Application of Micro-Genetic Algorithm for Task Based Computing

Abstract — Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the environment is dynamic and heterogeneous due to e.g., mobility of the user. An algorithm that decides on a component set and allocates it onto hosts accordingly to user task preferences and the platform constraints plays a central role in the application composition process. In this paper we will describe an algorithm for task-based application allocation. The algorithm uses micro-genetic approach and is characterized by a very low computational load and good convergence properties. We will compare the performance and the scalability of our algorithm with a straightforward evolutionary algorithm. Besides, we will outline a system for task-based computing where our algorithm is used. I

Towards User-Oriented Application Composition

International audienceThis paper presents an autonomic system for composing ubiquitous applications at run-time. The applications are composed according to the user preferences collected via a physical user interface. This interface allows users to specify preferences by simple actions of touching with their mobile terminals icons in the environment, instead of explicitly selecting resources and dealing with their properties. In this paper, we present a system prototype and an example multimedia application. We also evaluate the performance of the prototype and the allocation algorithm which is used to compose applications

Unleashing GPUs for Network Function Virtualization: an open architecture based on Vulkan and Kubernetes

International audienceGeneral-purpose computing on graphics processing units (GPGPU) is a promising way to speed up computationally intensive network functions, such as performing real-time traffic classification based on machine learning. Recent studies have focused on integrated graphics units and various performance optimizations to address bottlenecks such as latency. However, these approaches tend to produce architecture-specific binaries and lack the orchestration of functions. A complementary effort would be a GPGPU architecture based on standard and open components, which allows the creation of interoperable and orchestrable network functions. This study describes and evaluates such open architecture based on the cross-platform Vulkan API, in which we execute handwritten SPIR-V code as a network function. We also demonstrate a multi-node orchestration approach for our proposed architecture using Kubernetes. We validate our architecture by executing SPIR-V code performing traffic classification with random forest inference. We test this application both on discrete and integrated graphics cards and on x86 and ARM. We find that in all cases the GPUs are faster than the baseline Cython code

CADEAU: Supporting Autonomic and User-Controlled Application Composition in Ubiquitous Environments

International audienceNetworked devices, such as consumer electronics, digital media appliances and mobile devices are rapidly filling our everyday environments and changing them into ubiquitous spaces. Composing an application from resources and services available in these environments is a complex task which requires solving a number of equally important engineering challenges as well as issues related to user behaviour and acceptance. In this chapter we introduce CADEAU, a prototype that addresses these challenges through a unique combination of autonomic mechanisms for application composition and methods for user interaction. These methods differ from each other in the degree to which the user is involved in the control of the prototype. They are offered so that users can choose the appropriate method according to their needs, the application and other context information. These methods use the mobile device as an interaction tool that connects users and resources in the ubiquitous space. We present the architecture, the interaction design and the implementation of CADEAU and give the results of a user study that involved 30 participants from various backgrounds. This study explores the balance between user control and system autonomy depending on different contexts, the user's needs and expertise. In particular, the study analyses the circumstances under which users prefer to rely on certain interaction methods for application composition. We argue that this study is a key step towards better user acceptance of future systems for the composition of ubiquitous applications

Modeling Mobile Code Acceleration in the Cloud

Peer reviewe

How Can AI be Distributed in the Computing Continuum? Introducing the Neural Pub/Sub Paradigm

This paper proposes the neural publish/subscribe paradigm, a novel approach to orchestrating AI workflows in large-scale distributed AI systems in the computing continuum. Traditional centralized broker methodologies are increasingly struggling with managing the data surge resulting from the proliferation of 5G systems, connected devices, and ultra-reliable applications. Moreover, the advent of AI-powered applications, particularly those leveraging advanced neural network architectures, necessitates a new approach to orchestrate and schedule AI processes within the computing continuum. In response, the neural pub/sub paradigm aims to overcome these limitations by efficiently managing training, fine-tuning and inference workflows, improving distributed computation, facilitating dynamic resource allocation, and enhancing system resilience across the computing continuum. We explore this new paradigm through various design patterns, use cases, and discuss open research questions for further exploration