Conference Information

CCGRID 2027: International Symposium on Cluster, Cloud and Grid Computing

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Submission Date:
2026-11-24 Due in 26 days
Notification Date:
2027-02-01
Conference Date:
2027-05-24
Location:
Dallas-Fort Worth, Texas, USA
Years:
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CCF: C   ICORE: B   QUALIS: A1   Viewed: 343341   Tracked: 160   Attend: 43
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CCGRID
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Conference Partner Index (CP-I)

90.5 / 100
Ranked #68 of 5,763 conferences · Top 2%

#19 of 341 in Systems & Architecture

Academic recognition (35%)
100
Submission selectivity (20%)
85
Editions held (20%)
90
Community attention (10%)
77
Public record completeness (15%)
85

Inputs used: Listed as CCF C, ICORE B, QUALIS A1 · Acceptance rate: 24.1% (mean of 5 editions on file) · Editions on record: 27 · Researchers following it here: 160 · Researchers who opened this page in the past 24 months: 40

Missing from the public record: Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-10-29

Call For Papers

CCGRID 2027 (International Symposium on Cluster, Cloud and Grid Computing) is a CCF C / ICORE B / QUALIS A1 conference held in Dallas-Fort Worth, Texas, USA on 2027-05-24. The paper submission deadline is 2026-11-24. Acceptance notifications are sent on 2027-02-01.

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. We particularly encourage contributions stemming from industry efforts and academia-industry collaborations, including practical experiences, deployed systems, and applied research with tangible impact. Technical Tracks Track 1 – Hardware Systems, Architectures, and Quantum Future Compute Platforms: Innovations in computing and networking hardware, system architectures, and emerging compute paradigms. Topics include accelerators, heterogeneous systems, reconfigurable architectures, and quantum or neuromorphic computing platforms across cloud, edge, and HPC environments. This track also covers advanced networking technologies such as software-defined networking (SDN) and high-performance interconnects for distributed systems. Track 2 – Software Systems, Applications, and Programming Models: Advances in middleware, operating systems, programming frameworks, cloud-native systems, and distributed application development. Topics include serverless computing, microservices, container orchestration, and resource management and scheduling. This track also welcomes contributions on data-intensive systems, distributed storage architectures, and scalable data management frameworks. Track 3 – Sustainable Computing and Green Technologies: Energy-efficient computing, carbon-aware platforms, carbon-intelligent scheduling, and sustainable systems design across the cloud-to-edge continuum. Topics also include cost-aware computing, economic modeling, and optimization of resource usage to support sustainability goals. Track 4 – Performance Modeling, Analysis, and Optimization: Techniques for performance engineering, benchmarking, system tuning, and modeling of distributed, parallel, and cloud-based systems. Topics include optimization of scientific workflows, reproducibility-aware benchmarking methodologies, and pipeline performance engineering. Track 5 – Distributed Systems Security, Privacy, and Reliability: Innovative solutions for securing distributed infrastructures, enabling confidential computing, preserving data privacy, and enhancing cyber-resilience. Topics include trustworthy systems, intrusion detection, self-healing mechanisms, and approaches for building robust, fault-tolerant, and reliable computing platforms. Track 6 – Systems for AI/ML and Distributed Intelligence: Techniques and systems for scalable and efficient execution of AI/ML workloads across distributed platforms, addressing challenges in foundation models, Large Language Models (LLMs), model serving, and MLOps, including the compute continuum, osmotic computing, and liquid computing. Topics include edge intelligence, collaborative and federated learning, and system-level support for distributed AI.
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Acceptance Ratio

Average acceptance rate: 27.5% over 16 years (2001–2021).

YearSubmittedAcceptedAccepted(%)
20212306026.1%
20152686925.7%
20142835419.1%
20132575722.2%
20123028327.5%
20111895529.1%
20102195123.3%
20092715721%
20082016532.3%
20072187233%
20062576123.7%
20052357632.3%
20041383827.5%
20031143934.2%
20021082725%
20011264838.1%

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