iot meets the cloud ali ghodsi uc berkeley & kth & sics [email protected]
TRANSCRIPT
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