BUCS

1
Computer Architecture Research at HPCL
(www.ele.uri.edu/hpcl)
BUCSA Bottom Up Caching Structure for Storage
Servers Ming Zhang and Dr. Ken Qing YangHPCL,
Dept. of ECEURI

• Storage Volume
• Data storage plays an essential role in todays
  
• fast-growing data-intensive network services.
  
• Online data storage doubles every 9 months
• How much data are there?
  
• Read (Text)
  
• 100 KB/hr, 25 GB/lifetime per person
• 2. Hear (Speech _at_ 10KB/s)
• 40 MB/hr, 10 TB/lifetime per person
• 3. See (TV _at_ .5 MB/s)
  
• 2 GB/hr, 500TB/lifetime per person

Storage Cost in an IT Dept.
Storage Speed A Server-to-Storage
Bottleneck
Storage cost as proportion of total IT spending
as compared to server cost (Src Brocade)
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 gt300MB/s
• - 1 PCI bus 66 8 533 MB/s
  
• - PCI-X (1GB/s 8Gb/s)
  
• PCI Express, InfiniBand

Motivations Embedded systems have become more
powerful than ever
BUCS Bottom Up Caching Structure
System Bus
System bus
network
network

• 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 Time Randomly 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 Yang HPCL,
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.

Measured Performance Run PostMark and popular
Linux profiling tool, LTT The following table
shows the measured time and overhead HELP reduces
overhead of profiling to negligible