Modern data storage systems are characterized by a distributed architecture as well as the presence of multiple storage tiers and caches. Both system developers and operators are challenged with the complexity of such systems as it is hard to evaluate how a configuration change will impact the workload or system performance and identify the best configuration to satisfy some performance objective. DITIS is a new simulator that models the end-to-end execution of file requests on distributed tiered storage systems that addresses the aforementioned challenges efficiently without any costly system redeployments. The demonstration will showcase the key functionalities and benefits offered by DITIS, including (i) analyzing workload traces to understand their characteristics and the behavior of the underlying storage system; (ii) running simulations with different configurations to evaluate their impact on performance; and (iii) running optimizations over custom search spaces to find the best configuration that satisfies a given objective.

Research on data dependencies has experienced a revival as dependency violations can reveal errors in data. Several data cleaning systems use a DBMS to detect such violations. While DBMSs are efficient for some kinds of data dependencies (e.g., unique constraints), they are likely to fall short of satisfactory performance for more complex ones, such as order dependencies.

MapReduce query processing systems translate a query statement into a query plan, consisting of a set of MapReduce jobs to be executed in distributed machines. During query translation, these query systems uniformly allocate computing resources to each job by delegating the same tuning to the entire query plan. However, jobs may implement their own collection of operators, which lead to different usage of computing resources. In this paper we propose an adaptive tuning mechanism that enables setting specific resources to each job within a query plan. Our adaptive mechanism relies on a data structure that maps jobs to tuning codes by analyzing source code and log files. This adaptive mechanism allows delegating specific resources to the query plan at runtime as the data structure hosts specific pre-computed tuning codes.