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The high cost associated with powering servers has introduced new challenges in improving the energy efficiency of clusters running data processing jobs. Traditional high-performance servers are largely energy inefficient due to various factors such as the over-provisioning of resources. The increasing trend to replace traditional high-performance server(More)
The biggest change in the TPC benchmarks in over two decades is now well underway – namely the addition of an energy efficiency metric along with traditional performance metrics. This change is fueled by the growing, real, and urgent demand for energy-efficient database processing. Database query processing engines must now consider becoming energy-aware,(More)
—As traditional and mission-critical relational database workloads migrate to the cloud in the form of Database-as-a-Service (DaaS), there is an increasing motivation to provide performance goals in Service Level Objectives (SLOs). Providing such performance goals is challenging for DaaS providers as they must balance the performance that they can deliver(More)
Energy is a growing component of the operational cost for many " big data " deployments, and hence has become increasingly important for practitioners of large-scale data analysis who require scale-out clusters or parallel DBMS appliances. Although a number of recent studies have investigated the energy efficiency of DBMSs, none of these studies have looked(More)
In this paper we investigate some opportunities and challenges that arise in energy-aware computing in a cluster of servers running data-intensive workloads. We leverage the insight that servers in a cluster are often underutilized, which makes it attractive to consider powering down some servers and redistributing their load to others. Of course, powering(More)
—Long time-series datasets are common in many domains, especially scientific domains. Applications in these fields often require comparing trajectories using similarity measures. Existing methods perform well for short time-series but their evaluation cost degrades rapidly for longer time-series. In this work, we develop a new time-series similarity measure(More)
The current computing trend towards cloud-based Database-as-a-Service (DaaS) as an alternative to traditional on-site relational database management systems (RDBMSs) has largely been driven by the perceived simplicity and cost-effectiveness of migrating to a DaaS. However, customers that are attracted to these DaaS alternatives may find that the range of(More)
As the size and complexity of analytic data processing systems continue to grow, the effort required to mitigate faults and performance skew has also risen. However, in some environments we have encountered, users prefer to continue query execution even in the presence of failures (e.g., the unavailability of certain data sources), and receive a "partial"(More)