In the 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) A key barrier to building performant, remotely managed and self-optimizing multi-sensor, distributed stream processing edge applications is high programming complexity. We recently proposed DataX , a novel platform that improves programmer productivity by enabling easy exchange, transformations, … Continue reading DataXe: A System for Application Self-optimization in Serverless Edge Computing Environments
The exponential growth in smart sensors and rapid progress in 5G networks is creating a world awash with data streams. However, a key barrier to building perfor- mant multi-sensor, distributed stream processing applications is high programming complexity. We propose DataX, a novel platform that improves programmer productivity by enabling easy exchange, transformations, and fusion of data streams. DataX abstraction simplifies the application’s specification and exposes parallelism and dependencies among the application functions (microservices). DataX runtime automatically sets up appropriate data communication mechanisms, enables effortless reuse of microservices and data streams across applications, and leverages serverless computing to transform, fuse, and auto-scale microservices. DataX makes it easy to write, deploy and reliably operate distributed applications at scale. Synthesizing these capabilities into a single platform is substantially more transformative than any available stream processing system.
Microservices-based video analytics pipelines routinely use multiple deep convolutional neural networks. We observe that the best allocation of resources to deep learning engines (or microservices) in a pipeline, and the best configuration of parameters for each engine vary over time, often at a timescale of minutes or even seconds based on the dynamic content in the video. We leverage these observations to develop Magic-Pipe, a self-optimizing video analytic pipeline that leverages AI techniques to periodically self-optimize. First, we propose a new, adaptive resource allocation technique to dynamically balance the resource usage of different microservices, based on dynamic video content. Then, we propose an adaptive microservice parameter tuning technique to balance the accuracy and performance of a microservice, also based on video content. Finally, we propose two different approaches to reduce unnecessary computations due to unavoidable mismatch of independently designed, re-usable deep-learning engines: a deep learning approach to improve the feature extractor performance by filtering inputs for which no features can be extracted, and a low-overhead graph-theoretic approach to minimize redundant computations across frames. Our evaluation of Magic-Pipe shows that pipelines augmented with self-optimizing capability exhibit application response times that are an order of magnitude better than the original pipelines, while using the same hardware resources, and achieving similar high accuracy.
Keyboard by ainul muttaqin from the Noun Project This is a "brief" tale about how I used uinput and evdev to remap some keys of my latop's keyboard. Using this "low-level" approach it is possible to remap "unreamappable" key bindings, such as Alt+Right to End or Alt+Left to Home. I've been using, almost exclusively, an … Continue reading Low-level mapping of key combinations in GNU/Linux (remapping the unremappable)
"... mi ritrovai per una selva oscura, ché la diritta via era smarrita ..." (Divina Commedia - Canto I) Like Dante in his allegoric journey, I (also) "found myself deep in a darkened forest, for I had lost all trace of the straight path" in the computing world. The "darkened forest" in which I found … Continue reading First step of my journey for rediscovering the straight path (aka I’m using again GNU Emacs after many years)
In Proceedings of the 23nd international symposium on High-performance parallel and distributed computing (HPDC ’14). ACM, New York, NY, USA, 1-12. Intel Xeon Phi coprocessors provide excellent performance acceleration for highly parallel applications and have been deployed in several top-ranking supercomputers. One popular approach of programming the Xeon Phi is the offload model, where parallel … Continue reading Snapify: capturing snapshots of offload applications on Xeon Phi manycore processors