iot meets the cloud ali ghodsi uc berkeley & kth & sics alig@cs.berkeley.edu

IoT Meets the Cloud

Ali GhodsiUC Berkeley & KTH & SICS

alig@cs.berkeley.edu

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

Mobile Applications

Most of the Computation on the Cloud Already!

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

What is the cloud?

• Datacenter Computing– Thousands of servers– Co-located storage– Routers and switches– Backup power

supplies– Cooling

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 …)

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

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 …

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

Transformation of Computing

• Datacenter as a computer– Programs timeshare thousands

of servers

Berkeley Vision

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

Today’s Cloud Frameworks

• Frameworks simplify distributed programming– Programming models– Hide failures, synchronization, delay variance

Dryad

Pregel

Each framework runs on a dedicated cluster/partition

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

Solution: Mesos

• Common resource sharing layer – abstracts (“virtualizes”) resources to frameworks– enable diverse frameworks to share cluster

IoT FWHadoo

p

IoT FWHadoo

pMesos

Uniprograming Multiprograming

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

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

IoT Framework Applications

• Real time stream processing of data– Security, safety, health applications– Locating people, devices, objects

IoT Framework Applications (2)

• Batch processing of big data– Learning trends, patterns, anomalies– Collaborative filtering/recommendation– Computing global device statistics

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