On debugging in a parallel system

In this paper a description is given of a partly implemented parallel debugger for the Twente University Multicomputer (TUMULT). The system's basic method for exchange of data is message passing. Experience has learned that most programming errors in application software are made in calls to the kernel and the interprocess communication. The debugger is intended to be used for locating bugs at this level in the application software. It is assumed that basic blocks of the debuggee can be debugged using a traditional sequential sourcelevel debugger

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management

As users of big data applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the pay-as-you-go model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs - systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the check-pointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures. Copyright © 2013 ACM

LogBase: A Scalable Log-structured Database System in the Cloud

Numerous applications such as financial transactions (e.g., stock trading) are write-heavy in nature. The shift from reads to writes in web applications has also been accelerating in recent years. Write-ahead-logging is a common approach for providing recovery capability while improving performance in most storage systems. However, the separation of log and application data incurs write overheads observed in write-heavy environments and hence adversely affects the write throughput and recovery time in the system. In this paper, we introduce LogBase - a scalable log-structured database system that adopts log-only storage for removing the write bottleneck and supporting fast system recovery. LogBase is designed to be dynamically deployed on commodity clusters to take advantage of elastic scaling property of cloud environments. LogBase provides in-memory multiversion indexes for supporting efficient access to data maintained in the log. LogBase also supports transactions that bundle read and write operations spanning across multiple records. We implemented the proposed system and compared it with HBase and a disk-based log-structured record-oriented system modeled after RAMCloud. The experimental results show that LogBase is able to provide sustained write throughput, efficient data access out of the cache, and effective system recovery.Comment: VLDB201

Database of audio records

Diplomka a prakticky castDiplome with partical part

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

AT-GIS: highly parallel spatial query processing with associative transducers

Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3× the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10× for aggregation queries

