Quaestor: Query web caching for database-as-a-service providers

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching. </jats:p

Scalable big data systems: Architectures and optimizations

Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D

Cloud transactions and caching for improved performance in clouds and DTNs

In distributed transactional systems deployed over some massively decentralized cloud servers, access policies are typically replicated. Interdependencies ad inconsistencies among policies need to be addressed as they can affect performance, throughput and accuracy. Several stringent levels of policy consistency constraints and enforcement approaches to guarantee the trustworthiness of transactions on cloud servers are proposed. We define a look-up table to store policy versions and the concept of Tree-Based Consistency approach to maintain a tree structure of the servers. By integrating look-up table and the consistency tree based approach, we propose an enhanced version of Two-phase validation commit (2PVC) protocol integrated with the Paxos commit protocol with reduced or almost the same performance overhead without affecting accuracy and precision. A new caching scheme has been proposed which takes into consideration Military/Defense applications of Delay-tolerant Networks (DTNs) where data that need to be cached follows a whole different priority levels. In these applications, data popularity can be defined not only based on request frequency, but also based on the importance like who created and ranked point of interests in the data, when and where it was created; higher rank data belonging to some specific location may be more important though frequency of those may not be higher than more popular lower priority data. Thus, our caching scheme is designed by taking different requirements into consideration for DTN networks for defense applications. The performance evaluation shows that our caching scheme reduces the overall access latency, cache miss and usage of cache memory when compared to using caching schemes --Abstract, page iv

Issues on distributed caching of spatial data

Die Menge an digitalen Informationen über Orte hat bis heute rapide zugenommen. Mit der Verbreitung mobiler, internetfähiger Geräte kann nun jederzeit und von überall auf diese Informationen zugegriffen werden. Im Zuge dieser Entwicklung wurden zahlreiche ortsbasierte Anwendungen und Dienste populär. So reihen sich digitale Einkaufsassistenten und Touristeninformationsdienste sowie geosoziale Anwendungen in der Liste der beliebtesten Vertreter. Steigende Benutzerzahlen sowie die rapide wachsenden Datenmengen, stellen ernstzunehmende Herausforderungen für die Anbieter ortsbezogener Informationen dar. So muss der Datenbereitstellungsprozess effizient gestaltet sein, um einen kosteneffizienten Betrieb zu ermöglichen. Darüber hinaus sollten Ressourcen flexibel genug zugeordnet werden können, um Lastungleichgewichte zwischen Systemkomponenten ausgleichen zu können. Außerdem müssen Datenanbieter in der Lage sein, die Verarbeitungskapazitäten mit steigender und fallender Anfragelast zu skalieren. Mit dieser Arbeit stellen wir einen verteilten Zwischenspeicher für ortsbasierte Daten vor. In dem verteilten Zwischenspeicher werden Replika der am häufigsten verwendeten Daten von mehreren unabhängigen Servern im flüchtigen Speicher vorgehalten. Mit unserem Ansatz können die Herausforderungen für Anbieter ortsbezogener Informationen wie folgt addressiert werden: Zunächst sorgt eine speziell für die Zugriffsmuster ortsbezogener Anwendungen konzipierte Zwischenspreicherungsstragie für eine Erhöhung der Gesamteffizienz, da eine erhebliche Menge der zwischengespeicherten Ergebnisse vorheriger Anfragen wiederverwendet werden kann. Darüber hinaus bewirken unsere speziell für den Geo-Kontext entwickelten Lastbalancierungsverfahren den Ausgleich dynamischer Lastungleichgewichte. Letztlich befähigen unsere verteilten Protokolle zur Hinzu- und Wegnahme von Servern die Anbieter ortsbezogener Informationen, die Verarbeitungskapazität steigender oder fallender Anfragelast anzupassen. In diesem Dokument untersuchen wir zunächst die Anforderungen der Datenbereitstellung im Kontext von ortsbasierten Anwendungen. Anschließend diskutieren wir mögliche Entwurfsmuster und leiten eine Architektur für einen verteilten Zwischenspeicher ab. Im Verlauf dieser Arbeit, entstanden mehrere konkrete Implementierungsvarianten, die wir in diesem Dokument vorstellen und miteinander vergleichen. Unsere Evaluation zeigt nicht nur die prinzipielle Machbarkeit, sondern auch die Effektivität von unserem Caching-Ansatz für die Erreichung von Skalierbarkeit und Verfügbarkeit im Kontext der Bereitstellung von ortsbasierten Daten

