We live in the golden age of distributed computing. Public cloud platforms now offer virtually unlimited compute and storage resources on demand. At the same time, the Software-as-a-Service (SaaS) model brings enterprise-class systems to users who previously could not afford such systems due to their cost and complexity. Alas, traditional data warehousing systems are struggling to fit into this new environment. For one thing, they have been designed for fixed resources and are thus unable to leverage the cloud's elasticity. For another thing, their dependence on complex ETL pipelines and physical tuning is at odds with the flexibility and freshness requirements of the cloud's new types of semi-structured data and rapidly evolving workloads. We decided a fundamental redesign was in order. Our mission was to build an enterprise-ready data warehousing solution for the cloud. The result is the Snowflake Elastic Data Warehouse, or "Snowflake" for short. Snowflake is a multi-tenant, transactional, secure, highly scalable and elastic system with full SQL support and built-in extensions for semi-structured and schema-less data. The system is offered as a pay-as-you-go service in the Amazon cloud. Users upload their data to the cloud and can immediately manage and query it using familiar tools and interfaces. Implementation began in late 2012 and Snowflake has been generally available since June 2015. Today, Snowflake is used in production by a growing number of small and large organizations alike. The system runs several million queries per day over multiple petabytes of data.

This paper presents Polybase, a feature of SQL Server PDW V2 that allows users to manage and query data stored in a Hadoop cluster using the standard SQL query language. Unlike other database systems that provide only a relational view over HDFS-resident data through the use of an external table mechanism, Polybase employs a split query processing paradigm in which SQL operators on HDFS-resident data are translated into MapReduce jobs by the PDW query optimizer and then executed on the Hadoop cluster. The paper describes the design and implementation of Polybase along with a thorough performance evaluation that explores the benefits of employing a split query processing paradigm for executing queries that involve both structured data in a relational DBMS and unstructured data in Hadoop. Our results demonstrate that while the use of a split-based query execution paradigm can improve the performance of some queries by as much as 10X, one must employ a cost-based query optimizer that considers a broad set of factors when deciding whether or not it is advantageous to push a SQL operator to Hadoop. These factors include the selectivity factor of the predicate, the relative sizes of the two clusters, and whether or not their nodes are co-located. In addition, differences in the semantics of the Java and SQL languages must be carefully considered in order to avoid altering the expected results of a query.

Distributed systems will be increasingly built on top of wireless networks, such as sensor networks or hand-held devices with advanced sensing and computational abilities. Supporting cooperative processes executed by such unreliable and dynamic system components poses a various number of new technic

Workflow concepts are well suited for scenarios where many distributed entities work collaboratively together to achieve a common goal. Today, workflows are mostly used as computerized model for business processes executed in instances in commercial Workflow Management Systems. However, there are many other application domains where computer-supported cooperative work can be captured and organized by workflows. In this paper, we investigate the task of scheduling workflows in self-organizing wireless networks for disaster scenarios. Most research work in the field of workflow scheduling has been driven by temporal and causality constraints. We present an adaptive scheduling algorithm that finds a suitable execution sequence for workflow activities by additionally considering resource allocation constraints and dynamic topology changes. Our approach utilizes a multi-stage distribution algorithm which we extend with techniques to cope with network dynamics.

Today's applications are increasingly composed out of services. Standardized protocols, such as SOAP, WSDL, and UDDI are used to discover and invoke remotely located services. Nowadays, improved resource capabilities of mobile devices, e.g. PDAs, smart phones, and sensor devices allow the execution of services even on these smaller computing devices. In comparison to traditional centralized process management, a decentralized, cooperative execution of services on embedded real-time systems leads to higher system scalability, better system response time and higher data accuracy. In this paper we describe an efficient way to deploy services onto highly distributed, mobile, and unreliable devices. To achieve an efficient resource tracking we utilize different group-based data retrieval strategies. Furthermore, we present a prototype system that implements our distributed service deployment algorithm and that evaluates our approach in terms of scalability for different network topologies.