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The Hidden Face of Cloud Data Management

In this study, Magdalena Balazinska from the University of Washington explores three key challenges in cloud data management: fast data management, efficient (multitenant) data management, and transformative data management. The research problems are well-defined but require further exploration and solutions. The study aims to incentivize and recognize the translation of real-world use-cases into research problems, provide datasets for testing solutions, and evaluate current solutions' performance.

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The Hidden Face of Cloud Data Management

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  1. The Hidden Face of • Cloud Data Management Magdalena Balazinska Computer Science & Engineering -- U of Washington http://www.cs.washington.edu/people/faculty/magda Magdalena Balazinska - University of Washington

  2. Three Types of Challenges Problem 1: Fast Pb 2: Efficient (multitenant) Data Management in the Cloud Pb 3: Transformative (for developers and users) Well defined research problems Pretty well defined research problems Poorly defined research problems Magdalena Balazinska - University of Washington

  3. Pb 1: Fast Cloud Data Management • Well-known challenges around all V’s • Volume, velocity, variety, etc. • But: Need to incentivize and recognize translation of real-world use-cases into research problems • Description of application • Dataset(s) to use for testing solutions • List of current solutions and their performance • It’s too costly when everyone does it over and over again Magdalena Balazinska - University of Washington

  4. Pb2: Efficient Cloud Data Management • Multitenancy problem for Cloud providers: • OLTP: Well defined problems (predict, pack, and migrate) • OLAP: Less well defined problems • And what about service developers • Build services on top of existing Cloud resources • These services may have tenants of their own • Challenges • Resource management in hierarchy of multitenant systems • Performance, elasticity, costs, SLAs Magdalena Balazinska - University of Washington

  5. Pb3: Transformative Cloud Data Mgmt • New interface to data management • How should Cloud SLAs look like? How to avoid surprises? • How to reason about Cloud costs and capabilities? • How to do all types of processing at the same time? • Graphs, text, streams, etc. • Interactive, batch, visual • New levels of sharing across users and tenants • How to manage sharing of data and computation? • Data itself may have a price and/or a license agreement • How to support data discovery? quality assessment? Magdalena Balazinska - University of Washington

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