Data Storage and Dissemination in Pervasive Edge Computing Environments

Nowadays, smart mobile devices generate huge amounts of data in all sorts of gatherings. Much of that data has localized and ephemeral interest, but can be of great use if shared among co-located devices. However, mobile devices often experience poor connectivity, leading to availability issues if application storage and logic are fully delegated to a remote cloud infrastructure. In turn, the edge computing paradigm pushes computations and storage beyond the data center, closer to end-user devices where data is generated and consumed. Hence, enabling the execution of certain components of edge-enabled systems directly and cooperatively on edge devices. This thesis focuses on the design and evaluation of resilient and efficient data storage and dissemination solutions for pervasive edge computing environments, operating with or without access to the network infrastructure. In line with this dichotomy, our goal can be divided into two specific scenarios. The first one is related to the absence of network infrastructure and the provision of a transient data storage and dissemination system for networks of co-located mobile devices. The second one relates with the existence of network infrastructure access and the corresponding edge computing capabilities. First, the thesis presents time-aware reactive storage (TARS), a reactive data storage and dissemination model with intrinsic time-awareness, that exploits synergies between the storage substrate and the publish/subscribe paradigm, and allows queries within a specific time scope. Next, it describes in more detail: i) Thyme, a data storage and dis- semination system for wireless edge environments, implementing TARS; ii) Parsley, a flexible and resilient group-based distributed hash table with preemptive peer relocation and a dynamic data sharding mechanism; and iii) Thyme GardenBed, a framework for data storage and dissemination across multi-region edge networks, that makes use of both device-to-device and edge interactions. The developed solutions present low overheads, while providing adequate response times for interactive usage and low energy consumption, proving to be practical in a variety of situations. They also display good load balancing and fault tolerance properties.Resumo Hoje em dia, os dispositivos móveis inteligentes geram grandes quantidades de dados em todos os tipos de aglomerações de pessoas. Muitos desses dados têm interesse loca- lizado e efêmero, mas podem ser de grande utilidade se partilhados entre dispositivos co-localizados. No entanto, os dispositivos móveis muitas vezes experienciam fraca co- nectividade, levando a problemas de disponibilidade se o armazenamento e a lógica das aplicações forem totalmente delegados numa infraestrutura remota na nuvem. Por sua vez, o paradigma de computação na periferia da rede leva as computações e o armazena- mento para além dos centros de dados, para mais perto dos dispositivos dos utilizadores finais onde os dados são gerados e consumidos. Assim, permitindo a execução de certos componentes de sistemas direta e cooperativamente em dispositivos na periferia da rede. Esta tese foca-se no desenho e avaliação de soluções resilientes e eficientes para arma- zenamento e disseminação de dados em ambientes pervasivos de computação na periferia da rede, operando com ou sem acesso à infraestrutura de rede. Em linha com esta dico- tomia, o nosso objetivo pode ser dividido em dois cenários específicos. O primeiro está relacionado com a ausência de infraestrutura de rede e o fornecimento de um sistema efêmero de armazenamento e disseminação de dados para redes de dispositivos móveis co-localizados. O segundo diz respeito à existência de acesso à infraestrutura de rede e aos recursos de computação na periferia da rede correspondentes. Primeiramente, a tese apresenta armazenamento reativo ciente do tempo (ARCT), um modelo reativo de armazenamento e disseminação de dados com percepção intrínseca do tempo, que explora sinergias entre o substrato de armazenamento e o paradigma pu- blicação/subscrição, e permite consultas num escopo de tempo específico. De seguida, descreve em mais detalhe: i) Thyme, um sistema de armazenamento e disseminação de dados para ambientes sem fios na periferia da rede, que implementa ARCT; ii) Pars- ley, uma tabela de dispersão distribuída flexível e resiliente baseada em grupos, com realocação preventiva de nós e um mecanismo de particionamento dinâmico de dados; e iii) Thyme GardenBed, um sistema para armazenamento e disseminação de dados em redes multi-regionais na periferia da rede, que faz uso de interações entre dispositivos e com a periferia da rede. As soluções desenvolvidas apresentam baixos custos, proporcionando tempos de res- posta adequados para uso interativo e baixo consumo de energia, demonstrando serem práticas nas mais diversas situações. Estas soluções também exibem boas propriedades de balanceamento de carga e tolerância a faltas

