BUCS—A Bottom Up Caching Structure for Storage Servers

Ming Zhang and Dr. Ken Qing YangHPCL, Dept. of ECE

URI

Storage Volume

• Data storage plays an essential role in today’s fast-growing data-intensive network services.

• Online data storage doubles every 9 months • How much data are there?

1. Read (Text) 100 KB/hr, 25 GB/lifetime per person

2. Hear (Speech @ 10KB/s) 40 MB/hr, 10 TB/lifetime per person

3. See (TV @ .5 MB/s) 2 GB/hr, 500TB/lifetime per person

Storage Cost in an IT Dept.Storage Speed

A Server-to-Storage Bottleneck

Current Storage Servers: Motivations

• Data bus is becoming a bottleneck- 1 Gigabit NIC support 2 Gb/s (duplex) - 10Gb/s NIC is on the way

- A 10Gb/s TOE can achieve 7.9Gb/s - 6 SATA RAID0 can achieve >300MB/s - 1 PCI bus: 66 * 8 = 533 MB/s - PCI-X (1GB/s = 8Gb/s)

-PCI Express, InfiniBand

MotivationsEmbedded systems have become more powerful than ever

BUCS

• Functional marriage between HBA and NIC• Caching at controller level

• Data are placed at lower level caches • Replacing using LRU among

L1, L2, Disk • Only meta data are passed to bus and RAM

• Most reads and writes from network are done in lower level caches with minimum bus transactions

BUCS Controller Prototype Read Performance (Single Client)

Write Performance (Single Client)

Performance (Four Clients)

TPC-C Trace Results

Request Response TimeRandomly chosen 10K requests.

Conclusions

A New Cache Hierarchy Structure

Eliminate bus bottleneck Reduce Response time Increase system throughput by 3 times Compliance with Existing Standards Ready to be used

HELP---Hardware Environment for Low-overhead

Profiling

Ming Zhang and Ken Qing YangHPCL, Dept. of ECE, URI

Why Profiling?

•System profiling has been an important mechanism to observe system activities

•Profiling-based optimization has become a key technique in computer designs

•Continuous and online optimization is needed because of dynamic nature of computer systems

•Traditional approaches suffer from high overhead to already overloaded systems

HELP—Hardware Environment for Low-overhead Profiling

• Offload computing overheads from host processors to an embedded processor

• Continuous feedback loop model: 1. Low overhead profiling of system events 2. Parallel processing of raw data and

setting up new policies 3. Applying the new policies to host

HELP Architecture• Low cost, low power embedded processor• Expandable with secondary PCI slot • Interface with host via standard PCI slot

Adaptive Caching Policy

• IOMeter results of buffer cache with random write workloads

• HELP can help by adaptively setting cache policies

Potential Applications

• Performance: - Low overhead profiling - Adaptive pre-fetching and caching policies - Online code optimization and recompilation

• Availability: - Monitor system events and

report failures or faults • Security:

- Monitor abnormal system accesses, high risk events, intrusion detection

……

Conclusion

• HELP is a low cost, low power tool for system profiling and optimization

• Plug-and-Play device • Can be applied to any computer system with

PCI slots • “Offload” feature makes it superior to other

existing tools.

Computer Architecture Research at HPCL (www.ele.uri.edu/hpcl)

25%37%

50%75%

75%63%

50%25%

0%

20%

40%

60%

80%

100%

1996 1998 2001 2003

Storage Server

Storage cost as proportion of total IT spending as compared to server cost (Src Brocade)

BUCS – Bottom Up Caching Structure

Host RAM

CPU

HBA

Disk CtrlrNIC

Cache

Cache

Sys RAM

CPU

HBA

Disk CrlrNIC

Bus is in a critical path.

SystemBus

network

Systembus

network

HostHELP

12

3

Measured PerformanceRun PostMark and popular Linux profiling tool, LTT

The following table shows the measured time and overheadHELP reduces overhead of profiling to negligible

Read Write