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IoT Meets the Cloud

IoT Meets the Cloud. Ali Ghodsi UC Berkeley & KTH & SICS alig@cs.berkeley.edu. Cloud Computing?.

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IoT Meets the Cloud

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  1. IoT Meets the Cloud Ali Ghodsi UC Berkeley & KTH & SICS alig@cs.berkeley.edu

  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. Mobile Applications 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 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

  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 …)

  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

  8. Workload Consolidation • Data replicated over commodity machines • Pioneered by Inktomi • Interactive and latency sensitive jobs • User facing applicationse.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 workloadse.g. meet SLO guarantees • Automatic fault-handling e.g. through replication

  10. Transformation ofComputing • Datacenter as a computer • Programs timeshare thousandsof servers

  11. Berkeley Vision • Create an “Operating System Kernel” for the Datacenter Computer • First step with Mesos (mesosproject.org)

  12. Today’sCloud Frameworks • Frameworks simplify distributed programming • Programmingmodels • Hidefailures, synchronization, delayvariance Dryad Pregel 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 frameworkson the same cluster

  14. Solution: Mesos • Common resource sharing layer • abstracts (“virtualizes”) resources to frameworks • enable diverse frameworks to share cluster Hadoop IoT FW Hadoop IoT FW Mesos Multiprograming Uniprograming

  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 • IoTframework requirements • Efficient device tag matching and filtering • Online stream processing of IoT data • Offline storage and batch processing of IoTdata Goal: Buildfirstcloudframework for IoT

  17. IoT Framework Applications • Real time stream processing of data • Security, safety, health applications • Locating people, devices, objects

  18. IoT Framework Applications (2) • Batch processing of big data • Learning trends, patterns, anomalies • Collaborative filtering/recommendation • Computing global device statistics

  19. Summary • Dichotomy: • ChallengedIoT vs Powerful Clouds • ”nerves”—sensors, actuators—collectand send data to the ”brain”—the datacenter • Datacenter is the new super computer • Will needtomultiplexbetweenmanyIoT FW • Need IoT-tailored frameworks to aid IoT services

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