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2 Charles E. Leiserson's 60th Birthday Symposium 2

3 What to build? This question has spawned a cottage industry in the computer networking research community. Fat-tree [SIGCOMM 2008] VL2 [SIGCOMM 2009, CoNEXT 2013] DCell [SIGCOMM 2008] BCube [SIGCOMM 2009] Jellyfish [NSDI 2012] 3

4 Fat-tree SIGCOMM 2008 Isomorphic to butterfly network except at top level Bisection width n/2, oversubscription ratio 1 4

5 VL2 (SIGCOMM 2009, CoNEXT 2013) called a Clos network oversubscription ratio 1 (but 1Gbps links at leaves, 10Gbps elsewhere) 5

6 How to compare networks? Bisection width Diameter Maximum degree Degree sequence Area or volume Fault tolerance Cost 6

7 A Universal Approach Build a single network that is competitive, for any application, with any other network that can be built at the same cost. 7

8 Area-Universality Theorem [Leiserson, 1985]: There is a fattree network of area n that can emulate any other network that can be laid out in area n with slowdown O(log 3 n). Later improved to O(log n) slowdown area can be replaced by volume 8

9 Coarse Structure of Fat-Tree Network node channel leaves 9

10 Example of Fine Structure of a Fat-Tree switch link processors Butterfly Fat-Tree (Greenberg-Leiserson) 10

11 Recursive Decomposition of VLSI Layout Idea: match fat-tree channel capacity with maximum number of wires cut in layout. 4 n node channel 8 n leaves 11

12 Layout of Area-Universal Fat-Tree Top layer capacity: 4 n Next later capacity: 8 n but at half wire length Equal area at each layer! Message crossing link in emulated network fat-tree sends message across a pair of leaves 12

13 The Universal Approach for Data Center Design Allocate the same amount of money to each level in a three- or four-level fat-tree network. Levels: within a rack, between racks in a row, between rows, etc. Rule of thumb: build best network at lowest level; match cost at higher levels 13

14 Caveats Assumes that performance is proportional to cost (e.g., for one-third the cost, can buy one-third the capacity) Assumes that it is physically possible to spend the same amount at each level Assumes that bandwidth bottlenecks, not latency, limit performance. 14

15 Trends Looked at large-scale network deployed by a major provider of on-line services. Five years ago, money spent on layers (bottom up) was 6:2:1. In 2014, the ratio was 2:2:1 we re building nearly cost-universal networks today! 15

16

17 The Need Addressed by CloudLab How to optimize software (e.g., to take superfluous latency out)? How best to design and run applications? Storage, networking, virtualization, To investigate these questions, we need: Flexible, scalable scientific infrastructure That enables exploration of fundamental science in the cloud To ensure repeatability of research

18 The CloudLab Vision A meta-cloud for building clouds Build your own cloud on our hardware resources Agnostic to specific cloud software Run existing cloud software stacks (like OpenStack, Hadoop, etc.) or new ones built from the ground up Control and visibility all the way to the bare metal Sliceable for multiple, isolated experiments at once With CloudLab, it will be as easy to get a cloud tomorrow as it is to get a VM today

19 What Is CloudLab? Slice A Geo-Distributed Storage Research Slice B Stock OpenStack Supports transformative cloud research Built on Emulab and GENI Control to the bare metal Diverse, distributed resources Repeatable and scientific Slice C Slice D Virtualization and Isolation Research Allocation and Scheduling Research for Cyber-Physical Systems Utah Wisconsin Clemson GENI CC-NIE, Internet2 AL2S, Regionals

20 CloudLab s Hardware One facility, one account, three locations About 5,000 cores each (15,000 total) 8-16 cores per node Baseline: 4GB RAM / core Latest virtualization hardware TOR / Core switching design 10 Gb to nodes, SDN 100 Gb to Internet2 AL2S Partnerships with multiple vendors Wisconsin Clemson Utah Storage and net. Per node: 128 GB RAM 2x1TB Disk 400 GB SSD Clos topology Cisco High-memory 16 GB RAM / core 16 cores / node Bulk block store Net. up to 40Gb High capacity Dell Power-efficient ARM64 / x86 Power monitors Flash on ARMs Disk on x86 Very dense HP

21 Wisconsin/Cisco Nexus 3172PQ 8X10G Nexus 3132Q 40G 40G Nexus 3172PQ 20X12 servers 2X10G

22 Compute and storage 90X Cisco 220 M4 10X Cisco 240 M4 2X 8 2.4GHz 128GB RAM 1X 480GB SSD 2X 1.2 TB HDD 1X 1TB HDD 12X 3TB HDD (donated by Seagate) Over the next year: 140 additional servers; Limited number of accelerators, e.g., FPGAs, GPUs (planned)

23 Networking Nexus 3132q Nexus 3172pq OF 1.0 (working with Cisco on OF 1.3 support) Monitoring of instantaneous queue lengths Fine-grained tracing of control plane actions Support for multiple virtual router instances per router Support for many routing protocols

24 Experiments supported Large number of nodes/cores, and bare-metal control over nodes/switches, for sophisticated network/memory/storage research Network I/O performance (e.g., Presto), intra-cloud routing (e.g., Conga) and transport (e.g., DCTCP) Network virtualization (e.g., CloudNaaS, OpenNF) In-memory big data frameworks (e.g., Spark/Shark, Graphene) Cloud-scale resource management and scheduling (e.g., Mesos; Tetris) New models for Cloud storage (e.g., tiered; flat storage; IOFlow, split-level scheduling) New architectures (e.g., RAMCloud for storage)

25 Technology Foundations Built on Emulab and GENI ( ProtoGENI ) Provisions, then gets out of the way Run-time services are optional Controllable through a web interface and GENI APIs Scientific instrument for repeatable research Physical isolation for most resources Profiles capture everything needed for experiments Software, data, and hardware details Can be shared and published (eg. in papers)

27

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