chronolog

revolutionizing how we store and process activity data.

modern applications—from cutting-edge scientific instruments to ubiquitous iot devices—generate massive amounts of activity data at unprecedented rates. managing, storing, and processing this data efficiently is a significant challenge. chronolog is an innovative, open-source solution that transforms how we handle this deluge of data, making storage and retrieval faster, more efficient, and more scalable than ever before.

what is chronolog?

chronolog is a distributed, tiered, shared log store that leverages physical time as a natural ordering mechanism for data. by using time itself to organize data, chronolog eliminates the need for complex synchronization, enabling high concurrency and efficient data retrieval. it automatically manages data across multiple storage tiers, optimizing for both performance and capacity.

✨ what makes chronolog special?

• ⏰ time-based organization

like a well-organized diary, chronolog uses physical time to naturally order data. this means:

  • no expensive synchronization needed
  • natural, intuitive data ordering
  • efficient storage and retrieval operations

• 🔄 smart storage management

think of it as an intelligent librarian that:

  • 🚀 keeps recent books (data) on the front desk (memory)
  • 📚 moves older volumes to easily accessible shelves (ssds)
  • 📦 archives historical records in the basement (hdds)
  • auto-tiering happens seamlessly in the background

• 🌐 high concurrency and scalability

like a busy library that:

  • 📝 allows multiple people to write in different journals
  • 📖 enables countless others to read simultaneously
  • 🔄 scales up or out automatically as needed
  • 🎯 no manual intervention required

• 🔍 efficient data retrieval

imagine finding exactly what you need:

  • ⚡ lightning-fast range queries
  • 📊 perfect for time-series analysis
  • 🎯 precise temporal data access
  • 📈 optimized partial log processing

technical architecture

chronolog’s architecture consists of three main components working seamlessly:

  • chronovisor: the central control unit that manages system operations, ensuring smooth coordination among components.
  • chronokeeper: handles recent data with lightning-fast access, maintaining high performance for the most frequently accessed data.
  • chronostore: manages long-term storage across multiple tiers, ensuring data durability and optimal storage utilization.

chronolog’s architecture featuring chronovisor, chronokeeper, and tiered chronostore components.

key innovations

  1. physical time as global truth: utilizing physical time for data ordering eliminates synchronization overhead.
  2. 3d log distribution: scales both horizontally and vertically, distributing data across nodes and storage tiers.
  3. synchronization-free data distribution: simplifies the system design and improves performance.
  4. elastic storage with auto-tiering: automatically adjusts storage allocation based on data age and access patterns.
  5. native plugins for high-level interfaces: integrates seamlessly with various applications and services.

impact and applications

chronolog serves as a foundation for a wide range of data-intensive applications:

  • scientific research: supporting data collection and analysis from telescopes, particle accelerators, and other instruments.
  • iot and edge computing: managing massive streams of sensor data from distributed devices.
  • financial systems: enabling fast processing of time-sensitive trading data and time-series analysis.
  • system monitoring: tracking and analyzing system performance in real-time for telemetry and diagnostics.
  • performance analysis tools: providing detailed logs for debugging and optimization.
  • nosql databases and querying systems: enhancing data retrieval capabilities with efficient range queries.
  • icecube neutrino observatory: chronolog captures monitoring information from the icecube detector, aiding in the study of neutrinos and cosmic events.
  • cybergis: supports spatial data synthesis and gis analytics, enabling complex geospatial computations and visualizations.
  • dark energy science collaboration: assists in monitoring large-scale scientific workflows, contributing to our understanding of dark energy and the universe’s expansion.
  • financial computing: processes market transaction data in real-time, supporting high-frequency trading and financial analysis.

performance highlights

performance comparison with existing solutions.

chronolog outperforms existing log storage solutions, offering higher throughput and lower latency. its innovative architecture and optimizations ensure that applications can handle increasing data volumes without sacrificing performance.

looking forward

chronolog continues to evolve with exciting developments:

  • advanced querying capabilities: enhancing data retrieval and analysis features.
  • machine learning support: optimizing for workloads involving ai and machine learning.
  • improved automation: incorporating self-optimization techniques for better resource management.
  • expanded integration: developing plugins and interfaces for broader application support.

community and development

chronolog is an open-source project under the bsd license, welcoming contributions from academic and industrial researchers alike. we adhere to best practices in software development, ensuring a robust and reliable platform.

get involved

acknowledgements 🙏

chronolog’s development is supported by the national science foundation (nsf) under grant cssi-2104013. i extend my gratitude to my collaborators at the illinois institute of technology and the university of chicago.

join us!

are you passionate about distributed systems, data storage, or big data analytics? we’re looking for talented individuals to join our team. whether you’re a student seeking research opportunities, a professional exploring new challenges, or a collaborator interested in integrating chronolog into your applications, we’d love to hear from you.


Interested in learning more about ChronoLog or discussing potential collaborations? Don’t hesitate to reach out!


Related Publications

2024

  1. Jaime Cernuda, Jie Ye, Anthony Kougkas, and Xian-He Sun
    In Proceedings of the 53rd International Conference on Parallel Processing , Aug 2024

2022

  1. Keith Bateman, Neeraj Rajesh, Jaime Cernuda Garcia, Luke Logan, Jie Ye, Stephen Herbein, Anthony Kougkas, and Xian-He Sun
    In Proceedings of the 29th International Conference on High Performance Computing, Data, and Analytics , Dec 2022

2021

  1. Neeraj Rajesh, Hariharan Devarajan, Jaime Cernuda Garcia, Keith Bateman, Luke Logan, Jie Ye, Anthony Kougkas, and Xian-He Sun
    In Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing , Jun 2021

2020

  1. Anthony Kougkas, Hariharan Devarajan, Keith Bateman, Jaime Cernuda, Neeraj Rajesh, and Xian-He Sun
    In Proceedings of the 36th International Conference on Massive Storage Systems and Technology , Oct 2020
  2. Hariharan Devarajan, Anthony Kougkas, Keith Bateman, and Xian-He Sun
    In Proceedings of the International Conference on Cluster Computing , Sep 2020

2019

  1. Hariharan Devarajan, Anthony Kougkas, and Xian-He Sun
    In Proceedings of the 19th International Symposium on Cluster, Cloud and Grid Computing , May 2019