Serverless Stream-Based Processing for Real-Time Insights

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Building on our previous posts regarding messaging patterns and queue-based processing, we now explore stream-based processing and how it helps you achieve low-latency, near real-time data processing …

Scalable and Reliable Multi-dimensional Sensor Data …

reconfiguration at runtime, and preserve the scalability and reliability qualities of stream processing techniques. We propose a stream processing architecture fulfilling these …

Scalable fault-tolerant aggregation in large process groups

The paper discusses fault-tolerant, scalable solutions to the problem of accurately and scalably calculating global aggregate functions in large process groups communicating over unreliable networks. These groups could represent sensors or processes communicating over a network that is either fixed (e.g., the Internet) or dynamic (e.g., …

Build a fault-tolerant, serverless data aggregation pipeline with

The business problem of real-time data aggregation is faced by customers in various industries like manufacturing, retail, gaming, utilities, and financial services. In a previous post, we discussed an example from the banking industry: real-time trade risk aggregation. Typically, financial institutions associate every trade that is performed on …

Data Pipeline Architecture Explained: 6 Diagrams And Best …

Data pipeline architecture is the process of designing how data is surfaced from its source system to the consumption layer. This frequently involves, in some order, extraction (from a source system), transformation (where data is combined with other data and put into the desired format), and loading (into storage where it can be accessed). …

Scalable Hierarchical Aggregation and Reduction Protocol …

This paper describes SHARP Streaming-Aggregation hardware architecture and a set of synthetic and application benchmarks used to study this new reduction capability, and …

Introduction

Introduction. NVIDIA® Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)™ technology improves the performance of MPI and Machine Learning collective operation, by offloading collective operations from CPUs and GPUs to the network and eliminating the need to send data multiple times between endpoints. This innovative …

Scalable Aggregation Service for Satellite Remote Sensing …

This paper addresses two specific challenges in satellite data aggregation: 1) how to efficiently aggregate data from pixel level to grid level in a distributed …

Scalable Feedback Aggregating (SFA) Overlay for Large …

In this paper, we proposed a scalable feedback aggregating (SFA) overlay for large-scale P2P trust evaluation. First, the local trust rating method is defined based …

Introducing the Enhanced Layered Scalable Architecture …

SAP built the Layered Scalable Architecture (LSA) around the best-practice model that was created by one of the thought-leaders in data warehousing, Bill Inmon. The basic principle of the Inmon model, known as the Corporate Information Factory, was to develop a set of consistent and highly re-useable data modeling layers that included data ...

Scalable Feedback Aggregating (SFA) Overlay for Large …

However, most previous works either paid little attention to the scalability of feedback aggregating overlay or relied on the flooding-based strategy to collect feedback, which greatly affects the system scalability. In this paper, we proposed a scalable feedback aggregating (SFA) overlay for large-scale P2P trust evaluation.

Adaptive Aggregation For Federated Learning

Abstract—In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint.

Scalable hierarchical aggregation protocol (SHArP)

Scalable hierarchical aggregation protocol (SHArP): a hardware architecture for efficient data reduction ... this paper describes the SHArP technology designed to offload collective operation processing to the network. ... R. S. Amant, K. Sankaralingam, and D. Burger, "Dark silicon and the end of multicore scaling," in …

Sensors | Free Full-Text | Deep Layer Aggregation Architectures …

This paper introduces a deep learning approach to photorealistic universal style transfer that extends the PhotoNet network architecture by adding extra feature-aggregation modules. Given a pair of images representing the content and the reference of style, we augment the state-of-the-art solution mentioned above with deeper …

Scalable Hierarchical Aggregation Protocol (SHArP): A …

Scalable Hierarchical Aggregation Protocol (SHArP): A Hardware Architecture for Efficient Data Reduction. Abstract: Increased system size and a greater reliance on …

Performance Analysis of Packet Aggregation Mechanisms …

4.3. Modelling of the Size-Based Packet Aggregation Process. For a size-based packet aggregation mechanism, the content of the buffer is aggregated into an aggregated (larger) packet when the maximum size threshold N is reached. When a small packet arrives, its size is compared to the difference between the maximum size …

Synthesis and functionalization of scalable and versatile 2D …

Here we detail the process to prepare large 2D protein films with user-defined features and structures via the amyloid-like aggregation of commonly synthesized proteins.

