Aims and scope

Grid computing is an emerging technology that enables large-scale resource sharing and coordinated problem solving within distributed, often loosely coordinated groups-what are sometimes termed "virtual organizations. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, Grid technologies promise to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Similar technologies are being adopted within industry, where they serve as important building blocks for emerging service provider infrastructures.

Even though the advantages of this technology for classes of applications have been acknowledged, research in a variety of disciplines, including not only multiple domains of computer science (networking, middleware, programming, algorithms) but also application disciplines themselves, as well as such areas as sociology and economics, is needed to broaden the applicability and scope of the current body of knowledge. Topics that fit within this theme include:

    Protocols, middleware, and services for security, discovery, sharing, management etc., of computing, storage, data and other resources - within dynamic, distributed communities
    Scaling issues in various dimensions: number of sites, number of users, number of resources, aggregate performance, and amount of data
    Peer-to-peer and internet computing
    Design environments, application development tools, languages and compilation techniques for GRID Computing
    Grid applications and system solutions in science, engineering and commerce
    Novel uses of Grid Computing concepts and technologies, for example in sensor nets and education
    Advanced collaboration technologies for collaborative work, information sharing and problem solving
    The implications of Grid technologies for emerging optical and wireless infrastructures
    The human and/or market dynamics that may influence resource sharing decisions and the choice of collaboration modalities within small and large communities

Special Issue on Distributed Collaborative Intelligence: Enabling Cloud-Edge Synergy in Next-Generation Networks
Día de Entrega: 2026-02-28

With the rapid advancement of information and communication technologies, a new wave of innovative applications has emerged, leading to exponential data growth and imposing substantial pressure on network bandwidth, energy efficiency, and response latency. While cloud computing provides abundant computational and storage resources, it inherently suffers from high latency and potential bandwidth bottlenecks, particularly in latency-sensitive scenarios. In contrast, edge computing mitigates these limitations by enabling data processing at the network edge, thereby reducing latency and alleviating bandwidth burdens. However, the limited resources of edge nodes present significant challenges in meeting the increasing computational demands. This dichotomy underscores the urgent need for distributed collaborative intelligence—a paradigm that seamlessly integrates cloud and edge resources. In such a framework, communication, computation, and caching resources are not only highly heterogeneous but also deeply interdependent. The joint optimization of these resources is critical to enhancing overall network performance and ensuring quality of service (QoS). Nevertheless, the dynamic nature of next-generation networks—characterized by fluctuating channel conditions, user mobility, and variable resource availability—adds complexity to collaborative computing. In this context, distributed intelligence has emerged as a key enabler, offering advanced adaptability and efficiency in resource management. Despite its transformative potential, distributed intelligence also introduces notable challenges in system architecture design, resource orchestration, and practical implementation. Overcoming these challenges is essential to fully realizing the benefits of intelligent collaborative computing in future networks.This special issue invites both theoretical advancements and practical solutions in Distributed Collaborative Intelligence for Cloud-Edge Synergy in Next-Generation Networks. Topics of interest include, but are not limited to: 1.Efficient architectures for cloud-edge synergy 2.Lightweight models for cloud-edge synergy 3.Multimodal data fusion techniques for cloud-edge synergy 4.Applications of large language models (LLMs) in cloud-edge synergy 5.Blockchain technologies for cloud-edge synergy 6.Resource optimization strategies for cloud-edge synergy 7.Real-time data processing for cloud-edge synergy 8.High-efficiency communication protocols for cloud-edge interaction 9.Distributed intelligence for dynamic and adaptive decision-making 10.Security and privacy-preserving mechanisms in collaborative cloud-edge environments 11.System implementation, testbed development, and real-world deployment of cloud-edge synergy