sedcl: stanford experimental data center laboratory
Post on 20-Dec-2015
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SEDCL:Stanford Experimental Data Center Laboratory
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Tackle Data Center Scaling Challenges
with
Stanford’s research depth and breadth
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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
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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
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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
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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, …
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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
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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
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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
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Layer 2.5: Motivation and use cases
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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
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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)
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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