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Ali Ghodsi UC Berkeley & KTH & SICS

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1 Ali Ghodsi UC Berkeley & KTH & SICS
IoT Meets the Cloud Ali Ghodsi UC Berkeley & KTH & SICS

2 Cloud Computing? Larry Ellison, CEO of Oracle Corporation “The computer industry is the only industry that is more fashion-driven than women's fashion. Maybe I'm an idiot, but I have no idea what anyone is talking about. What is it? It's complete gibberish. It's insane. When is this idiocy going to stop?” Richard M. Stallman, President of FSF “It’s stupidity. It’s worse than stupidity: it’s a marketing hype campaign. Somebody is saying this is inevitable — and whenever you hear somebody saying that, it’s very likely to be a set of businesses campaigning to make it true.” My claim: Cloud computing is inevitable for the Internet-of-Things

3 Most of the Computation on the Cloud Already!
Mobile Applications Proximity sensor, ambient light, digital compass, gyroscope, accelerometer, dual microphone, multi-touch touchscreen bluetooth Most of the Computation on the Cloud Already!

4 Do we need the cloud for IoT?
Device deluge 3 billion smart phones Another 40 billion IoT devices Devices will be challenged Limited storage Limited processing Limited communication Limited energy 12kbit/s, P2P Clouds needed for IoT, just as for phones and desktops

5 What is the cloud? Datacenter Computing Thousands of servers
Co-located storage Routers and switches Backup power supplies Cooling Talk of the size... Half a mill.... Amazon

6 Why do we need datacenters?
Multi-core Computing Processing speed stagnation Increased parallelism Supercomputer not sufficient Parallel computing quintessential to cloud computing Request-level parallelism Parallel algorithms (MapReduce, Indexing …) Typical machine, 12 disk drives, 16 cores, several NICs, 160 GB memory

7 Why do we need datacenters? (2)
Economy of scale Reduce server cost Reduce cooling cost Reduce power cost Clouds are efficient PUE = total_facility_power/ equipment_power ~ 1.2 Energy economy-of-scale Commodity servers Workload consolidation Energy: common cooling, near cheap places and good climate for cooling, common infrastructure, buildings, backup power Commodity: reliability

8 Workload Consolidation
Data replicated over commodity machines Pioneered by Inktomi Interactive and latency sensitive jobs User facing applications e.g. search queries, tweets, … Millisecond SLOs Batch-jobs Building search indexes … Analytics of trends, business data … AV/spam filtering …

9 Workload Consolidation (2)
Interactive and batch on same machines Virtualization of computation e.g. migration, hardware agnosticism Isolation of workloads e.g. meet SLO guarantees Automatic fault-handling e.g. through replication

10 Transformation of Computing
Datacenter as a computer Programs timeshare thousands of servers Twitter

11 Berkeley Vision Create an “Operating System Kernel” for the Datacenter Computer First step with Mesos ( Twitter

12 Today’s Cloud Frameworks
Dryad Pregel Frameworks simplify distributed programming Programming models Hide failures, synchronization, delay variance Each framework runs on a dedicated cluster/partition

13 One Framework Per Cluster Challenges
Inefficient resource usage E.g., Hadoop cannot use available resources from IoT FW cluster No opportunity for stat. multiplexing Hard to share data Copy or access remotely, expensive Hard to cooperate E.g., Not easy for IoT FW to use data generated by Hadoop Hadoop IoT FW Hadoop IoT FW Need to run multiple frameworks on the same cluster

14 Solution: Mesos Common resource sharing layer Mesos Uniprograming
abstracts (“virtualizes”) resources to frameworks enable diverse frameworks to share cluster Hadoop IoT FW Hadoop IoT FW Mesos Our solution is Mesos, a resource sharing layer that abstracts resources to framework, and this way allows over which diverse frameworks cab run, by abstracting resources to framework By doing so we go from uniprograming or one framework per cluster to multiprogramming where we share entire cluster among diverse frameworks. Uniprograming Multiprograming

15 IoT Framework Diversity
Today’s frameworks tailored for specific application domains MapReduce for indexing and filtering Pregel for graph algorithms IoT problem domain highly diverse Existing frameworks poor fit for IoT

16 New IoT Frameworks for Clouds
IoT framework requirements Efficient device tag matching and filtering Online stream processing of IoT data Offline storage and batch processing of IoT data Goal: Build first cloud framework for IoT

17 IoT Framework Applications
Real time stream processing of data Security, safety, health applications Locating people, devices, objects Real time. Supply-chain, real time trigger the ordering of new supplies when inventory reaches low Real time. Enough-smoke sensors over an area goes off, trigger emergency system

18 IoT Framework Applications (2)
Batch processing of big data Learning trends, patterns, anomalies Collaborative filtering/recommendation Computing global device statistics Batch. Retailers in a region can collaborate to get aggregate trend of undersupply of a product category in a city. Batch. Aggregate users behavior to recommend to others

19 Summary Dichotomy: Challenged IoT vs Powerful Clouds ”nerves”—sensors, actuators—collect and send data to the ”brain”—the datacenter Datacenter is the new super computer Will need to multiplex between many IoT FW Need IoT-tailored frameworks to aid IoT services

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