SEDCL:Stanford Experimental Data Center Laboratory

Tackle Data Center Scaling Challenges

with

Stanford’s research depth and breadth

Data Center Scaling• A network of data centers and web services are the key

building blocks for future computing

• Factors contributing to data center scaling challenges

– Explosive growth of data with no locality of any kind

– Legal requirement to backup data in geographically-separated locations---big concern for financial industry

– Emergence of mobile and Cloud Computing

– Massive “interactive” web application

– Energy as a major new factor and constraint

– Increasing capex and opex pressures

• Continued innovations critical to sustain growth3

Stanford Research Themes• RAMCloud: main-memory based persistent storage

– Extremely low latency RPC

• Networking: – Large, high-bandwidth, low-latency network fabric– Scalable, error-free packet transport– Software defined data center networking with OpenFlow

• Servers and computing– Error and failure resilient design– Energy aware and energy proportional design– Virtualization and mobile VMs

4

Major research topics of SEDCL

• RAMCloud: Scalable DRAM-based Storage– Scalable nvRAM

– All data in DRAMs all the time

• Interconnect fabric– Bufferless networks: low-latency, high-bandwidth network

• Packet transport– Reliable delivery of packets: R2D2—L2.5

– Congestion management: QCN (IEEE 802.1Qau), ECN-HAT, DCTCP

– Programmable bandwidth partitioning for multi-tenanted DCs: AF-QCN

– Low-latency 10GBaseT

• Related projects– OpenFlow

– Energy aware and energy proportional design 5

Experimentation is Key to Success

• Many promising ideas and technologies– Will need iterative evaluation at scale with real applications

• Interactions of subsystems and mechanisms not clear– Experimentation best way to understand the interactions

• Difficult to experiment with internal mechanisms of a DC– No experimental facilities and that is a big barrier to innovations

• Ongoing efforts to enable experimentation – Facebook, Microsoft, NEC, Yahoo!, Google, Cisco, Intel, …

6

Overview of Research Projects

• RAMCloud

• Packet transport mechanisms – Reliable and reliable data delivery: R2D2—L2.5 – ECN-HAT, DCTCP: collaboration with Microsoft

• Data center switching fabric – Extremely low latency, low errors and congestion (bufferless)– High port density with very large bisection bandwidth

project just initiated

7

RAMCloud OverviewLead: John Ousterhout

• Storage for datacenters• 1000-10000 commodity

servers• 64 GB DRAM/server• All data always in RAM• Durable and available• Low-latency access:

5µs RPC• High throughput:

1M ops/sec/server

Application Servers

Storage Servers

Datacenter

RAMCloud Research Issues

• Data durability and availability

• Low latency RPC: 5 microseconds

– Need suitable network!

• Data model

• Concurrency/consistency model

• Data distribution, scaling

• Automated management

• Multi-tenancy

• Client-server functional distribution

Layer 2.5: Motivation and use cases

10

L2.5 Research Issues

• Determine simple signaling method

– Simplify (or get rid of) headers/tags for L2.5 encapsulation

• Develop and refine the basic algorithm for TCP

– In the kernel

– In hardware (NICs)

• Develop the algorithm for storage (FC, FCoE)

• Deploy in a large testbed

• Collaborate on standardization

DCTCP

• DCTCP: TCP for data centers

– Operates with really small buffers

– Optimized for low-latency

– Uses ECN marking

with Mohammad Alizadeh, and Greenberg et al at Microsoft

Influenced by ECN-HAT (with Abdul Kabbani)

DCTCP: Transport Optimized for Data Centers1. High throughput

– Creating multi-bit feedback at TCP sources

2. Low Latency (milliseconds matter)

– Small buffer occupancies due to early and aggressive ECN marking

3. Burst tolerance

– Sources react before packets are dropped

– Large buffer headroom for bursts

1. Use full info in stream of ECN marks

2. Adapt quickly and in proportion to level of congestion

Packet buffer

KMark Don’tMark

ECN Marks DCTCP TCP

1 0 1 1 1 1 0 1 1 1 Cut window by 40% Cut window by 50%

0 0 0 0 0 0 0 0 0 1 Cut window by 5% Cut window by 50%

Sauce

DCTCP

Reduces variabilityReduces queuing

IncastQueuebuildup

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Research Themes and Teams

Networking

Virtualization: Server and network

Energy Aware

M. RosenblumB. Prabhakar

P. LevisK. Kozyrakis

WEB App Framework

N. McKeown

B. PrabhakarG. Parulkar

J. Ousterhout

N. McKeown

Resilient Systems

M. RosenblumS. Mitra

N. McKeown

Storage J. OusterhoutM. RosenblumD. Mazieres