AN INTEGER-LINEAR ALGORITHM FOR OPTIMIZING ENERGY EFFICIENCY IN DATA CENTERS
4 - Optimizing Data Centers for IoT Revolution - Optimizing Data Centers...requires moving on the...
Transcript of 4 - Optimizing Data Centers for IoT Revolution - Optimizing Data Centers...requires moving on the...
2019 Storage Developer Conference India © All Rights Reserved. 1
Optimizing Data Centers for IoT Revolution
Geet Chauhan, Kishor JadhavMicrochip
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Agenda
r Introductionr IoT Revolutionr Data Explosionr Data Centersr Future
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Introduction
r A brief history of Data Centersr Big picture of Internet of Things
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Evolution of Data Centers
Compute
Storage
Network
Applications
Management
Legacy Consolidated Virtualized
x86 Distributed Farm nodes Fully virtualized
DAS NAS SAN, vSAN, Object, SaaS
Ethernet Ethernet vLAN, NaaS
Stand alone Centralized Optimized, On Demand
Siloed Cluster VM Farm
Long time ago Some time ago Now
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Big Picture of Internet of Things
Internet GW
Internet GW
Cloud GW
Data Lake
Data Warehouse
Data Processor(s)
Data Dispatchers
Control Applications
ML Applications
ML Models
End User Business
Logic
User Applications
Control Data
webserver
gateway
Analytics
Web Applications
MobileApplications
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Core Tenets of IoT
r Data capturer Data filtering and pre-processingr Data storager Data analytics and predictions
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IoT and Data
r What does it mean in terms of ‘data’?r What does it mean in terms of ‘data at scale’?r What do we know about ‘managing big data’?r What do we do about ‘security’, ‘QoS’, ‘privacy’, ‘vulnerabilities’,
‘standardization’?
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Sensorsr Sensor Networks
r Multiple sensors and coverager Types of Sensors
r Aerialr Terrestrialr Undergroundr Underwaterr Multimediar Mobile
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Challenges with Sensors
r Limited storage capacityr Limited processing powerr Power sourcer Power requirements change with ranger Passive sensors generate small amounts of energyr Distance between data creation spot and data analytics enginesr Signal strength and amplification
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Data Explosion
r IoT hits all the three V’s of Big Data premiser Volumer Velocityr Variety
r Example industriesr Healthcarer Agriculturer Defenser Automotiver Retail
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Volume
r Increasing use of IoT devicesr Forecast of multi billion devices with multi billion sensorsr Ever increasing number of application areasr Huge amount of sensor generated datar Zettabytes of data will be stored per day
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Volumes1.7 GB / day / 100 users
8 bytes / sensor
64 bytes / deviceDevice
18432 bytes / day
User
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Velocity
r Speed of data generation / processingr Types
r Batchr Periodicr Near real timer Real time
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Varietyr Unstructured data
r Text files & documentsr e-mail, voice mailr Imagesr AV filesr Sensor datar Application logs
r Structured datar Databaser Data structuresr Can be queried intor Follows a schema
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Areas of Optimization
r Periodicity of data collectionr High bandwidth data transportr High speed data stores for
r Storager Retrieval
r Fast data ingestion into ML algorithms
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Optimization before Reaching Data Centers
r Increase intelligence in edge devicesr Make processing closer to generation pointr Have a micro server in between end point and cloud
r Efficient decision algorithms to chose data variety to be storedr Smart data filtersr Run smart decision engines on edge devices
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Optimization after Reaching Data Centers
r High speed storage switching fabricr High speed storage controllers r Data deduplicationr Object storage
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Optimizing Data Center Storage
r Data storesr High speed flash storager Storage tieringr Hyper converged infrastructure
r Data availabilityr High speed storage controllersr High speed storage switchesr Storage controllers with high IOPS
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QoS and Efficiency
r Data reliability and integrityr Quality of Servicer Efficient storage
r Power efficient storage arraysr Thermally efficient
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Optimizations for ML Applications
r Low power consumption.r High throughput and flexibility.r High use of GPU for different areas like: deep learning, machine
learning, image classification, speech recognition, autonomous driving, bioinformatics and video analytics.
r Support the need for growing parallel computing growth.
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Optimizations for ML Applications
r High performance fabric connectivity and composability for multi-host CPU and NVMe SSD systems is critical.
r Dynamic partitioning.r Multi host SR-IOV sharing.r Enable real time “composition” or dynamic allocation of GPU
resources.
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Optimization for ML Applications
r PCIe advanced switch solutionsr Scalabler Cost-effective multi host interconnectr Low latencyr Fabric application programming API
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Optimizations for ML Applications
r Scaled viewr Multi noder High Availability at fabric
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Optimization using Rack Scale Design
r No single data center fabric is cost, power and performance optimal.r Media and CPU disaggregation for maximizing platform flexibility,
density and utilization.r Optimize resource utilization and reduce “resource stranding”.r Using storage switching and pooling solutions help storage
disaggregation and dynamic hybrid pool support.r PCIe switching and dynamic partitioning capabilities enable high
speed NVM pools at lowest latency and power.
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Optimization using Rack Scale Design
r Top of Rack (ToR) Switchr RSD – Rack Management Switchr RSD – Ethernet Storage Drawerr RSD – PCIe Storage Drawerr RSD – SAS Storage Drawerr RSD – Computer Drawer
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IoT and Edge Computing
r Need for decision in milliseconds require pushing towards computing and decision making at the edge.
r Technology induced latency is a curse.r Sensing, learning, decision making and reacting faster than ever
requires moving on the spot analytics closer to data creation.r In future, most of the sensor data generated would never reach a
data center.r The goal is to reduce dependency on traditional data centers for the
intelligence.
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Edge Computing Application Areas
r Data gathering, filtering and pre–processing r Analyticsr Securityr Location Based Servicesr Asset management (autonomous logistics machinery)r Mining with Robots
And more …
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Data Centers Would Still Evolve
r Use of Persistent Memory in data center storage stacksr Typical NVDIMM target application areasr Low latency lookupsr Byte level data processingr Fast in-memory workloads and faster IOPs
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Data Centers Would Still Evolve
r Use of Computational Storage in data centersr Using Computational Storage Device (CSx); Drive, Processor
and Arraysr Gen-Z adoption in the industry