A Scalable Hierarchically Distributed Architecture for Next …

The rigidity of traditional network architectures, with tightly coupled control and data planes, impairs their ability to adapt to highly dynamic requirements of future application domains. While Software-Defined Networking (SDN) can provide the required dynamism, it suffers from scalability issues. Therefore, efforts have been made to …

Scalable Hierarchical Aggregation and Reduction …

aggregation capability added to Mellanox's Scalable Hierarchical Aggre-gation and Reduction Protocol in its HDR InfiniBand switches. For large messages, this capability is designed to achieve reduction band-widths similar to those of point-to-point messages of the same size, and complements the latency-optimized low-latency aggregation reduc-

Scalable and Reliable Multi-Dimensional Aggregation of …

processing architecture for aggregating data streams. Section V shows how we implement this architecture in an industry-applied IoT monitoring platform. Section VI …

TornadoAggregate: Accurate and Scalable Federated …

Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much consideration of the communication scalability or the diurnal property relied on clients' local time variety.In contrast, ring architecture can resolve the scalability …

Scalable Hierarchical Aggregation and Reduction Protocol …

Interior aggregation-group nodes forward the data to their parent, and the root of the aggregation-group initiates result distribution. An important feature of the aggregation protocol is that a single result is forwarded towards the root of the tree from each AN thereby reducing the amount of data forwarded by its aggregation radix.

Scalable and Reliable Multi-dimensional Sensor Data …

We generate the workload described in the scalability evaluation, which simulates 4 nested groups of sensors, and aggregate the data with 24 instances of the aggregation component. After 10 min of processing, we inject a. 1Due to Kafka Streams' task model, the throughput is subject to large fluctuations.

Scalable Aggregation Service for Satellite Remote Sensing Data

Have feedback or suggestions for a way to ... Jianyu, Rajapakshe, Chamara, Kay, Savio, Kandoor, Lakshmi, Maxwell, Thomas, and Zhang, Zhibo. Scalable Aggregation Service for Satellite Remote Sensing Data ... Proceedings of the 20th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2020 ...

Adaptive Aggregation For Federated Learning

In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint. Next, we present the …

(PDF) Scalable and Reliable Multi-Dimensional Aggregation …

We argue that a scalable architecture in this context must meet requirements for fault tolerance, extensibility, real-time data processing, and resource efficiency. ... Window aggregation is a ...

Adaptive Aggregation For Federated Learning

Abstract. In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint. …

Scalable Hierarchical Aggregation Protocol (SHArP): A …

The SHArP technology designed to offload collective operation processing to the network is described, implemented in Mellanox's SwitchIB-2 ASIC, using innetwork trees to reduce data from a group of sources, and to distribute the result. Increased system size and a greater reliance on utilizing system parallelism to achieve computational …

Scalable and Reliable Multi-dimensional Sensor Data Aggregation …

The dual streaming model [] is the foundation for the stream processing architectures described in this paper.It is a model to define the semantics of a stream processing architecture. It adopts the notion of data streams and streaming operators from other established stream processing models [10,11,12].A data stream is an …

Scalable hierarchical aggregation protocol (SHArP)

This paper describes the new hardware-based streaming-aggregation capability added to Mellanox's Scalable Hierarchical Aggregation and Reduction Protocol in its HDR …

Scalable Multi-Agent Reinforcement Learning through …

p(i) and velocity of agent in a global frame, and. goal is the position of the goal relative to the agent's position. InforMARL uses this information mode. Global: Here, o(i) =. glob [p(i); v(i); p(i) goal; p(i) other], where p(i) comprises of the relative positions of all the other other entities in the environment.