The increasing proliferation of loT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-time processing. The compute continuum integrates edge and cloud resources to meet this need, balancing the low-latency demands of the edge with the high computational power of the cloud. However, managing resources in such a distributed environment presents challenges due to the diversity and complexity of these systems. Traditional resource management methods, often relying on heuristic algorithms, struggle to manage the increasing complexity, scale, and dynamics of these systems, as well as adapt to dynamic workloads and changing network conditions. Moreover, designing such approaches is often time-intensive and highly tailored to specific applications, demanding deep expertise. In this paper, we introduce a novel framework for intent-driven resource management in the compute continuum, using large language models (LLMs) to help automate decision-making processes. Our framework ensures that user-defined intents - such as achieving the required response times for time-critical applications - are consistently fulfilled. In the event of an intent violation, our system performs root cause analysis by examining system data to identify and address issues. This approach reduces the need for human intervention and enhances system reliability, offering a more dynamic and efficient solution for resource management in distributed environments.

The Internet of Things (IoT) has led to a surge in smart devices, generating vast volumes of data. Cloud computing offers scalability but does not suffice for many real-time and privacy-sensitive IoT applications. This limitation has prompted a blend of both edge and cloud resources, creating the need for seamless integration, known as the “compute continuum“. Testing applications and resource management techniques within this continuum is vital but can be very complex. Simulation and emulation are preferred methods, with emulation providing more accurate representations of real-world environments. In this paper, we introduce iContinuum, a novel emulation toolkit facilitating an intent-based platform for edge-to-cloud testing and experimentation. Leveraging Software-Defined Networking (SDN) and containerization, iContinuum enables experimentation and performance evaluation while aligning application requirements with actual performance. We present our detailed architecture, implementation, and evaluation of iContinuum, showcasing how our proposed toolkit bridges the gap between simulation and real-world deployment within compute continuum environments, and further demonstrate the effectiveness of Intent-Based Scheduling through a specific use case.

The rapid development of emerging vehicular edge computing (VEC) brings new opportunities and challenges for dynamic resource management. The increasing number of edge data centers, roadside units (RSUs), and network devices, how-ever, makes resource management a complex task in VEC. On the other hand, the exponential growth of service applications and end-users makes corresponding QoS hard to maintain. Intent-Based Networking (IBN), based on Software-Defined Networking, was introduced to provide the ability to automatically handle and manage the networking requirements of different applications. Motivated by the IBN concept, in this paper, we propose a novel approach to jointly orchestrate networking and computing resources based on user requirements. The proposed solution constantly monitors user requirements and dynamically re-configures the system to satisfy desired states of the application. We compared our proposed solution with the state-of-the-art networking embedding algorithms using real-world taxi GPS traces. Results show that our proposed method is significantly faster (up to 95%) and can improve resource utilization (up to 76%) and the acceptance ratio of computing and networking requests with various priorities (up to 71%). We also present a small-scale prototype of the proposed intent management framework to validate our solution.