Architecting Data Centers for High Efficiency and Low Latency

Modern data centers, housing remarkably powerful computational capacity, are built in massive scales and consume a huge amount of energy. The energy consumption of data centers has mushroomed from virtually nothing to about three percent of the global electricity supply in the last decade, and will continuously grow. Unfortunately, a significant fraction of this energy consumption is wasted due to the inefficiency of current data center architectures, and one of the key reasons behind this inefficiency is the stringent response latency requirements of the user-facing services hosted in these data centers such as web search and social networks. To deliver such low response latency, data center operators often have to overprovision resources to handle high peaks in user load and unexpected load spikes, resulting in low efficiency. This dissertation investigates data center architecture designs that reconcile high system efficiency and low response latency. To increase the efficiency, we propose techniques that understand both microarchitectural-level resource sharing and system-level resource usage dynamics to enable highly efficient co-locations of latency-critical services and low-priority batch workloads. We investigate the resource sharing on real-system simultaneous multithreading (SMT) processors to enable SMT co-locations by precisely predicting the performance interference. We then leverage historical resource usage patterns to further optimize the task scheduling algorithm and data placement policy to improve the efficiency of workload co-locations. Moreover, we introduce methodologies to better manage the response latency by automatically attributing the source of tail latency to low-level architectural and system configurations in both offline load testing environment and online production environment. We design and develop a response latency evaluation framework at microsecond-level precision for data center applications, with which we construct statistical inference procedures to attribute the source of tail latency. Finally, we present an approach that proactively enacts carefully designed causal inference micro-experiments to diagnose the root causes of response latency anomalies, and automatically correct them to reduce the response latency.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144144/1/yunqi_1.pd

Multi-core devices for safety-critical systems: a survey

Multi-core devices are envisioned to support the development of next-generation safety-critical systems, enabling the on-chip integration of functions of different criticality. This integration provides multiple system-level potential benefits such as cost, size, power, and weight reduction. However, safety certification becomes a challenge and several fundamental safety technical requirements must be addressed, such as temporal and spatial independence, reliability, and diagnostic coverage. This survey provides a categorization and overview at different device abstraction levels (nanoscale, component, and device) of selected key research contributions that support the compliance with these fundamental safety requirements.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-65316-P, Basque Government under grant KK-2019-00035 and the HiPEAC Network of Excellence. The Spanish Ministry of Economy and Competitiveness has also partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft

eChIDNA: Continuous Data Validation in Advanced Metering Infrastructures

New laws and regulations increase the demands for a more data-intense metering infrastructure towards more adaptive electricity networks (aka smart grids). The automatic measuring, often involving wireless communication, introduces errors both in software and during data transmission. These demands, as well as the large data volumes that need to be validated, present new challenges to utilities. First, measurement errors cannot be allowed to propagate to the data stored by utilities. Second, manual fixing of errors after storing is not a feasible option with increasing data volumes and decreasing lead times for new services and analysis. Third, validation is not only to be applied to current readings but also to past readings when new types of errors are discovered. This paper addresses these issues by proposing a hybrid system, eChIDNA, utilizing both the store-then-process and the data streaming processing paradigms, enabling for high throughput, low latency distributed and parallel analysis. Validation rules are built upon this paradigm and then implemented on the state of the art Apache Storm Stream Processing Engine to assess performance. Furthermore, patterns of common errors are matched, triggering alerts as a first step towards automatic correction of errors. The system is evaluated with production data from hundreds of thousands of smart meters. The results show a performance in the thousands messages per second realm, showing that stream processing can be used to validate large volumes of meter data online with low processing latency, identifying common errors as they appear. The results from the pattern matching are cross-validated with system experts and show that pattern matching is a viable way to minimize time required from human operators