Breakout Session

1. Auto-Scaling Redis Caches
Observability, Efficiency & Performance SLAs
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2. Uncontrolled
Performance
=
Lost Revenue
Amazon.com
100 ms latency => 1% drop in sales
Aberdeen (1,000 company sample)
Latency => 9% enterprise revenue loss
Stable
High latency ads => 46% viewer loss
Google
500 ms =>20% less clicks: ad revenue loss
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3. Delivering Data QoS is Challenging
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App Datarequirements
changingdaily. Providing
QoS has become hard

4. Performance Management is Getting Harder
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The problemsare
gettingmuch
worse with increasing hardware
complexity

5. What’s the Path
Forward?
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6. New Layer Needed
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Systems of
Intelligence
Self-Learn / Model
• Application workloads
• Infrastructure components
Control / Operate to maximize:
• Efficiency
• Performance
• SLA Conformance
Infrastructure
Applications

7. How about an
Auto-Scaling Data Infrastructure?
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8. 8 Copyright 2018 CachePhysics, Inc.
Autonomous Performance Management?

9. KeepExisting
Applications
9Copyright 2018 CachePhysics, Inc.
KeepExisting
Infrastructure Add Real-time Self-Learning
Instrumentation
Enforce SLA
Optimize
Monitor
Existing Application Infrastructure
Model
Control
Apps & Microservices
Caches
Network, Database, Filesystem, Compute
Predict
SaaS API
Calls
API
Callbacks
Autonomous Performance
Management
Auto Size,
Reconfigure

10. Modeling Cache Performance in Real-Time
10
Lower is better
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Cache Size (GB)
Latency(ms)
• Learn performance model of applications
and cache
• Predict the performance of workload as
f(cache size, params)
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Cache Performance
Hit Ratio
Cache Size
65%
128GB
123

11. Understanding Cache Models
Lower is better
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Cache Size (GB)
Latency(ms)
123
x2 x4 x8 Models help decide useful
increments of change.
In this example, no benefit
despite an 8x increase in
budget.
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12. Understanding Cache Models
Lower is better
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Cache Size (GB)
Latency(ms)
123
Often, most operating points are
highly inefficient.
This cache is operating at the
lowest ROI point; equivalent
performance to 1/8 the budget.
Auto-scaling data infrastructure
would picking only the efficient
operating points.
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13. Understanding Model-based Adaptation
Single Workload.
Prediction of
performance under
different policies.
A auto-scaling data
infrastructure would
always pick the optimal.
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14. Sample Models From Production Workloads
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Achieving Latency Targets
Latency
Target (7 ms)
Cache
Allocation
(>16 GB)Client target 95th %ile latency is 7 ms
Autoset cache partitions size to 16GB to
guarantee avg latency SLOs * Throughput
targets can be
implemented
similarlyCache Size (GB)
95th%ileLatency(ms)
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Achieveing Multi-Tier Sizing
* Can model network
bandwidth as a
function of cache
misses from each tier
Tier 0 (DRAM) allocation
Tier 1 (3D Xpoint)
Network
Misses
Tier 2 (Local Flash)
Tier 3 (Remote
Flash)
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17. Auto-Scaling Data Infrastructure
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Remote
Tier
Monitoring Auto-Select Policies
(dynamic parameters)
Lower is better
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Cache Size (GB)
MissRatio
0.0
0.2
0.4
0.6
0.8
0.00
0.25
0.50
0.75
1.00
0 5 10 15 20 25
Cache Size(GB)
MissRatio
Block Size (KB)
4
16
64
256
1024
Latency Reduction
(Thrashing Remediation)
Latency Guarantees Accurate Tiering
Tier 0 allocation for this client
Tier 1 allocation for this clientLatency
Target (7 ms)
Cache
Allocation
(>16 GB)
Client target IO latency is: 7 ms
Guarantee avg latency: autoset
cache partition to 16GB
Multi-Tenant Isolation
vs
msr_mds (1.10%) msr_proj (0.06%) msr_src1 (0.06%) t01 (0.05%)
t06 (0.33%) t08 (0.04%) t14 (0.38%) t15 (0.10%)
t18 (0.08%) t19 (0.06%) t30 (0.06%) t32 (0.98%)
0.0
0.5
1.0
0.0
0.5
1.0
0.0
0.5
1.0
0 40 80 0 500 1000 0 200 0 200 400
0 50 100 0 300 600 0 100 200 300 0 200 400
0 100 200 0 200 400 0 100 200 300 0 9 18
Cache Size (GB)
MissRatio
msr_mds (1.10%) msr_proj (0.06%) msr_src1 (0.06%) t01 (0.05%)
t06 (0.33%) t08 (0.04%) t14 (0.38%) t15 (0.10%)
t18 (0.08%) t19 (0.06%) t30 (0.06%) t32 (0.98%)
0.0
0.5
1.0
0.0
0.5
1.0
0.0
0.5
1.0
0 40 80 0 500 1000 0 200 0 200 400
0 50 100 0 300 600 0 100 200 300 0 200 400
0 100 200 0 200 400 0 100 200 300 0 9 18
Cache Size (GB)
MissRatio
smax = 8K exact MRC
Client 0 Client 1
Part. 0 Part. 1
Results:
• Safely quantify
impact of changes
• Often 50-150%
cache efficiency
improvements ($$)
• Latency SLAs met
• Fewer production
fire fights
• Higher consolidation
ratios
• Accurate Capacity
Planning

