1. Qingbo Zhu, Asim Shankar and Yuanyuan Zhou
PB-LRU: A Self-Tuning Power Aware Storage Cache Replacement Algorithm for Conserving Disk Energy
Qingbo Zhu, Asim Shankar and Yuanyuan Zhou
Presented: Hang Zhao Chiu Tan
12/31/2018

2. PB-LRU: Partition-Based LRU
Storage is a major energy consumer, 27% of power budget in a data center.
PB-LRU is a power aware, on-line cache management algorithm.
PB-LRU dynamically partitions cache at run time for energy optimal cache size per disk.
Practical algorithm that dynamically adapts to workload changes with little tuning.
12/31/2018

3. Outline Motivation Background Why need PB-LRU? Main Idea
Energy estimation at Run Time
Solving MCKP
Evaluation & Simulation
Conclusion
12/31/2018

4. Motivation Why is power conservation important?
Data centers are an important component of the Internet infrastructure.
Power needs for a data center are increasing at 25% a year, with storage taking up 27%.
How to reduce power in storage?
Simple. Spin down disk when not in use.
12/31/2018

5. Motivation (II)
But …
Performance and energy penalty when disk moving from low to high mode.
Data center volume is high. Idle periods small. Makes spinning up and down impractical.
Solution: Multi-speed disk architecture.
PB-LRU targets multi-speed disk.
12/31/2018

6. Background
Break-even time: Minimum length of idle time needed justify spinning up/down.
Oracle DPM: Knows length of next idle period. Uses this to regulate power modes.
Practical DPM: Use thresholds to regulate powering up or down.
12/31/2018

7. Why need PB-LRU? Earlier work: PA-LRU.
Idea: Keep blocks from less active disks in cache. Thus extends idle period.
Cost: More misses to active disks.
Justification: Since active disks are already spinning, cheaper in terms of power consumption.
12/31/2018

8. However …
PA-LRU requires complicated parameter tuning. 4 parameters needed.
No intuition between parameters and disk power consumption or IO times.
Thus difficult to adopt simple extensions or heuristics for real world implementation.
PB-LRU is a practical implementation !
12/31/2018

9. PB-LRU: Main Idea Divide cache into partitions, one for each disk.
Each partitioned managed individually.
Resize partitions periodically.
Workloads are not equally distributed through different disks.
12/31/2018

10. Main Idea (II) So what do we need?
Estimate, for each disk, the energy consumed for a particular cache size. (estimation problem)
Use these estimates to find partitioning that minimize total energy consumption for all disks. (MCKP problem)
12/31/2018

11. Estimation Problem
Q: How to estimate energy consumption per disk for different cache sizes at run time?
Use simulators. One (multi-disk) simulator for every cache size.
Requires (NumCacheSizes X NumDisks) simulators. Impractical!
12/31/2018

12. Estimation Problem (II)
Mattson’s Stack:
Take advantage of inclusion property.
A cache of k blocks is a subset of k+1 blocks. Accessing a stack at position i means a miss at caches smaller than size i.
PB-LRU uses Mattson’s Stack to predict hit or a miss for different partition sizes.
12/31/2018

13. Estimation Problem (III)
In addition, PB-LRU keeps track of previous access time and previous energy consumption.
With these pieces of information, energy consumption of various cache is estimated.
12/31/2018

14. Before Cache Size Pre_miss Energy Cache Accesses Time T1 T2 T3 T4 T5
5 possible
Cache sizes
Stack 1 2 3 4 5
Mattson
Stack
Cache Size
Pre_miss
Energy 1 T5 E5 2 3 4 5
RCache 1 2 3
Before
Existing
Cache (real)
12/31/2018

15. Miss for cache size < 4
Time T1 T2 T3 T4 T5 T6
Access 5 4 3 2 1
LRU
4th element of stack.
Miss for cache size < 4
Stack
4 (1)
1 (2)
2 (3)
3 (4)
5 (5)
T6: Access
Block 4
Cache Size
Pre_miss
Energy 1 T6 E6 2 3 4 T5 E5 5
RCache 4 2 3
E6 = E5 + E(T6-T5) + 10ms + ActivePower
LRU
12/31/2018

16. Solving MCKP
MCKP is NP-hard. But modified problem solvable using dynamic programming.
General result: Increase cache size for less active disks, decrease cache size for active disks.
Why? Penalty for reducing cache size of an active disk is small, while the energy saved for increasing cache size for inactive disk is large
12/31/2018

17. Evaluation Methodology
The integrated simulator
Disk power model
CacheSim
DiskSim
Multi-speed disks model
Similar to IBM Ultrastar 36Z15
Add 4 lower-speed modes: 12k, 9k, 6k and 3k RPM
Power model: 2-competitive thresholds
12/31/2018

18. Evaluation Methodology cont.
The traces
Real system traces
OLTP – database storage system, (21 disks, 128MB cache)
Cello96 – Cello file server from HP, (19 disks, 32MB cache)
Synthetic traces
generated based on storage system workloads
zipf distribution to distribute requests among 24 disks and blocks in each disk
“hill” shape to reflect temporal locality
Inter-request arrival distribution: exponential, Pareto
12/31/2018

19. Simulation results Algorithms Infinite cache LRU PA-LRU PB-LRU
Limited save due
to high cold
misses rate 64%
Algorithms
Infinite cache
LRU
PA-LRU
PB-LRU
PB-LRU
saves 9%
Outperform LRU 22%
12/31/2018

20. Simulation results cont.
PB-LRU has
5% better
response time
saves 40% response time
Oracle DPM does not slow down the average response time for it always spin disk in time for a request
All PB-LRU results are insensitive to the epoch length
12/31/2018

21. Accuracy of Energy Estimation
OLTP, 21 disks with Practical DPM
Largest deviation of estimated energy from real energy is 1.8%
12/31/2018

22. Cache partition sizes MCKP partition tendency
11-12MB
1MB
MCKP partition tendency
gives small sizes to disks which remain active
increase the sizes assigned to relatively inactive disks
12/31/2018

23. Effects of spin-up cost
Disks stay longer at low-power mode
Break-even time increases
12/31/2018

24. Sensitivity Analysis on Epoch Length
The epoch length just needs to be large enough to accommodate the “warm-up” period after re-partitioning.
12/31/2018

25. Conclusion
PB-LRU: online storage cache replacement algorithm partitioning the total system cache amongst individual disks
It focuses on multiple disks with data center workloads
Achieving similar or better energy saving and response time improvement with significant less parameter tuning
12/31/2018

26. Future work
Taking pre-fetching into consideration to investigate the role of cache management in energy conservation
Optimally divide the total cache between the cache and pre-fetching buffers
Implement the disk power modeling component into the real storage system
12/31/2018

27. Impact of PB-LRU 5 citations found at Google Scholar
Energy conservation techniques for disk array-based servers (ICS’04)
Performance Directed Energy Management for Main Memory and Disks (ASPLOS’04)
Power Aware Storage Cache Management
Power and Energy Management for Server Systems
Management Issues
12/31/2018