Data Structures & Algorithm Analysis in C++

This is the textbook for CSIS 215 at Liberty University.https://digitalcommons.liberty.edu/textbooks/1005/thumbnail.jp

Understanding the Impact of Databases on the Energy Efficiency of Cloud Applications

RÉSUMÉ Aujourd'hui, les applications infonuagiques sont utilisées dans toutes les industries ; de la finance, au commerce de détail, en passant par l'éducation, la communication, la manufacture, les services publics et les transports. Malgré leur popularité et leur large adoption, peu d'informations sont disponibles sur l'empreinte énergétique de ces applications et, en particulier, celle de leurs bases de données, qui constituent l'épine dorsale de ces applications infonuagiques. Pourtant, la réduction de la consommation d'énergie des applications est un objectif majeur pour la société et continuera de l'être à l'avenir. Deux familles de bases de données sont actuellement utilisées dans les applications infonuagiques: Les bases de données relationnelles et non-relationnelles. Aussi, nous examinons la consommation d'énergie des trois bases de données utilisées par les applications infonuagiques : MySQL, PostgreSQL et MongoDB, respectivement relationelle, relationelle, et non-relationelle. Nous réalisons une série d'expériences avec trois applications infonuagiques (une application multi-thread RESTful, DVD Store, et JPetStore). Nous étudions également l'impact des patrons infonuagiques sur la consommation d'énergie parce que les bases de données dans les applications infonuagiques sont souvent implémentées conjointement avec des patrons infonuagiques tels que le Local Database Proxy, le Local Sharding Based Router, ou la Priority Message Queue. Nous mesurons la consommation d'énergie en utilisant l'outil Power-API pour garder une trace de l'énergie consommée au niveau de processus par les variantes des applications infonuagiques. Cette estimation énergétique au niveau processus donne une précision plus exacte que d'une estimation au niveau d'un logiciel en général. En plus de cela, nous mesurons le temps de réponse de l'application infonuagique pour mettre en contraste le temps de réponse avec l'efficacité énergétique, afin que les développeurs soient conscients des compromis entre ces deux indicateurs de qualité lors de la sélection d'une base de données pour leur application. Nous rapportons que le choix des bases de données peut réduire la consommation d'énergie d'une application infonuagique quelque soit les trois types des patrons infonuagiques étudiés. Nous avons montré que la base de données MySQL est la moins consommatrice d'énergie, mais est la plus lente parmi les trois bases de données étudiées. PostgreSQL est la plus consommatrice d'énergie entre les trois bases de données, mais est plus rapide que MySQL, mais plus lente que MongoDB. MongoDB consomme plus d'énergie que MySQL, mais moins que PostgreSQL et est la plus rapide parmi les trois bases de données étudiées.----------ABSTRACT Cloud-based applications are used in about every industry; from financial, retail, education, and communication, to manufacturing, utilities, and transportation. Despite their popularity and wide adoption, little is still known about the energy footprint of these applications and, in particular, of their databases, which are the backbone of cloud-based applications. Reducing the energy consumption of applications is a major objective for society and will continue to be so in the near to far future. Two families of databases are currently used in cloud-based applications: relational and non-relational databases. Consequently, in this thesis, we study the energy consumption of three databases used by cloud-based applications: MySQL, PostgreSQL, and MongoDB, which are respectively relational, relational, and non-relational. We devise a series of experiments with three cloud-based applications (a RESTful multi-threaded application, DVD Store, and JPetStore). We also study the impact of cloud patterns on the energy consumption because databases in cloud-based applications are often implemented in conjunction with patterns like Local Database Proxy, Local Sharding-Based Router, and Priority Message Queue. We measure the energy consumption using the Power-API tool to keep track of the energy consumed at the process-level by the variants of the cloud-based applications. We measure the response time of the cloud-based application because we wanted to contrast response time with energy efficiency, so that developers are aware of the trade-offs between these two quality indicators when selecting a database for their application. We report that the choice of the databases can reduce the energy consumption of a cloud-based application regardless of the three cloud patterns that are implemented. We showed that MySQL database is the least energy consuming but is the slowest among the three databases. PostgreSQL is the most energy consuming among the three databases, but is faster than MySQL but slower than MongoDB. MongoDB consumes more energy than MySQL but less than PostgreSQL and is the fastest among the three databases

Search-Based Temporal Testing of Multicore Applications

Multicore systems are increasingly common as a modern computing platform. Multicore processors not only offer better performance-to-cost ratios relative to single-core processors but also have significantly minimised space, weight, and power (SWaP) constraints. Unfortunately, they introduce challenges in verification as their shared components are potential channels for interference. The potential for interference increases the possibility of concurrency faults at runtime and consequently increases the difficulty of verifying. In this thesis, search-based techniques are empirically investigated to determine their effectiveness in temporal testing—searching for test inputs that may lead a task running on an embedded multicore to produce extreme (here longest) execution times, which might cause the system to violate its temporal requirements. Overall, the findings suggest that various forms of search-based approaches are effective in generating test inputs exhibiting extreme execution times on the embedded multicore environment. All previous work in temporal testing has evolved test data directly; this is not essential. In this thesis, one novel proposed approach, i.e. the use of search to discover high performing biased random sampling regimes (which we call 'dependent input sampling strategies'), has proved particularly effective. Shifting the target of search from test data itself to strategies proves particularly well motivated for attaining extreme execution times. Finally, we present also preliminary results on the use of so-called 'hyper-heuristics', which can be used to form optimal hybrids of optimisation techniques. An extensive comparison of direct approaches to establishing a baseline is followed by reports of research into indirect approaches and hyper-heuristics. The shift to strategies from direct data can be thought of as a leap in abstraction level for the underlying temporal test data generation problem. The shift to hyper-heuristics aims to boost the level of optimisation technique abstraction. The former is more fully worked out than the latter and has proved a significant success. For the latter only preliminary results are available; as will be seen from this work as the whole computational requirements for research experimentation are significant