Computing. Chaitan Baru San Diego Supercomputer Center. Competitive Advantage Through Cloud Computing

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1 1 Technical Aspects of Cloud Computing Chaitan Baru San Diego Supercomputer Center Competitive Advantage Through Cloud Computing

2 2 News flash Now Available in the Cloud.. What does this mean? For the developers For the users

3 3 Outline The changing context Cloud computing definitions Implementation considerations Application case studies Future directions Course materials: click on Education Files stored in the SDSC Cloud

4 4 The Changing Context Rapid growth in data data-driven business decisions Scientific workloads as a predictor of future business workloads Sensor-based systems, remote sensing, genome sequencing A point of inflexion Changing software: from RDBMS, to nosql, to streaming, to scientific data, Changing hardware: multi-core, solid-state disk, large memory and new types of memory Changing platforms: wholly-owned ( on premises ) systems vs clouds Changing business costs / models: ultra-high productivity, energy efficiency, rent vs own,

5 5 New sources of increasing data volumes Sensor Networks Top Social Networks for Big Data, Stacey Higginbotham Sep. 13, 2010,

6 6 Gene Sequencing ~2TB/experiment in next-generation gene sequencing Sequencing of individuals Multiple runs per individual Multiple sequencing over time Managing and Analyzing Next-Generation Sequence Data, Richter BG, Sexton DP (2009). PLoS Comput Biol 5(6): e doi: / journal.pcbi , June 2009

7 7 Remote Sensing ~1TB of high-resolution topographic data for San Andreas Fault 10x more for imagery Repeated scans for ecological applications OpenTopography.org LaSDI Initiative: Laser Spatial Data Infrastructure Sub-meter to 10cm scale 3D models of earth

8 8 Data is the new oil! The data ecosystem From acquisition, to transfer, storage, creation of derived products, and exploitation Those with data are better off than those without Those who can exploit data have the competitive advantage Walmart, Fedex, Wall Street trading, Internet companies (Google, Amazon, Facebook, Twitter,..) And, cannot find oil without data! Oil exploration data growing from 10 stb to PBs over next few years

9 9 Where should all the data reside? All in your private systems (private cloud)? All in a public cloud? Hybrid model: Private + Public?

10 Cloud Computing: Definition Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction Another turn of the screw in our push towards productivity

11 11 Cloud Computing: NIST Definition On-demand self-service You get the resource when you ask for it, using APIs Broad network access Accessible from anywhere Resource pooling Shared resource provisioning Rapid elasticity Uniform scaleout Measured service Monitoring of usage, reporting of usage

12 Delivery Models Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Importance? Implications on what type of programming work needs to be done Who do you have (or want to hire) to work on this?

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14 Delivery Model: Software as a Service Software as a Service (SaaS) The capability provided to the consumer is to use the provider s applications running on a cloud infrastructure Accessible from various client devices through a thin client interface such as a Web browser (e.g., web-based ) The consumer does not manage or control the underlying cloud infrastructure With the possible exception of limited user-specific application configuration settings.

15 Software as a Service: Example Google Maps API Users are provided with simple APIs for maps Uses cloud resources at the back-end Facebook Social networking site using a suite of cloud-based tools at the back-end Animoto Service that makes videos from user uploaded images Uses Amazon EC2 at the back-end

16 16 Salesforce.com: SaaS

17 Delivery Models: Platform as a Service Platform as a Service (PaaS) The capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created applications using programming languages and tools supported by the provider (e.g., java, python,.net) The consumer does not manage or control the underlying cloud infrastructure, network, servers, operating systems, or storage But the consumer has control over the deployed applications and possibly application hosting environment configurations.

18 Platform as a Service: Google AppEngine *Credit:

19 Delivery Model: Infrastructure as a Service Infrastructure as a Service (IaaS) The capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources Consumer is able to deploy and run arbitrary software, which can include operating systems and applications The consumer does not manage or control the underlying cloud infrastructure But has control over operating systems, storage, deployed applications, and possibly select networking components (e.g., firewalls, load balancers)

20 Infrastructure as a Service: Amazon Web Services (AWS) Amazon Elastic Compute Cloud (EC2) A web service that provides resizable compute capacity in the cloud. Configure an Amazon Machine Instance (AMI) and load it into the Amazon EC2 service Quickly scale capacity, both up and down, as your computing requirements change Amazon Simple Storage Service (S3) A simple web services interface that can be used to store and retrieve large amounts of data, at any time, from anywhere on the web It gives any developer access to the same highly scalable, reliable, fast, inexpensive data storage infrastructure that Amazon uses to run its own global network of web sites

21 21 AWS, aws.amazon.com

22 22 Microsoft Azure

23 23 OpenStack, Receiving attention. Example, Cisco support for OpenStack

24 24 Eucalyptus Target market On-premise (private) IaaS Use existing infrastructure to create AWS-compatible cloud Products: Eucalyptus IaaS Eucalyptus OpenSource Eucalyptus RightScale

25 25 Eucalyptus IaaS

26 26 Nirvanix CloudComplete Can vary among private, hybrid, public cloud implementations, using Nirvanix s public cloud

27 27 Virtualization and Cloud Computing Virtualization is the ability to run virtual machines on top of a hypervisor. A virtual machine (VM) is a software implementation of a machine (i.e., a computer) that executes programs like a physical machine. Each VM includes its own kernel, operating system, supporting libraries and applications. A hypervisor provides a uniform abstraction of the underlying physical machine. Multiple VMs can execute simultaneously on a single hypervisor. The decoupling of the VM from the underlying physical hardware allows the same VM to be started on different physical machines. Virtualization is an enabler for cloud computing Gives the cloud computing provider the flexibility to move and allocate the computing resources requested by the user wherever the physical resources are available.

