but they can also benefit other types of applications that have concurrent requests executing a series of actions from a predefined set and face similar scalability problems on emerging hardware