IoT Meets the Cloud
Ali Ghodsi
UC Berkeley & KTH & SICS
[email protected]
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 workloads
e.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
Dryad
Pregel
• Frameworks simplify distributed programming
– Programming models
– Hide failures, synchronization, delay variance
Each framework runs on a dedicated cluster/partition
One Framework Per Cluster Challenges
50%
• Inefficient resource usage
– E.g., Hadoop cannot use available
resources from IoT FW cluster
– No opportunity for stat. multiplexing
25%
Hadoop
50%
IoT FW
• Hard to share data
– E.g., Not easy for IoT FW to use data
generated by Hadoop
25%
0%
– Copy or access remotely, expensive
• Hard to cooperate
0%
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
Hadoop IoT FW
Hadoop
IoT FW
Uniprograming
Mesos
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