The recent developments in the field of microservices and edge computing highlight a significant shift towards enhancing efficiency, scalability, and reliability in distributed systems. Innovations are particularly focused on optimizing resource management, improving service delivery, and ensuring quality of service (QoS) in complex network environments. A notable trend is the adoption of decentralized architectures and advanced machine learning techniques to address the challenges posed by the dynamic and distributed nature of modern applications. These approaches aim to improve the scalability of service function chaining, optimize microservice deployment in edge networks, and enhance the performance of high-performance computing applications on edge devices. Additionally, there is a growing emphasis on developing adaptive and heuristic-based solutions for scheduling and resource allocation, which are crucial for meeting the stringent requirements of latency-sensitive applications, such as augmented reality and assistive technologies for visually impaired individuals.

Noteworthy Papers

• A Microservice Graph Generator with Production Characteristics: Introduces a Service Dependency Graph Generator that significantly improves resource efficiency by up to 44.8% while ensuring QoS.
• Scalability Assurance in SFC provisioning via Distributed Design for Deep Reinforcement Learning: Proposes a distributed SFC provisioning framework that enhances the acceptance ratio of service requests by up to 60% compared to centralized approaches.
• Contention-Aware Microservice Deployment in Collaborative Mobile Edge Networks: Develops the CAMD algorithm, which optimizes microservice deployment in MEC to balance rapid response and processing efficiency.
• Adaptive Heuristics for Scheduling DNN Inferencing on Edge and Cloud for Personalized UAV Fleets: Introduces DEMS-A and GEMS strategies that significantly improve task completion rates and QoS utility for assistive drone applications.
• DMSA: A Decentralized Microservice Architecture for Edge Networks: Presents a decentralized microservice architecture that improves service response delay and execution success rate by approximately 60% to 75% and 10% to 15%, respectively.
• HPC Application Parameter Autotuning on Edge Devices: A Bandit Learning Approach: Introduces LASP, a lightweight autotuning strategy that significantly improves the performance of HPC applications on edge devices.
• A Proof of Concept Resource Management Scheme for Augmented Reality Applications in 5G Systems: Demonstrates a resource management scheme that reduces bandwidth usage and power consumption while delivering high QoS for augmented reality applications.