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Enhancing Cloud Resource Management with REEF for Unified Workflows and Real-Time Processing

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Discover REEF, a powerful framework by Microsoft CISL designed to improve resource management in cloud environments. With true multi-tenancy support, it offers unified real-time and batch processing workflows, enabling scientists and developers to utilize a single cluster for various sophisticated applications. Key features include fault tolerance, elasticity, checkpointing, and support for SQL and MapReduce. REEF breaks down silos in data management, allowing seamless composition of scalable data pipelines while optimizing resource allocation and reducing redundancy through shared libraries.

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Enhancing Cloud Resource Management with REEF for Unified Workflows and Real-Time Processing

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  1. REEFRetainable Evaluator Execution Framework Rusty Sears Microsoft Cloud and Information Services Lab (CISL)

  2. Resource Managers True multi-tenancy… Unified realtime-batch workflows In-situ processing Utilization: one cluster for scientists and production …but, only for sophisticated apps

  3. Resource Managers True multi-tenancy… Unified realtime-batch workflows In-situ processing Utilization: one cluster for scientists and production …but, only for sophisticated apps Fault tolerance Pre-emption Elasticity

  4. Running SQL / MapReduce Checkpointing Fault tolerance Elasticity

  5. Machine learning Checkpointing Fault tolerance Elasticity Iterative computations

  6. Graph processing Checkpointing Fault tolerance Elasticity Iterative computations Low latency communication

  7. Problems with silos Tedious: users write code to dump + load data at each step Slow: Data unnecessarily written to disk, read back (and re-parsed) at each step Hard to build: Each duplicates the same mechanisms under the hood

  8. REEF’s goals Support YARN versions of new (and existing) scalable data pipelines. Allow them to be transparently composed. Move redundant tooling and plumbing into shared libraries.

  9. + 3 REEF control flow Yarn ( ) handles resource management (security, quotas, priorities) Per-job Drivers ( ) request resources, coordinate computations, and handle faults, preemption, etc… REEF Evaluators ( ) hold hardware resources, allowing multiple Activities ( , , , , , , etc…) to use the same cached state.

  10. M M Retaining Evaluators R R R R Handover of pre-partitioned and parsed data between frameworks Iterative computation Interactive queries $…

  11. Wake: Events + I/O Thread per connection / file doesn’t scale Provide static subset of Rx → static checking of event flows → aggressive JVM event inlining Latency, throughput profiler

  12. A B A B REEF data plane B Fault-tolerant async communication Group communication / shuffle Low-latency communication Storage, checkpointing, preemption

  13. Tang: Config Manager Configuring distributed systems is hard So is reasoning about event flows Tang performs static and dynamic checks to help ease the pain

  14. cmd = ‘ls’ Command = ‘ls’ ShellActivity Tang: Config Manager Error: container-4872364523847-02.stderr: NullPointerExceptionat: java…eval():1234 ShellActivity.helper():546 ShellActivity.onNext():789 YarnEvaluator.onNext():12 Evaluator Activity YarnEvaluator Evaluator Configuring distributed systems is hard So is reasoning about event flows Tang performs static and dynamic checks to help ease the pain Error: Unknown parameter “Command” Missing required parameter “cmd” Error: Required instanceof Evaluator Got ShellActivity #!

  15. Open source release soon! Apache 2.0 licensed Java support first, C# to follow Interested in early access? Ping us!

  16. sears@microsoft.com

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