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

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Presentation on theme: "IoT Meets the Cloud Ali Ghodsi UC Berkeley & KTH & SICS"— Presentation transcript:

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

2 Cloud Computing? Larry Ellison, CEO of Oracle CorporationThe 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 FSFIts stupidity. Its worse than stupidity: its a marketing hype campaign. Somebody is saying this is inevitable and whenever you hear somebody saying that, its 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 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

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

12 Todays Cloud Frameworks Frameworks simplify distributed programming – Programming models – Hide failures, synchronization, delay variance 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 frameworks on the same cluster

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

15 IoT Framework Diversity Todays 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

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

19 Summary Dichotomy: – Challenged IoT vs Powerful Clouds nervessensors, actuatorscollect and send data to the brainthe 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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