Computing systems are evolving to be more ubiquitous, heterogeneous, and dynamic.Many emerging domains, such as Internet of Things (IoT), federated learning, and smart buildings, rely on a diverse edge-to-cloud continuum where the execution of applications spans various tiers of systems with significantly different computational capabilities.Computing resources in each tier, such as processing units inside of in-the-field edge devices and high-performance servers in datacenters, are handled in isolation due to scalability and resource segregation.This practice results in task mappings limited to only a subset of all available processing units, preventing an efficient overall utilization of the system.In this paper, we propose a holistic approach to capture diverse computational characteristics of edge-cloud systems with arbitrary topologies and to efficiently manage computational resources with the whole continuum in the scope.Our approach is built upon a multi-layer graph-based hardware (HW) representation and a modular performance modeling interface that can capture interactions and interference between computational resources in the system.We introduce an orchestrator mechanism that leverages the graph-based HW representation to hierarchically locate processing units to which a given set of tasks can be mapped while respecting the isolation between the computational tiers of an edge-cloud system.We demonstrate the utility of our approach on two distinct edge-cloud systems deployed in the field, improving the latency up to 47% over the best baseline with less than 2% scheduling overhead and reducing the average prediction error rate from 27.4% to 3.2%.

In recent years, deep neural networks (DNNs) have gained widespread adoption for continuous mobile object detection (OD) tasks, particularly in autonomous systems. However, a prevalent issue in their deployment is the one-size-fits-all approach, where a single DNN is used, resulting in inefficient utilization of computational resources. This inefficiency is particularly detrimental in energy-constrained systems, as it degrades overall system efficiency. We identify that, the contextual information embedded in the input data stream (e.g., the frames in the camera feed that the OD models are run on) could be exploited to allow a more efficient multi-model-based OD process. In this paper, we propose SHIFT which continuously selects from a variety of DNN-based OD models depending on the dynamically changing contextual information and computational constraints. During this selection, SHIFT uniquely considers multi-accelerator execution to better optimize the energy-efficiency while satisfying the latency constraints. Our proposed methodology results in improvements of up to 7.5x in energy usage and 2.8x in latency compared to state-of-the-art GPU-based single model OD approaches.

Summary The Blue Waters system, installed in 2012 at NCSA, has the largest component count of any system Cray has built. Blue Waters includes a mix of dual‐socket CPU (XE) and single‐socket CPU, single GPU (XK) nodes. The primary storage is provided by Cray's Sonexion/ClusterStor Luster storage system delivering 35 PB (raw) storage at 1 TB/s. The statistical failure rates over time for each component including CPU, DIMM, GPU, disk drive, power supply, blower, etc and their impact on higher level failure rates for individual nodes and the systems as a whole are presented in detail, with a particular emphasis on identifying any increases in rate that might indicate the right‐side of the expected bathtub curve has been reached. Strategies employed by NCSA and Cray for minimizing the impact of component failure, such as the preemptive removal of suspect disk drives, are also presented.