Gennaro Mellone works at University of Naples, Parthenope.

Posts

ECO-LLM: LLM-based Edge Cloud Optimization

AI/ML techniques have been used to solve systems problems, but their applicability to customize solutions on-the-fly has been limited. Traditionally, any customization required manually changing the AI/ML model or modifying the code, configuration parameters, application settings, etc. This incurs too much time and effort, and is very painful. In this paper, we propose a novel technique using Generative Artificial Intelligence (GenAI) technology, wherein instructions can be provided in natural language and actual code to handle any customization is automatically generated, integrated and applied on-the-fly. Such capability is extremely powerful since it makes customization of application settings or solution techniques super easy. Specifically, we propose ECO-LLM (LLM-based Edge Cloud Optimization), which leverages Large Language Models (LLM) to dynamically adjust placement of application tasks across edge and cloud computing tiers, in response to changes in application workload, such that insights are delivered quickly with low cost of operation (systems problem). Our experiments with real-world video analytics applications i.e. face recognition, human attributes detection and license plate recognition show that ECO-LLM is able to automatically generate code on-the-fly and adapt placement of application tasks across edge and cloud computing tiers. We note that the trigger workload (to switch between edge and cloud) for ECO-LLM is exactly the same as the baseline (manual) and actual placement performed by ECO-LLM is only slightly different i.e. on average (across 2 days) only 1.45% difference in human attributes detection and face recognition, and 1.11% difference in license plate recognition. Although we tackle this specific systems problem in this paper, our proposed GenAI-based technique is applicable to solve other systems problems too.

Improving Real-time Data Streams Performance on Autonomous Surface Vehicles using DataX

In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge environments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine situations require sophisticated and highly efficient platforms. This study optimizes real-time operations on a containerized distributed processing platform designed for Autonomous Surface Vehicles (ASV) to help safeguard the marine environment. The primary objective is to improve the efficiency and speed of data processing by adopting a microservice management system called DataX. DataX leverages containerization to break down operations into modular units, and resource coordination is based on Kubernetes. This combination of technologies enables more efficient resource management and real-time operations optimization, contributing significantly to the success of marine missions. The platform was developed to address the unique challenges of managing data and running advanced algorithms in a marine context, which often involves limited connectivity, high latencies, and energy restrictions. Finally, as a proof of concept to justify this platform’s evolution, experiments were carried out using a cluster of single-board computers equipped with GPUs, running an AI-based marine litter detection application and demonstrating the tangible benefits of this solution and its suitability for the needs of maritime missions.

Citizen Science for the Sea with Information Technologies: An Open Platform for Gathering Marine Data and Marine Litter Detection from Leisure Boat Instruments

Data crowdsourcing is an increasingly pervasive and lifestyle-changing technology due to the flywheel effect that results from the interaction between the Internet of Things and Cloud Computing. This paper presents the Citizen Science for the Sea with Information Technologies (C4Sea-IT) framework. It is an open platform for gathering marine data from leisure boat instruments. C4Sea-IT aims to provide a coastal marine data gathering, moving, processing, exchange, and sharing platform using the existing navigation instruments and sensors for today’s leisure and professional vessels. In this work, a use case for the detection and tracking of marine litter is shown. The final goal is weather/ocean forecasts argumentation with Artificial Intelligence prediction models trained with crowdsourced data.

AnB: Application-In-A-Box To Rapidly Deploy and Self-Optimize 5G Apps

We present Application in a Box (AnB) product concept aimed at simplifying the deployment and operation of remote 5G applications. AnB comes pre-configured with all necessary hardware and software components, including sensors like cameras, hardware and software components for a local 5G wireless network, and 5G-ready apps. Enterprises can easily download additional apps from an App Store. Setting up a 5G infrastructure and running applications on it is a significant challenge, but AnB is designed to make it fast, convenient, and easy, even for those without extensive knowledge of software, computers, wireless networks, or AI-based analytics. With AnB, customers only need to open the box, set up the sensors, turn on the 5G networking and edge computing devices, and start running their applications. Our system software automatically deploys and optimizes the pipeline of microservices in the application on a tiered computing infrastructure that includes device, edge, and cloud computing. Dynamic resource management, placement of critical tasks for low-latency response, and dynamic network bandwidth allocation for efficient 5G network usage are all automatically orchestrated. AnB offers cost savings, simplified setup and management, and increased reliability and security. We’ve implemented several real-world applications, such as collision prediction at busy traffic light intersections and remote construction site monitoring using video analytics. With AnB, deployment and optimization effort can be reduced from several months to just a few minutes. This is the first-of-its-kind approach to easing deployment effort and automating self-optimization of the application during system operation.

Content-aware auto-scaling of stream processing applications on container orchestration platforms

Modern applications are designed as an interacting set of microservices, and these applications are typically deployed on container orchestration platforms like Kubernetes. Several attractive features in Kubernetes make it a popular choice for deploying applications, and automatic scaling is one such feature. The default horizontal scaling technique in Kubernetes is the Horizontal Pod Autoscaler (HPA). It scales each microservice independently while ignoring the interactions among the microservices in an application. In this paper, we show that ignoring such interactions by HPA leads to inefficient scaling, and the optimal scaling of different microservices in the application varies as the stream content changes. To automatically adapt to variations in stream content, we present a novel system called DataX AutoScaler that leverages knowledge of the entire stream processing application pipeline to efficiently auto-scale different microservices by taking into account their complex interactions. Through experiments on real-world video analytics applications, such as face recognition and pose classification, we show that DataX AutoScaler adapts to variations in stream content and achieves up to 43% improvement in overall application performance compared to a baseline system that uses HPA.

DataXc: Flexible and efficient communication in microservices-based stream analytics pipelines

A big challenge in changing a monolithic application into a performant microservices-based application is the design of efficient mechanisms for microservices to communicate with each other. Prior proposals range from custom point-to-point communication among microservices using protocols like gRPC to service meshes like Linkerd to a flexible, many-to-many communication using broker-based messaging systems like NATS. We propose a new communication mechanism, DataXc, that is more efficient than prior proposals in terms of message latency, jitter, message processing rate and use of network resources. To the best of our knowledge, DataXc is the first communication design that has the desirable flexibility of a broker-based messaging systems like NATS and the high-performance of a rigid, custom point-to-point communication method. DataXc proposes a novel “pull” based communication method (i.e consumers fetch messages from producers). This is unlike prior proposals like NATS, gRPC or Linkerd, all of which are “push” based (i.e. producers send messages to consumers). Such communication methods make it difficult to take advantage of differential processing rates of consumers like video analytics tasks. In contrast, DataXc proposes a “pull” based design that avoids unnecessary communication of messages that are eventually discarded by the consumers. Also, unlike prior proposals, DataXc successfully addresses several key challenges in streaming video analytics pipelines like non-uniform processing of frames from multiple cameras, and high variance in latency of frames processed by consumers, all of which adversely affect the quality of insights from streaming video analytics. We report results on two popular real-world, streaming video analytics pipelines (video surveillance, and video action recognition). Compared to NATS, DataXc is just as flexible, but it has far superior performance: upto 80% higher processing rate, 3X lower latency, 7.5X lower jitter and 4.5X lower network bandwidth usage. Compared to gRPC or Linkerd, DataXc is highly flexible, achieves up to 2X higher processing rate, lower latency and lower jitter, but it also consumes more network bandwidth.