Introduc)on to Cloud Compu)ng Dr. Zhenlin Wang Michigan Tech
Very Short Bio • BS, MS, Peking University – How did I get there? – Why CS? • PhD, University of MassachuseAs, Amherst • Professor at Tech 2
Hobbies? • Teaching and Research • Go • Tea & Sports Games – Well, PE was the only course I couldn’t get an A 3
Clouding CompuOng is here! • Google docs • Dropbox, Overleaf – I am using them now • Tencent, TwiAer, Facebook – Wechat: 600M users and counOng… • NeYlix, Amazon Prime – I am a subscriber • …. 4
What is Cloud Compu)ng ? Let’s hear from the “experts” 5
What is Cloud Compu)ng ? A few years back…. The infinite wisdom of the crowds (via Google Suggest ) 6
What is Cloud CompuOng? • Now 7
What is Cloud Compu)ng ? We’ve redefined Cloud CompuOng to include everything that we already do. . . . I don’t understand what we would do differently in the light of Cloud CompuOng other than change the wording of some of our ads. Larry Ellison , Co-founder, CEO of Oracle 8
What is Cloud Compu)ng ? It’s stupidity . It’s worse than stupidity : it’s a markeOng hype campaign Richard Stallman GNU 9
What is Cloud Compu)ng ? Cloud CompuOng will become a focal point of our work in security. I’m opOmisOc … Ron Rivest The R of RSA 10
What is Cloud Compu)ng ? It’s about jobs! It’s about small business! 11
So, What really is Cloud Compu)ng ? Cloud compu)ng is a new compuOng paradigm, involving data and/or computaOon outsourcing, with – Infinite and elasOc resource scalability – On demand “just-in-Ome” provisioning – No upfront cost … pay-as-you-go That is, use as much or as less you need, use only when you want, and pay only what you use, 12
NeYlix Version 1 NeDlix Movies: Master Home copies Amazon.com 13
What’s new in Today’s Clouds? Besides massive scale, three major features: I. On-demand access: Pay-as-you-go, no upfront commitment. Anyone can access it (e.g., Washington Post – Hillary Clinton – example) II. Data-intensive Nature: What was MBs has now become TBs, PBs. Daily logs, forensics, Web data, photos, videos, etc. – Do you know the size of Wikipedia dump? – III. New Cloud Programming Paradigms: MapReduce/Hadoop, Pig LaOn, and many others. High in accessibility and ease of programmability – CombinaOon of one or more of these gives rise to novel and unsolved distributed compuOng problems in cloud compuOng. 14
The real story “CompuOng UOlity” – holy grail of computer science in the 1960s. Code name: MULTICS (MulOplexed InformaOon and CompuOng Service) Why it failed? • Ahead of Ome … lack of communicaOon tech. (In other words, there was NO (public) Internet) • And personal computer became cheaper and stronger 15
The real story Mid to late ’90s, Grid compu)ng was proposed to link and share compuOng resources 16
The real story … conOnued Post-dot-com bust, big companies ended up with large data centers, with low uOlizaOon Solu)on: Throw in virtualizaOon technology, and sell the excess compuOng power And thus, Cloud Compu)ng was born … 17
Cloud compuOng provides numerous economic advantages For clients: – No upfront commitment in buying/leasing hardware – Can scale usage according to demand – Barriers to entry lowered for startups For providers: – Increased uOlizaOon of datacenter resources 18
Cloud compuOng means selling “X as a service” IaaS: Infrastructure as a Service – Selling virtualized hardware PaaS : PlaYorm as a service – Access to a configurable plaYorm/API SaaS : Somware as a service – Somware that runs on top of a cloud 19
Cloud compuOng architecture e.g., Web browser SaaS , e.g., Google Docs PaaS , e.g., Google AppEngine IaaS , e.g., Amazon EC2 20
Top 10 Obstacles (Berkley’09) • Availability of Service – Use MulOple Cloud Providers; Use ElasOcity to Prevent DDOS • Data Lock-In – Standardize APIs – CompaOble SW to enable Surge CompuOng • Data ConfidenOality and Auditability – Deploy EncrypOon, VLANs, Firewalls; Geographical Data Storage • Data Transfer BoAlenecks – FedExing Disks; Data Backup/Archival; Higher BW Switches • Performance Unpredictability – I/O interferences – Improved VM Support; Flash Memory; Gang Schedule VMs 21
Top 10 Obstacles • Scalable Storage – Invent Scalable Store • Bugs in Large Distributed Systems – Invent Debugger that relies on Distributed VMs • Scaling Quickly – Invent Auto-Scaler that relies on machine learning – Snapshots for ConservaOon • ReputaOon Fate – Sharing offer reputaOon-guarding services like those for email • Somware Licensing – Pay-for-use licenses; Bulk use sales 22
My Research • Memory system modeling and virtualizaOon • Dynamic data center resource management 23
Memory Balancing • Dynamic member balancing for virtual machines (Zhao&Wang VEE’99, Wang et al. 2G? 2G? 2G? ATC’11) 24
Memory Balancing 2G? 2G? 2G? 473.astar 25
Memory Balancing: Demand PredicOon Control Plane Phase detectioin Miss ratio curve WSS Estimation Intermittent Memory resize Kernel Tracking restore revoke Dynamic Hot Set resize AVL-tree Based LRU Hardware L1,L2,DTLB Monitoring 26
Key-Value Store Management • LAMA: Op(mized Locality-aware Memory Alloca(on for Key-value Cache (Hu et al. ATC 15) class A class B How to dynamically adjust cache allocaOon? 27
Cross-Architecture Co-Tenancy PredicOon • NSF CSR’14 with Dr. Laura Brown (CCGRID’15, AAAI PhD ConsorOum’15) Sensitivity Curve y=f_astar(x) ... latency � sensitive program A’s ... astar sensitivity function on HW1 Latency � Sensitive ... ... Programs Curve fitting y=f_gcc(x) Input Core 1 Core 2 gcc Batch programs Profiling as interference cross architectural mapping report pressure Shared cache/Memory score of batch y=g_astar(x) program y=f_astar(x) Program A’s sensitivity curve ... ... Hardware Configuration1(HW1) ... Output on HW2 Training ... ... ... Sensitivity Curve Regression y=f_gcc(x) y=g_gcc(x) Performance? performace degradation Model: final y=g_astar(x) g_program=func(f_program,HW1,HW2) prediction ... astar ... Latency � Sensitive ... ... Programs Curve fitting p_astar q_astar pressure score y=g_gcc(x) ... ... Core 1 Core 2 ... ... gcc Output Batch programs Profiling Training ... ... Program B’s pressure score as interference p_gcc q_gcc on HW2 Regression Batch’s Shared cache/Memory Pressure Model: Score p_program=func’(q_program,HW1,HW2) Hardware Configuration2(HW2) Input batch program B’s pressure score on HW1 28
Systems research is exciOng! Students are always welcome! – Junior year is the best Ome to join 29
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