The IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid) is the premier forum for disseminating the latest advances in distributed systems, cloud computing, edge intelligence, and future computing paradigms. In 2026, for the first time, CCGrid will be proudly hosted in Sydney, Australia, bringing together researchers, practitioners, and industry leaders from across the globe to explore innovations shaping the future of distributed and cloud technologies. We invite original, high-quality papers that address fundamental research and emerging challenges across a broad spectrum of distributed systems and applications. Submissions may focus on theoretical foundations, system design, implementation, or real-world deployments. Topics of interest include but are not limited to: Technical Tracks Track 1 - Hardware Systems, Networks, and Architectures: Design and evaluation of scalable hardware, high-performance networks, system architectures, and accelerators. Track 2 - Software Systems, Applications, and Programming Models: Advances in middleware, operating systems, programming frameworks, and distributed application development. Track 3 - Sustainable Computing and Green Technologies: Energy-efficient computing, carbon-aware architectures and platforms, and sustainable design across cloud-to-edge systems. Track 4 - Performance Modeling, Analysis, and Optimization: Performance prediction, benchmarking, system tuning, and modeling for distributed, parallel, and cloud systems. Track 5 - Security, Privacy, and Trustworthy Systems: Innovative solutions for securing distributed infrastructures, ensuring data privacy, and enhancing cyber-resilience. Track 6 - Dependability, Reliability, and Fault Tolerance: Fault detection, self-healing systems, and techniques for robust and reliable distributed computing. Track 7 - Quantum Computing and the Future Compute Continuum Quantum algorithms, quantum software ecosystems, hybrid systems, and emerging paradigms at the edge of classical and quantum computing. Track 8 - Federated Learning, Edge AI, and Distributed Intelligence: Techniques and applications for distributed machine learning, edge intelligence, collaborative AI, and system-level distributed AI/ML innovations.