Dynamic Data Streaming for an Appliance Many applications require to analyse large amounts of continuous flows of data produced by different data sources before the data is stored. Data streaming engines emerged as a solution for processing data on the fly. At the same time, computer architectures have evolved to systems with several interconnected CPUs and Non Uniform Memory Access (NUMA), where the cost of accessing memory from a core depends on how CPUs are interconnected. In order to get better resource utilization and adaptiveness to the load dynamic migration of queries must be available in data streaming engines. Moreover, data streaming applications require high availability so that failures do not cause service interruption and losing data. This paper presents the dynamic migration and faulttolerance capabilities of UPMCEP, a data streaming engine designed to take advantage of NUMA architectures. The preliminary evaluation using Intel HiBench benchmark shows the effect of the query migration and faulttolerance on the system performance.

IO Acceleration via MultiTiered Data Buffering and Prefetching Modern HighPerformance Computing (HPC) systems are adding extra layers to the memory and storage hierarchy, named deep memory and storage hierarchy (DMSH), to increase IO performance. New hardware technologies, such as NVMe and SSD, have been introduced in burst buffer installations to reduce the pressure for external storage and boost the burstiness of modern IO systems. The DMSH has demonstrated its strength and potential in practice. However, each layer of DMSH is an independent heterogeneous system and data movement among more layers is significantly more complex even without considering heterogeneity. How to efficiently utilize the DMSH is a subject of research facing the HPC community. Further, accessing data with a highthroughput and lowlatency is more imperative than ever. Data prefetching is a wellknown technique for hiding read latency by requesting data before it is needed to move it from a highlatency medium (e.g., disk) to a lowlatency one (e.g., main memory). However, existing solutions do not consider the new deep memory and storage hierarchy and also suffer from underutilization of prefetching resources and unnecessary evictions. Additionally, existing approaches implement a clientpull model where understanding the applicationxe2x80x99s IO behavior drives prefetching decisions. Moving towards exascale, where machines run multiple applications concurrently by accessing files in a workflow, a more datacentric approach resolves challenges such as cache pollution and redundancy. In this paper, we present the design and implementation of Hermes: a new, heterogeneousaware, multitiered, dynamic, and distributed IO buffering system. Hermes enables, manages, supervises, and, in some sense, extends IO buffering to fully integrate into the DMSH. We introduce three novel data placement policies to efficiently utilize all layers and we present three novel techniques to perform memory, metadata, and communication management in hierarchical buffering systems. Additionally, we demonstrate the benefits of a truly hierarchical data prefetcher that adopts a serverpush approach to data prefetching. Our evaluation shows that, in addition to automatic data movement through the hierarchy, Hermes can significantly accelerate IO and outperforms by more than 2x stateoftheart buffering platforms. Lastly, results show 10%xe2x80x9335% performance gains over existing prefetchers and over 50% when compared to systems with no prefetching.