Virtual and Extended Reality technologies are increasingly adopted in fields such as healthcare, entertainment, and education. These applications heavily rely on Smart Eye-Wears (SEWs) and AI to provide users with new ways to perceive their environment. However, SEWs face limitations in computational power, memory, and battery life. Offloading computations to external servers is a prominent example of edge computation. However, this also presents considerable challenges due to delays caused by varying network conditions and server workloads. This article proposes self-adaptive techniques based on tabular reinforcement learning (RL) to optimize the offloading of Deep Neural Network tasks between the SEW, the user’s smartphone, and cloud servers. The goal is to maintain a high-quality user experience while minimizing energy consumption and 5G connection costs. We evaluated our framework under varying 5G and WiFi bandwidths and cloud latency. The results show that Q-learning, SARSA, and Expected SARSA achieve near-optimal policies, with Q-learning demonstrating superior performance in reducing execution time violations (approximately at 10%) and improving agent stability. Additionally, our approach offers a more favorable tradeoff between energy efficiency and execution time violations compared to two baseline methods. Real-system experiments reveal that the proposed solution can double SEW battery life with respect to local computation while maintaining a good quality of service, with only 11% execution time violations. These findings highlight the effectiveness of our approach in managing resources and enhancing the overall user experience in SEW AI applications.

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test serverless application workflows running on multiple hardware and node combinations in cloud and edge environments. OSCAR-P obtains relevant profiling information on the execution time of the individual application components. These data are later used by aMLLibrary to train ML-based performance models. This makes it possible to predict the performance of applications on unseen configurations. We test our framework on clusters with different architectures (x86 and arm64) and workloads, considering multi-component use-case applications. This extensive experimental campaign proves the efficiency of OSCAR-P and aMLLibrary, significantly reducing the time needed for the application profiling, data collection, and data processing. The preliminary results obtained on the ML performance models accuracy show a Mean Absolute Percentage Error lower than 30% in all the considered scenarios.

The growth, development, and commercialization of artificial intelligence-based technologies such as self-driving cars, augmented-reality viewers, chatbots, and virtual assistants are driving the need for increased computing power. Most of these applications rely on Deep Neural Networks (DNNs), which demand substantial computing capacity to meet user demands. However, this capacity cannot be fully provided by users’ local devices due to their limited processing power, nor by cloud data centers due to high transmission latency from long distances. Edge cloud computing addresses this issue by processing user requests through 5G, which reduces transmission latency from local devices to computing resources and allows the offloading of some computations to cloud back-ends. This paper introduces a model for a Mobile Edge Cloud system designed for an application based on a DNN. The interaction among multiple mobile users and the edge platform is formulated as a one-leader multi-follower Stackelberg game, resulting in a challenging non-convex mixed integer nonlinear programming (MINLP) problem. To tackle this, we propose a heuristic approach based on Karush-Kuhn-Tucker conditions, which solves the MINLP problem significantly faster than the commercial state-of-the-art solvers (up to 50,000 times). Furthermore, we present an algorithm to estimate optimal platform profit when sensitive user parameters are unknown. Comparing this with the full-knowledge scenario, we observe a profit loss of approximately 1%. Lastly, we analyze the advantages for an edge provider to engage in a Stackelberg game rather than setting a fixed price for its users, showing potential profit increases ranging from 16% to 66%.

The proliferation of the Internet of Things, artificial intelligence, and real-time data processing applications has driven the demand for distributed computing architectures that span cloud, fog, and edge layers in a computing continuum. These architectures must address critical challenges in component placement and resource optimization to ensure low latency, cost efficiency, and compliance with Quality of Service (QoS) constraints. This paper introduces a novel optimization framework for addressing the joint problem of component placement and resource optimization in computing continua. The framework employs a Mixed Integer Nonlinear Programming model, where application components are modeled as a Directed Acyclic Graph and their performance is predicted using analytical models. A method based on the Karush-Kuhn-Tucker conditions is employed to compute the optimal number of virtual machine instances for a given component placement. This optimization is embedded within a reinforcement learning loop that iteratively refines placement decisions in response to fluctuations in workload. This hybrid approach ensures cost effectiveness while adhering to QoS constraints. Extensive experimental evaluations demonstrate the superiority of our framework. It outperforms leading approaches, including BARON solver, SPACE4AI-D, PPO_DLX, and a minimum k-cut baseline, achieving average cost reductions of 19%, 60%, 11%, and 6%, respectively, under dynamic workload conditions. These results highlight the efficiency, scalability, and adaptability of our approach, making it a robust solution to the demands of modern distributed systems.

Nowadays, Artificial Intelligence (AI) applications are becoming increasingly popular in a wide range of industries, mainly thanks to Deep Neural Networks (DNNs) that needs powerful resources. Cloud computing is a promising approach to serve AI applications thanks to its high processing power, but this sometimes results in an unacceptable latency because of long-distance communication. Vice versa, edge computing is close to where data are generated and therefore it is becoming crucial for their timely, flexible, and secure management. Given the more distributed nature of the edge and the heterogeneity of its resources, efficient component placement and resource allocation approaches become critical in orchestrating the application execution. In this paper, we formulate the resource selection and AI applications component placement problem in a computing continuum as a Mixed Integer Non-Linear Problem (MINLP), and we propose a design-time tool for its efficient solution. We first propose a Random Greedy algorithm to minimize the cost of the placement while guaranteeing some response time performance constraints. Then, we develop some heuristic methods such as Local Search, Tabu Search, Simulated Annealing and Genetic Algorithms, to improve the initial solutions provided by the Random Greedy. To evaluate our proposed approach, we designed an extensive experimental campaign, comparing the heuristics methods with one another and then the best heuristic against Best Cost Performance Constraint (BCPC) algorithm, a state-of-the-art approach. The results demonstrate that our proposed approach finds lower-cost solution than BCPC (27.6% on average) under the same time limit in large-scale systems. Finally, during the validation in a real edge system including FaaS resources our approach finds the globally optimal solution, suffering a deviation of around 12% between actual and predicted costs.

The adoption of Artificial intelligence (AI) technologies is steadily increasing. However, to become fully pervasive, AI needs resources at the edge of the network. The cloud can provide the processing power needed for big data, but edge computing is close to where data are produced and therefore crucial to their timely, flexible, and secure management. In this paper, we introduce the AI-SPRINT project, which will provide solutions to seamlessly design, partition, and run AI applications in computing continuum environments. AI-SPRINT will offer novel tools for AI applications development, secure execution, easy deployment, as well as runtime management and optimization: AI-SPRINT design tools will allow trading-off application performance (in terms of end-to-end latency or throughput), energy efficiency, and AI models accuracy while providing security and privacy guarantees. The runtime environment will support live data protection, architecture enhancement, agile delivery, runtime optimization, and continuous adaptation.

Artificial Intelligence (AI) and Deep Learning (DL) are pervasive today, with applications spanning from personal assistants to healthcare. Nowadays, the accelerated migration towards mobile computing and Internet of Things, where a huge amount of data is generated by widespread end devices, is determining the rise of the edge computing paradigm, where computing resources are distributed among devices with highly heterogeneous capacities. In this fragmented scenario, efficient component placement and resource allocation algorithms are crucial to orchestrate at best the computing continuum resources. In this paper, we propose a tool to effectively address the component placement problem for AI applications at design time. Through a randomized greedy algorithm, it identifies the placement of minimum cost providing performance guarantees across heterogeneous resources including edge devices, cloud GPU-based Virtual Machines and Function as a Service solutions.