Webinar: StorPool and WHIR - better storage, better business
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The document presents a solution called StorPool to address problems with today's storage solutions. StorPool provides distributed, software-defined storage using standard servers and drives in a flat network. It offers horizontal scalability, self-healing capabilities, and survives partial failures gracefully. StorPool storage nodes can be combined with compute servers for converged infrastructure, reducing costs versus separate storage and compute solutions. A demo then shows StorPool's basic functions like volume management, snapshots, placement groups, performance, and fault tolerance.
Webinar: StorPool and WHIR - better storage, better business
- 1. Better Storage, Better Business
- 2. Presented by: Boyan Krosnov Chief of Product & co-founder linkedin.com/in/krosnov - Informatics – IOI, BOI - Networks – ISPs, CCIE #8701 (2002) - Lived in Iceland, Ireland, UK and Malaysia - Doing IT infrastructure since 1999 - Distributed Storage since 2011 - StorPool
- 3. Boyan Ivanov CEO & co-founder linkedin.com/in/boyanivanov - tried programming at age 10… - started his first venture at same age… - experience in both the corporate and entrepreneurship worlds - has developed two “formal” start-ups prior StorPool
- 4. Agenda 1. The Storage Problem 2. Why is it so hard? 3. Today’s solutions 4. Tomorrow’s solution 5. StorPool Demo 6. Q & A
- 5. Defining the problem • Primary block-layer storage is important – Compute, Storage, Networking • Storage is hard – In networking – lose a packet – everything’s OK, no need to panic – In storage – lose the data – costly and slow recovery from backup – In compute – lose a CPU – just move the user to another server
- 6. Defining the problem • Storage is expensive • Storage breaks badly and looses data • Storage is slow ... Or, in reality, all of the above
- 7. Today’s storage solutions • Local storage – Workaround for problems with shared storage solutions – Performance good, but when you lose a node, you lose the data – Lack of key storage features – thin provisioning, snapshots, etc. – Not an ideal solution, some have learned to live with it
- 8. Today’s storage solutions • “Marketing-defined” storage solutions – We call it “Software-defined”, because it’s hip. – Would you like to buy our boxes? – e.g. EMC, NetApp, HP, IBM • Centralized storage solutions (both HW and SW) – Many compute servers, one storage box – Bottleneck, SPOF, inflexible, hard to scale – e.g. SANs, ZFS, Nexenta
- 9. Today’s storage solutions • Slow distributed storage solutions – Distributed, scales, but inefficient – Takes way too much CPU and RAM – Does not deliver promised performance – e.g. Ceph
- 10. So how do we make this better? (And not just fix the problems. Really make it way better.)
- 11. We didn’t know it is impossible. So we just built it. “ But, …. but this is impossible! ” The CTO of a major infrastructure software vendor (to remain unnamed)
- 12. Storage system wishlist • Horizontal scalability in performance and capacity • Based on standard servers, drives and a standard network • Online expansion, online changes, in-service upgrades • Automatic self-healing • Simple architecture: • No dedicated metadata servers • No active/passive pairs of servers • No requirement for an additional layer of shared storage • Ideally, just servers on a flat LAN
- 13. Storage system wishlist (cont.) • Snapshots and clones • IO Limits • Survive partial failures gracefully! • End-to-end data integrity • Brutal efficiency and performance – get the most of your investment
- 14. Storage Network Host Guest Guest Guest Host Guest Guest Guest Host Guest Guest Guest Storage Node Storage Node Storage Node Storage Node Storage Node Storage Cluster Distributed storage - overview
- 15. Compute Storage Distributed, software-defined storage Network
- 16. Storage Spock is not impressed Compute Distributed, software-defined storage
- 17. Storage + Compute in the same servers ½ the servers ½ the network ports lower footprint & utilities Only one type of server to manage: Big win! Converged Storage+Compute
- 18. The economics • vs. Converged Storage+Compute nodes • 1x or 2x 10GE network ports (shared with compute) • 1RU space typical. Medium power density. • Cost per node: $6k-10k, of which, ~ $1k for the storage system • Storage system takes a small and fixed amount (10-15%) of CPU and RAM • Commodity stand-alone storage servers • 1x or 2x 10GE network ports • 2RU space typical. Low power density. • Separate compute servers with high power density • Cost per node: $2k - 3k ½ the servers to manage, ½ the network ports, roughly ½ the datacenter bill
- 19. The economics (cont.) • Large pool of capacity • Reduced waste from ~50% to 10-20% • Large pool of performance • Allow each user to peak, when they need to • Users peak at different times • Higher compute density • IOPS is no longer the bottleneck • More VMs per server • More cost-efficient CPUs (8- and 10-core) • Higher performance – happier customers, more customers
- 20. Demo • Basics • Cluster • Volumes – creating, attaching, resizing • Snapshots • Placement groups – hard drive pool, hybrid pool • Performance • Fault tolerance • Self-Healing
Editor's Notes
- #2: Good afternoon, My name is Boyan, I am co-founder and CEO at StorPool. We have a storage technology that radically improves how data storage is done. It makes it several times more reliable, flexible and efficient.