CURVE Roadmap CURVE Community You can take awayDesign objectives App scenario & requires ● Database scenario ● Elastic block Storage for KVM / Kubernetes / iSCSI Design objectives ● Thin provisioning take awayPerformance in database scenario Solution architecture ● CURVE provides underlying distributed shared storage services ● Polardb for PostgreSQL provides database services Solution features seperation between storage and computation ● The call method is the same as that of the standalone database Performance test vs CEPH ● BenchmarkSQL transactions per minute increased by 39% ● In PGBench

designate particularly influential models within the AI/machine learning ecosystem. Epoch maintains a database of 900 AI models released since the 1950s, selecting entries based on criteria such as state-of-the-art mobile app ordering, point of sale, kitchen and delivery optimization, menu management, inventory and labor management, and team member tools. - Yum! Press Release, 2/25 Number of Restaurants Yum Restaurants Using at Least One Byte by Yum! Product Byte is Yum! Brands' AI-powered restaurant management platform designed to optimize store operations by automating repetitive tasks like inventory tracking

an Agile Approach Agile represents a radical shift from industrial age processes to a modern management and development approach suited to the digital age. Agile practices help to make progress and information. However, Agile is not a panacea: it does not promise to solve all IT and program management problems, and may not be appropriate for use in all cases. Even successful adoption of Agile practices trust that spans the decision authority, developers, testing organization, acquirers, program management, and users is critical to Agile delivery. This demands a technically competent government team

System (CurveFS) • CurveFS: a high performance cloud native file systemUse Cases • Container • Database • Data apps(middleware/bigdata/ai) • Data backupContainer • Aggregates underlying storage container-native storageDatabase • Database services orchestrated in the cloud • Curve can backup / sync data to slave cloud • When master cloud failure happens, Database service can move to the slave long NETWORK LATENCY 50MS 1s frequently 7s recentlyStorage Engine Comparison (vs. Ceph) META MANAGEMENT CURVE CHUNK SERVER BLUESTORE META Precreate Chunk File Pool on ext4 RocksDB META OVERHEAD

Development  Operations (Operational Waterfall)  Infrastructure Ops  Product Ops  Product Management  Every technology under the sun  Solaris, Windows, Linux  Apache, IIS, TCServer, etc.  homogenization and assimilation – no snowflakes  Deployment methodologies, automation, monitoring, and management tested continuously. Steve Barr steve.barr@csgi.com @srbarr1 Overall Quality improvements, “it” DEG and 3rd Party Application Installations  Route Adds – requires heightened security access  Database Data Script Execution  Load Balancer Node Disablement  OS and Security Patching  Requesting

supports standard SQL queries to simplify data access and management, ensuring that teams can leverage existing skills and tools to interact with the database. Seamless integration: CrateDB easily integrates

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 17 Modules 218 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 17 Modules 219 17.1 Namespace management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 17.2 Submodules and relative Julia Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408 31.5 Memory Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409 31.6 Working with Arrays inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 35.4 Memory management and arrays . . . . . . . . . . . . . . . . . . . . . . . . . . . 474 35.5 Execution latency, package