Query-Time Data Integration

Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to traditional up-front integration. It aims at enabling users to issue ad-hoc queries on their own data as if all potential other data sources were already integrated, without declaring specific sources and mappings to use. Automated data search and integration methods are then coupled directly with query processing on the available data. The ambiguity and uncertainty introduced through fully automated retrieval and mapping methods is compensated by answering those queries with ranked lists of alternative results. Each result is then based on different data sources or query interpretations, allowing users to pick the result most suitable to their information need. To this end, this thesis makes three main contributions. Firstly, we introduce a novel method for Top-k Entity Augmentation, which is able to construct a top-k list of consistent integration results from a large corpus of heterogeneous data sources. It improves on the state-of-the-art by producing a set of individually consistent, but mutually diverse, set of alternative solutions, while minimizing the number of data sources used. Secondly, based on this novel augmentation method, we introduce the DrillBeyond system, which is able to process Open World SQL queries, i.e., queries referencing arbitrary attributes not defined in the queried database. The original database is then augmented at query time with Web data sources providing those attributes. Its hybrid augmentation/relational query processing enables the use of ad-hoc data search and integration in data analysis queries, and improves both performance and quality when compared to using separate systems for the two tasks. Finally, we studied the management of large-scale dataset corpora such as data lakes or Open Data platforms, which are used as data sources for our augmentation methods. We introduce Publish-time Data Integration as a new technique for data curation systems managing such corpora, which aims at improving the individual reusability of datasets without requiring up-front global integration. This is achieved by automatically generating metadata and format recommendations, allowing publishers to enhance their datasets with minimal effort. Collectively, these three contributions are the foundation of a Query-time Data Integration architecture, that enables ad-hoc data search and integration queries over large heterogeneous dataset collections

OPTIMIZING CLIENT-SERVER COMMUNICATION FOR REMOTE SPATIAL DATABASE ACCESS

Technological advances in recent years have opened ways for easier creation of spatial data. Every day, vast amounts of data are collected by both governmental institutions (e.g., USGS, NASA) and commercial entities (e.g., IKONOS). This process is driven by increased popularity and affordability across the whole spectrum of collection methods, ranging from personal GPS units to satellite systems. Many collection methods such as satellite systems produce data in raster format. Often, such raster data is analyzed by the researchers directly, while at other times such data is used to produce the final dataset in vector format. With the rapidly increasing supply of data, more applications for this data are being developed that are of interest to a wider consumer base. The increasing popularity of spatial data viewers and query tools with end users introduces a requirement for methods to allow these basic users to access this data for viewing and querying instantly and without much effort. In our work, we focus on providing remote access to vector-based spatial data, rather than raster data. We explore new ways of allowing visualization of both spatial and non-spatial data stored in a central server database on a simple client connected to this server by possibly a slow and unreliable connection. We considered usage scenarios where transferring the whole database for processing on the client was not feasible. This is due to the large volume of data stored on the server as well as a lack of computing power on the client and a slow link between the two. We focus on finding an optimal way of distributing work between the server, clients, and possibly other entities introduced into the model for query evaluation and data management. We address issues of scalability for clients that have only limited access to system resources (e.g., a Java applet). Methods to allow these clients to provide an interactive user interface, even for databases of arbitrary size, are also examined

Service Quality Assessment for Cloud-based Distributed Data Services

The issue of less-than-100% reliability and trust-worthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. An example of S is a replicated data service to handle customer queries with fault-tolerance and performance goals. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or excessive statistical-sharing of system resources (e.g., VM cycles, number of servers). Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. We use computational models of S to determine the optimal feasible resource schedules and verify how close is the actual system behavior to a model-computed \u27gold-standard\u27. Our QoS assessment methods allow comparing different service vendors (possibly with different business policies) in terms of canonical properties: such as elasticity, linearity, isolation, and fairness (analogical to a comparative rating of restaurants). Case studies of cloud-based distributed applications are described to illustrate our QoS assessment methods. Specific systems studied in the thesis are: i) replicated data services where the servers may be hosted on multiple data-centers for fault-tolerance and performance reasons; and ii) content delivery networks to geographically distributed clients where the content data caches may reside on different data-centers. The methods studied in the thesis are useful in various contexts of QoS management and self-configurations in large-scale cloud-based distributed systems that are inherently complex due to size, diversity, and environment dynamicity