18. Why Now?
Every code change can morph
infrastructure requirements. App
performance today: static,
manual, error-prone.
We auto-learn dynamically
New Apps: Cloud Native
Hardware complexity increasing,
even as data growth accelerates.
But latency not keeping up
w/FLOPS (-4%/yr).
We tame complexity, maximize ROI
New Layers: NVMe/3DXpoint
IncreasingComplexity
Massive DataGrowth
Latencyworseby4%/yr
SMR HDD
SLOW FLASH
FLASH
MRAM
RERAM
7200 RPM HDD
ENTERPRISE HDD
NVME
DRAM
I$
D$ LLC
SMR HDD, OPTICAL
TAPE
3DXPOINT
DRAM
Kubernetes’ CRDs + API extensions =
default platform for next decade. But
performance unpredictability pain
unsolved.
We layer on SLA guarantee solution
Infrastructure »» Declarative
* CD = Continuous Deployment, the predominant trend where code is deployed automatically after testing
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19. Use Case #1: Self-service Infrastructure Sizing
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• Value for Developers
• Observability dashboard
• Sizing recommendations
• Cost savings analysis
• Value for Ops
• Achieve desired SLAs for lowest spend
• Observability for data teams
• Faster troubleshooting for infra teams
• Predictive planning
Monitoring
Lower is better
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Resources ($$)
Latency
0.0
2.0
4.0
6.0
8.0

20. Use Case #2: Automated QoS SLA
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• Value for Developers
• Dial-in latency or throughput target and a budget
• Auto-scaling data infrastructure allocates just
enough capacity to hit SLA
• Value for Ops
• Automated SLA achievement!
• Set and Forget, ease of mind
• Revenue disruption avoidance
• Improved margins
• Zero OpEx performance scaling
• Dramatically reduced service interruptions
Latency Guarantees
Latency
Target (7 ms)
Cache
Allocation
(>16 GB)
Client target IO latency is: 7 ms
Guarantee avg latency: autoset cache
partition to 16GB

21. Model your Redis cache!
1. Sign up for free trial: www.cachephysics.com/redisconf18
2. Test your access pattern to create a Redis cache model, receive a live dashboard
3. Cache Observability ==> Faster Time to Resolution ==> Performance SLAs
– Simple!
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22. Thank You
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