Distributed Systems: Concepts, Fundamental Problems, and Models, Study notes of Electrical and Electronics Engineering

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Lecture 1 introduction: systems, fundamental problems, and models distributed systems CS425 / ECE 428 / CSE 424 acknowledgment These slides are based on ideas and material from the following sources: • slides prepared by Professors M. T. Harandi and J. Hou and subsequently  modified by Professors Indranil Gupta, Nitin Vaidya, and Yih‐Chun Hu at  University of Illinois • slides from Professor S. Ghosh’s course at University of Iowa 1. the Internet = : g We we pe eal So aa x hup:innww cheatin camdmapandex tra Copyright (C) 1999, Lucent Technolagies ans.not “7 7 urchy/Cheswick map of the Itemet showing the major ISPs, Data collected 28 June 1999 Internet Mapping Project, color coded by ISPs a small part of the Internet intranet ISP desktop: backbone satellite link server: network link: a typical Intranet the rest of email server Web server Desktop computers File server router/firewall print and other servers other servers print Local area network email server the Internet 4. distributed mobile robots si Kiva robot 500 robots coordinate to manage inventory in a warehouse http://www. raffaello.name/KivaSystems.html 5. computation grids • Seti@Home • uses Internet‐connected  computers in the Search for  Extraterrestrial Intelligence  • downloads and analyzes  radio  telescope data on your desktop  and sends it back to Seti servers • http://setiathome.berkeley.edu/ what is a distributed system? it is a collection of entities (computers, robots, fireflies,…) – each of which is autonomous, asynchronous and [possibly] failure‐ prone – communicating through [possibly] unreliable channels (ethernet,  wireless, vision,…) – to perform some common function (detection, communication,  computation) nodes or  processes or agents edges or  links or  channels why distributed systems ? * geographic distribution of processes * resource sharing (as used in P2P networks) * computation speed up (as in a grid) e fault tolerance CS 425 CS 425 is an introduction to the key concepts for — designing — analyzing and — implementing distributed systems important role of failures another definition: a distributed system is a system in which the failure of a computer you’ve never heard of can prevent you from getting your work done fundamental problems in  distributed systems • time synchronization • leader election • mutual exclusion • distributed snapshot • routing • consensus • replica management • transactions we will study algorithms (and lower bounds) for these  types of problems approaching  implementations the rest of email server Web server Desktop computers File server router/firewall print and other servers other servers print Local area network email server the Internet a simple communication model: message passing system topology is a graph G = (V, E), where  V = set of nodes (sequential processes)  E = set of edges (links or channels, bi/unidirectional) four types of actions by a process: – internal action: sequential process computes – send action: sends a message (puts message in channel) and  performs computation – receive action: receives a message (message taken out from channel)  and performs computation a reliable FIFO channel Axiom 1. Message m sent ⇔ message m received Axiom 2. Message propagation delay is arbitrary but finite. Axiom 3. m1 sent before m2 ⇒ m1 received before m2. P Q shared memory model address spaces of processes overlap M1 1 3 2 4 M2 concurrent operations on a shared variable are serialized weak vs. strong models One object (or operation) of a  strong model = more than one  objects (or operations) of a  weaker model.  Often, weaker models are  synonymous with fewer  restrictions. One can add layers (additional  restrictions) to create a stronger  model from weaker one. Examples HLL model is stronger than assembly languagemodel. Asynchronous is weaker than synchronous. Bounded delay is stronger than unbounded delay (channel) model transformation Stronger models ‐ simplify reasoning, but  ‐ needs extra work to  implement Weaker models ‐ are easier to implement.  ‐ have a closer relationship  with the real world “Can model X be implemented  using model Y?” is an  interesting question in  computer science. sample problems non‐FIFO to FIFO channel message passing to shared  memory from non-FIFO to FIFO channel ? Cr) m2 m3 m4 mi Ce} buffer typical design goals • heterogeneity – can the system handle different types of PCs and  devices? • robustness – is the system resilient to crashes and failures? • availability – are data, services always there? • transparency – does the system hide internal workings from the  users? • concurrency – can the server handle multiple clients simultaneously? • efficiency – is it fast enough? • scalability – can it handle 100 million nodes? • security – can the system withstand hacker attacks? • openness – is the system extensible? extra slides Internet: Protocols and  Transport Application e-mail remote terminal access Web file transfer streaming multimedia remote file server Internet telephony Application layer protocol smtp [RFC 821] telnet [RFC 854] http [RFC 2068] ftp [RFC 959] rtsp [RFC 2326], proprietary NFS, AFS, … H.323, SIP [RFC 2543], … Underlying transport protocol TCP TCP TCP TCP TCP or UDP TCP or UDP typically UDP Security • Security has three components: – Confidentiality (protection against disclosure to unauthorized  individuals). – Integrity (protection against alternation or corruption). – Availability (protection against interference with the means to  access the resources). • Two security challenges: – Denial of service attacks: bombarding the service with a large  number of pointless requests. – Mobile code security:  mobile codes may be accessing local  resources. Scalability • A system is said to be scalable if it will  remain effective when there is a  significant increase in the number of  resources and users: – Controlling the cost of resources – Controlling the performance loss – Preventing software resources running out  (e.g., IP addresses) – Avoiding performance bottlenecks Failure Handling • Failure in DS is partial – some component fails  while the rest is functional; – Detecting failures (remote site crash or delay in  message transmission?) – Masking failures (message retransmission, file  replication) – Tolerance for failure (clients give up after a pre‐ determined number of attempts and take other  actions) – Failure recovery (checkpoint and rollback recovery) – Redundancy (multipath routing, replicated database,  replicated DNS)