Automated Lookahead Data Migration in SSD-enabled Multi-tiered Storage Systems

Transcription

1 Automated Lookahead Data Migration in SSD-enabled Multi-tiered Storage Systems Gong Zhang, Ling Liu Georgia Institute of Technology Lawrence Chiu, Clem Dickey, Paul Muench IBM Almaden Research Center 2010/5/ IBM Corporation

2 Multi-tiered Storage Architecture Hosts Virtual LUN LUN 1 Storage controller Data Placement Advisor Fiber Channel LUN 2 Data Migration Planner LUN 3 Device Virtualization Disks Tier 0 SSD Tier 1 (SATA ) Tier 1 (SCSI) /5/6

3 SPC-1 Heat Map

4 TPC-E Heat Map

5 Response migration Data Migration converge deadline utility cost area Peak resp ρ Saturation resp μ resp function f(t) approximation function 0 (t) Learning phaseθ Data Migration ψ Optimization Phase π

6 Response migration Data Migration converge deadline HDD SSD Max_Resp Avg_Resp Min_Resp 0 Workload Learning Data migration /5/6 Optimization

7 Response Optimization 1: Reducing Learning Phase converge deadline learning migration 0 w1 w2 w1 w2

8 Optimization 1: Reducing Learning Phase Response converge deadline learning migration 0 w1 w2 w1 w2

9 Response Optimization 2 :Lookahead Migration converge deadline α β Δ w2 p Ω w1 Δ γ p Δ θ β 0 w1 w2

10 Optimization 3: Adaptive Lookahead Data Migration: Constant lookahead length is not optimal IO density distribution change IO density change drives the changes of hot s in terms of quantity and heat distribution New lookahead length is demanded Hot changes drives the adaptation of lookahead length Greedy algorithm to compute near optimal lookahead length in an approximation manner New lookahead length is computed based on the learned IO performance profile and constraints /5/6

11 Greedy Algorithm: max(diff( w2-ωw1)) near optimal lookahead length Response converge deadline Δ w2 Ω w1 0 w1 w /5/6

12 Adaptive Lookahead Migration Computation Greedy algorithm is limited by the increment granularity Adaptive lookahead data migration: computing optimal lookahead length through bandwidth, SSD Size, etc.

13 Response time reduction with learning phase learning phase reduced migration

14 Lookahead length on response time reduction

15 Compute optimal lookahead length

16 Questions and Thanks? /5/6

17 SPC-1 Response in milliseconds SPC-1 Average Response is only Reduced with Concurrent Migration SPC-1 Response - Full Run DA health checks Migration Begins after 1 hour Minutes into SPC-1 Run SPC-1 Response No Migration 1 st hr Avg: 9.13ms 1 st hr SD: 1.89ms Migration Active 2 nd +3 rd hr Avg: 7.61ms 2 nd +3 rd hr SD: 1.31ms 17

18 What type of workloads are good matching workloads? Sensitivity to migration Stable heat pattern or dynamic heat patttern? Heat distribution among all s Hot s ratio Workload cycle duration Exception: migration not finished before deadline if too short workload time and limited bandwidth

19 The impacts of SSD size The ratio between SSD and HDD The ratio is too small SSD only holds the hottest and creates limited migration impacts The ratio is too large Extra unnecessary cold s migration Bandwidth waste Convergence point point differentiate necessary migration and unnecessary migration

20 How to guarantee the migration deadline Migration deadline must be guaranteed SSD size Saturated unsaturated The data volume to be migrated Impacting s: s migrated before convergence Different on different workload The allocated bandwidth for migration Migration deadline must be guaranteed Unfinished migration Deadline is not ensured Migration finished earlier SSD is full SSD is not full and a mixture of different workloads

21 Impacts of migration on response time Migration reduces response time Approximation function Linear approximation function Non-linear approximation function Convergence process

22 Lookahead migration The factors impacting lookahead migration Peak response time Saturation response time Migration time length Measurement Utility cost

23 Future Work and Conclusion Future Work Multiple workloads (>2 workloads) Parallel workloads Smarter IO density monitoring Workload/IO density prediction Conclusion Lookahead Migration further reduces response time and improves system resource utilization

24 Deadline Aware Data Migration in Multi-tiered Storage Systems User scenario Daytime workload: OLTP Night time workload: Batch processing SSD is scarce resource Constraints Repeating periodical workload cycle Finish migration by deadline Relatively Stable IO profile Diminishing migration return Challenge How to improve data migration performance gains in migration deadline bounded multi-tier storage environment? Response time Resource utilization /5/6

25 Optimization 1: Reducing Learning Phase Response converge deadline HDD SSD Max_Resp Avg_Resp Min_Resp 0 migration optimization /5/6

26 Response converge Static Data Migration deadline Peak Response 0 w1 w2 w1 w2

27 Response converge Lookahead Data Migration deadline α β Peak Response 0 w1 w2 w1 w2