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IoT Meets the Cloud

Cloud computing is crucial for supporting the anticipated explosion of Internet of Things (IoT) devices. With over 40 billion IoT devices and extensive computational demands, existing devices face limitations in storage, processing, and energy. This presentation explores why datacenters are essential, covering aspects like resource sharing, workload consolidation, and the need for specialized IoT frameworks. By developing cloud solutions tailored for IoT applications, we can effectively utilize the vast data generated, ensuring efficient processing and facilitating real-time analytics and decision-making.

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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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