28 28 SNIA CDMI Cloud Data Management Interface Standardizing at the IaaS level

29 29 Some Take Home Lessons Cloud providers are providing you a service, not just a product Product model: sell product, support product Service model: provide service, become intimately exposed to all aspects of the service that the customer sees Seeing this from a customer s viewpoint

30 Cloud Computing Costs* * Source: McKinsey & Co

31 31 Cloud Computing: The Rationale Flatten out the peaks and valleys of utilization to get higher overall utilization of entire infrastructure Bring together workloads with different valley / peak behaviors But is running a high utilization operation the same as running a low utilization operation? Velocity 2010: Datacenter Infrastructure Innovation, James Hamilton, VP & Distinguished Engineer, Amazon AWS

32 32 Dealing with Peaks Old approach: Provision for peak workload. Low utilization at other times HPC approach: Build a machine for a certain max job size. Provide job queue and on-demand, pre-emptible access at other times. Cloud approach: Charge different rates for use at different times, based on usage E.g. Amazon Spot Instance Typical server workloads: 10-15%

33 33 Some Application Case Studies from XLDB XLDB11: 5 th Extremely Large Databases Workshop, Oct 18-19, SLAC, Palo Alto, CA State of practice workshop Presentations on current system implementation and challenges, and needs and requirements E.g. presentations from: Facebook, LinkedIn, ebay, Google, Netflix, Novartis, Quora, Metamarkets, Microsoft,

34 34 Quora.com Scaling up Quickly in the Cloud, Edmond Lau, Quora, XLDB Workshop

35 35 Quora.com

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37 37 Quora

38 38 Quora

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40 40 Metamarkets, Michael Driscoll, cofounder, CTO

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43 43 Metamarkets: Performance at scale Evaluating the online ad market Billions of microtransactions per day Require billion rows/second performance Fast analytics over 100 s of terabytes Metamarket s Druid system Partial aggregates + In-memory data + Indexes Distributed data + Parallelizable Queries = Horizontal Scalability Real-time analytics Implemented in the cloud (AWS)

44 44 Cloud Data Analytics, Roger Barga, Microsoft Azure

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48 48 Netflix Presentation by Eric Colson, VP, Netflix All Netflix processing (DVD rentals and Streaming video) is in the cloud (AWS) Total cost of Netflix implementation may be higher than an in-house solution but, Netflix made a business strategy decision. They are not in the business of running IT infrastructure Cloud computing required them to build a distributed IT team, which did not match their culture of building close teams.

49 49 Application Examples: Bioinformatics Crossbow Genotyping from short reads using cloud computing index.shtml SDSC Project to implement Hadoop-based processing for next generation sequencing on SDSC s HPC systems as well as clouds (AWS)

50 50 Role of Big Data What is the connection between cloud and big data? Cloud Scaling Lots of data Big data Lots of data Hadoop A software (eco)system for efficient processing of very large data Uses MapReduce, which has become a convenient language for low entry barrier, very large-scale data processing Could use cloud computing resources to implement Hadoop $1M Question: data movement and data locality

51 51 Discussion of Survey Results from Cloud Storage: Adoption, Practice and Deployment, survey conducted for the Storage Networking Industry Association (SNIA)

52 52 Research and Markets Survey Cloud Computing in HPC: Rationale for Adoption Top reason: Access to extra resources to meet peak system load requirements Cost Avoidance Continued demand for HPC compute cycles. Cloud computing could deliver lowcost computing cycles. Capacity Management Deal with periodic demand peaks and better management of data center growth, power, and cooling issues. Collaboration Integration of internet-based applications and communications may allow HPC users to better work with those both inside and outside of their organizations. Evaluation of Cloud Systems Looking at the cloud system alternative to determine if and how they can make use of the technology and concepts. Organizational Requirement Making sure that the competition does not steal a march with a new technology.

53 53 Monitoring and Benchmarking Monitoring of resource usage is essential for cloud environments What about SLAs and QoS? Cloud computing means get your legal teams lined up! Resource monitoring is well-recognized need But need to ensure the right level of monitoring and reporting is available Benchmarking is a new frontier

54 54 Amazon Cloudwatch cloudwatch/

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56 56 Azurescope See:

57 57 Azurescope application probe

58 58 Azurescope write probe

59 Future Directions: Benchmarking The need for Big Data and Cloud benchmarks The changing software landscape From RDBMS and Data Warehousing to NoSQL, Hadoop, Unstructured data, Stream Processing, Graph Processing, The changing hardware landscape Multi-core, SSD, new types of memory, large memory, different networking options, commodity vs high-end, Multiple platform choices Dedicated data platforms Cloud platforms

60 Benchmarking Issues Reference benchmarks for big data (TPC style) Define modalities of big data Define end-to-end flows of big data Identify key real-world characteristics, e.g. multi-rack, heterogeneous hardware Identify which existing benchmarks can be reused Probe benchmarks for clouds Cloud performance can be variable Application-level performance probes The Cloud Weather Service Benchmarking workshop being planned for Feb 2012 timeframe

61 61 Future Directions: Vertical Clouds Clouds that are aimed at major markets Should have something unique about them, and a sizable market E.g. Collocated clouds for performance e.g. Wall Street trading systems, online advertisement systems, etc. Collocated clouds for security / privacy

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