Modern data analytics in the cloud era

Cloud Computing ist die dominante Technologie des letzten Jahrzehnts. Die Benutzerfreundlichkeit der verwalteten Umgebung in Kombination mit einer nahezu unbegrenzten Menge an Ressourcen und einem nutzungsabhängigen Preismodell ermöglicht eine schnelle und kosteneffiziente Projektrealisierung für ein breites Nutzerspektrum. Cloud Computing verändert auch die Art und Weise wie Software entwickelt, bereitgestellt und genutzt wird. Diese Arbeit konzentriert sich auf Datenbanksysteme, die in der Cloud-Umgebung eingesetzt werden. Wir identifizieren drei Hauptinteraktionspunkte der Datenbank-Engine mit der Umgebung, die veränderte Anforderungen im Vergleich zu traditionellen On-Premise-Data-Warehouse-Lösungen aufweisen. Der erste Interaktionspunkt ist die Interaktion mit elastischen Ressourcen. Systeme in der Cloud sollten Elastizität unterstützen, um den Lastanforderungen zu entsprechen und dabei kosteneffizient zu sein. Wir stellen einen elastischen Skalierungsmechanismus für verteilte Datenbank-Engines vor, kombiniert mit einem Partitionsmanager, der einen Lastausgleich bietet und gleichzeitig die Neuzuweisung von Partitionen im Falle einer elastischen Skalierung minimiert. Darüber hinaus führen wir eine Strategie zum initialen Befüllen von Puffern ein, die es ermöglicht, skalierte Ressourcen unmittelbar nach der Skalierung auszunutzen. Cloudbasierte Systeme sind von fast überall aus zugänglich und verfügbar. Daten werden häufig von zahlreichen Endpunkten aus eingespeist, was sich von ETL-Pipelines in einer herkömmlichen Data-Warehouse-Lösung unterscheidet. Viele Benutzer verzichten auf die Definition von strikten Schemaanforderungen, um Transaktionsabbrüche aufgrund von Konflikten zu vermeiden oder um den Ladeprozess von Daten zu beschleunigen. Wir führen das Konzept der PatchIndexe ein, die die Definition von unscharfen Constraints ermöglichen. PatchIndexe verwalten Ausnahmen zu diesen Constraints, machen sie für die Optimierung und Ausführung von Anfragen nutzbar und bieten effiziente Unterstützung bei Datenaktualisierungen. Das Konzept kann auf beliebige Constraints angewendet werden und wir geben Beispiele für unscharfe Eindeutigkeits- und Sortierconstraints. Darüber hinaus zeigen wir, wie PatchIndexe genutzt werden können, um fortgeschrittene Constraints wie eine unscharfe Multi-Key-Partitionierung zu definieren, die eine robuste Anfrageperformance bei Workloads mit unterschiedlichen Partitionsanforderungen bietet. Der dritte Interaktionspunkt ist die Nutzerinteraktion. Datengetriebene Anwendungen haben sich in den letzten Jahren verändert. Neben den traditionellen SQL-Anfragen für Business Intelligence sind heute auch datenwissenschaftliche Anwendungen von großer Bedeutung. In diesen Fällen fungiert das Datenbanksystem oft nur als Datenlieferant, während der Rechenaufwand in dedizierten Data-Science- oder Machine-Learning-Umgebungen stattfindet. Wir verfolgen das Ziel, fortgeschrittene Analysen in Richtung der Datenbank-Engine zu verlagern und stellen das Grizzly-Framework als DataFrame-zu-SQL-Transpiler vor. Auf dieser Grundlage identifizieren wir benutzerdefinierte Funktionen (UDFs) und maschinelles Lernen (ML) als wichtige Aufgaben, die von einer tieferen Integration in die Datenbank-Engine profitieren würden. Daher untersuchen und bewerten wir Ansätze für die datenbankinterne Ausführung von Python-UDFs und datenbankinterne ML-Inferenz.Cloud computing has been the groundbreaking technology of the last decade. The ease-of-use of the managed environment in combination with nearly infinite amount of resources and a pay-per-use price model enables fast and cost-efficient project realization for a broad range of users. Cloud computing also changes the way software is designed, deployed and used. This thesis focuses on database systems deployed in the cloud environment. We identify three major interaction points of the database engine with the environment that show changed requirements compared to traditional on-premise data warehouse solutions. First, software is deployed on elastic resources. Consequently, systems should support elasticity in order to match workload requirements and be cost-effective. We present an elastic scaling mechanism for distributed database engines, combined with a partition manager that provides load balancing while minimizing partition reassignments in the case of elastic scaling. Furthermore we introduce a buffer pre-heating strategy that allows to mitigate a cold start after scaling and leads to an immediate performance benefit using scaling. Second, cloud based systems are accessible and available from nearly everywhere. Consequently, data is frequently ingested from numerous endpoints, which differs from bulk loads or ETL pipelines in a traditional data warehouse solution. Many users do not define database constraints in order to avoid transaction aborts due to conflicts or to speed up data ingestion. To mitigate this issue we introduce the concept of PatchIndexes, which allow the definition of approximate constraints. PatchIndexes maintain exceptions to constraints, make them usable in query optimization and execution and offer efficient update support. The concept can be applied to arbitrary constraints and we provide examples of approximate uniqueness and approximate sorting constraints. Moreover, we show how PatchIndexes can be exploited to define advanced constraints like an approximate multi-key partitioning, which offers robust query performance over workloads with different partition key requirements. Third, data-centric workloads changed over the last decade. Besides traditional SQL workloads for business intelligence, data science workloads are of significant importance nowadays. For these cases the database system might only act as data delivery, while the computational effort takes place in data science or machine learning (ML) environments. As this workflow has several drawbacks, we follow the goal of pushing advanced analytics towards the database engine and introduce the Grizzly framework as a DataFrame-to-SQL transpiler. Based on this we identify user-defined functions (UDFs) and machine learning inference as important tasks that would benefit from a deeper engine integration and investigate approaches to push these operations towards the database engine

NoSQL Data Stores In Publish/Subscribe-Based RESTful Web Services

In the era of mobile cloud computing, the consumption of virtualized software and Web-based services from super-back-end infrastructure using smartphones and tablets is gaining much research attention from both the industry and academia. Nowadays, these mobile devices generate and access multimedia data hosted in social media and other sources in order to enhance the users’ multimedia experience. However, multimedia data is unstructured which can lead to challenges with data synchronization between these mobile devices and the cloud computing back-end. The issue with data synchronization is further fueled by the fact that mobile devices can experience intermittent connectivity losses due to unstable wireless bandwidths. While previous works proposed Simple Object Access Protocol (SOAP) -based middleware for the Web services’ synchronization, this approach is not efficient in a mobile environment because the SOAP protocol is verbose. Thus, the Representational State Transfer (REST) standard has been proposed recently to model the Web services since it is lightweight. This thesis proposes a novel approach for implementing a REST-based mobile Web Service for multimedia file sharing that utilizes a channel-based publish/subscribe communication scheme to synchronize smartphone or tablet-hosted NoSQL databases with a cloud-hosted NoSQL database. This thesis evaluates the synchronicity and the scalability of a prototype system that was implemented according to this approach. Also, this thesis assesses the overhead of the middleware component of the system