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Towards a dynamic multi-cloud computing universe

Towards a dynamic multi-cloud computing universe. Divy Agrawal & Amr El Abbadi UC Santa Barbara amr@cs.ucsb.edu. Origins of Cloud. Single data center Focus : Virtualization technologies Reliable and robust infrastructure What if the data center fails ?. Source:

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Towards a dynamic multi-cloud computing universe

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  1. Towards a dynamic multi-cloud computing universe DivyAgrawal & Amr El Abbadi UC Santa Barbara amr@cs.ucsb.edu

  2. Origins of Cloud • Single data center • Focus: • Virtualization technologies • Reliable and robust infrastructure • What if the data center fails? Source: http://www.logichp.com/2010/04/21/hps-houston-data-center-cisco-free-twice-as-efficient/

  3. Cloud Evolution • Multiple static data centers • Geo-replication • Fault-tolerance • Lower latency across regions • What are we missing? Source: http://www.clusterdb.com/wp-content/uploads/2009/08/multi_master_replication2.jpg

  4. Cloud Evolution • Too many providers • Potential for vendor lock-in • Lack of cloud standards • Focus only on large “cores” • Missing out on resources at the edge Source: http://fennelway.com/img/cloud-lock.jpg Source: http://www.valuecdn.com/images/cdn-globe.jpg

  5. CloudRevolution • Build a cloud ecosystem: • Lots of resources at the edge • Content Delivery Networks • Huge potential with transient resources • Wall street companies • Large infrastructures with diurnal usage • Inter-operability between cloud providers

  6. Dynamic Clouds (diurnal behavior) Transition through time epochs 1:00 am 9:00 am Nucleus Nucleus

  7. Research Vision • Design Principles: • Separate system and application data • Decouple control from storage of date • A collection of static cloud “nucleus” • Heart and mind: coordinate and synchronize. • A collection of dynamic cloud “cores” • Augment system capacity.

  8. Technical Challenges • A uniform namespace for multi cloud cores. • Efficient integration of surplus capacity. • Effective load and data migration. • Scalable monitoring and system modeling.

  9. Agility and Elasticity • Managing Churn • Predictable; checkpoints. • Load balancing and elasticity • Migration; replication • Data Placement • Need models to account for churn and load changes.

  10. Building a Dynamic Cloud • System Monitoring and Control • System stats, failures, data placement, etc • Metadata Management (system state) • Strong consistency within a single core • Casual consistency across cores (dist dictionary)? • Data Access Control • Inexpensive tenant migration • Data Storage (fault tolerance) • Cross core replication—timeline consistency?

  11. More Issues • Standards • Differential pricing: varying demand and availability • SLAs versus Operating cost • Privacy and security • Supporting diverse applications and workloads

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