TDDD82 Secure Mobile Systems Lecture 1: Introduction and Distributed Systems Models

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1 TDDD82 Secure Mobile Systems Lecture 1: Introduction and Distributed Systems Models Mikael Asplund Real-time Systems Laboratory Department of Computer and Information Science Linköping University Based on slides by Simin Nadjm-Tehrani

2 People & organisation Lecturer: Mikael Asplund Lessons: Chih-Yuan (Sana) Lin (2 scheduled occasions)

3 Module overview 3hp Some parts that strongly relate to your projects Distributed systems, dependability, qualityof-service General CS knowledge: concurrent programming Processes, resource sharing, deadlocks

4 Lecture organisation Lecture 1: Distributed systems (intro) Lecture 2-4:Processes All concurrency related topics, including synchronisation, mutual exclusion, deadlocks Lecture 5: Dependability Lecture 6: Quality of Service

5 Distributed systems

6 Reading Chapter 2 of Coulouris, Dollimore, and Kindberg

7 Examples

8 Common in all these? Distributed model of computing: Multiple processes Disjoint address spaces Inter-process communication Collective goal

9 Distributed Systems A collection of independent computers that appears to its users as a single coherent system

10 Networking vs. Distributed systems Networking treats the internal mechanisms for inter-process communication: Routing Error control (reliable transmission) Flow control (low level treatment of overloads) Distributed computing treats the application view of the architecture for communication and cooperation

11 This lecture Basic aspects affecting design Distributed systems architectures and models

12 Overview Why is it hard to get it right? Variations in workload, connectivity, mobility, requirements Heterogeneity in systems environment, hardware, operating systems, and networks Consequences of timing and failure issues Security threats, and distributed attacks

13 Architectural models Placement of processes and data across a network of computers Patterns of communication to achieve functional and extra(non)-functional properties Challenges: Scalability, interoperability

14 Architectural models Placement of processes and data across a network of computers Patterns of communication to achieve functional and extra(non)-functional properties Challenges: Scalability, interoperability What are these?

15 System requirements Functional requirements Describe the main objectives of the system, also referred to as correct service Extra-functional requirements Also called non-functional properties Cover other requirements than those relating to main function, for example expressing the frequency and severity of acceptable service failures Example non-functional requirements Timeliness, availability, energy efficiency

16 Scalability Allow the system to become bigger without negatively affecting performance Multiple dimensions: Size: Adding more resources and users Geographic: Dispersed across locations Administrative: Spanning multiple administrative domains 16

17 Architectural roles Client-server Client implements the user interface Server(s) has most of the functionality Computation, data E.g.: Web Peer-to-peer (P2P) Each component is symmetric in functionality Peer: Combination of server-client No well-known centralized server Hybrid Combination of the two

18 System organisation Centralised Most functionality is in a single unit Decentralised Functionality is spread across multiple units

19 Types of distribution Vertical distribution Logically different components on different machines e.g., multitiered architectures Horizonal distribution Multiple logically equivalent parts Potentially operating on different data

20 Physical two-tired architectures Alternative client-server organizations (a) (e)

21 Exaple of horizontal distribution 1-31 An example of horizontal distribution of a Web service. 21

22 A taxonomy of architectural models Distributed systems Client-server Peer-to-peer Hybrid... Centralised Decentralised Decentralised & horizontally distributed Vertically distributed Vertically distributed Horizontally distributed Horiz. & vert. distributed

23 Interaction Interaction

24 What affects timing in a distributed system?

25 Latency

26 Clock drift C (t) > 1 (fast clock) C (t)=1 (Perfect clock) Timestamp of clock C C (t) < 1 (slow clock) reference time t Baspresentation LiU

27 Clock drift C (t) > 1 (fast clock) Real clock C (t)=1 (Perfect clock) Timestamp of clock C C (t) < 1 (slow clock) reference time t Baspresentation LiU

28 Two interaction models Asynchronous No relation between computation rate at different nodes, No bound on message exchange delay, Clock drift rates are arbitrary Synchronous Bounded message exchange delay, Related processing rates at different nodes, Clock drift rates bounded

Implications Synchronous: Local clocks can be used to implement timeouts Lack of response from another node can be interpreted as detection of

29 Implications Synchronous: Local clocks can be used to implement timeouts Lack of response from another node can be interpreted as detection of failure Hard to guarantee! Asynchronous: In the absence of global (synchronised) time one cannot relate clocks at different nodes How can events be ordered?

30 When order matters

31 Another problem: global state P1 Time P2 m1 m3 P3 m2

32 Another problem: global state P1 Time P2 m1 m3 P3 m2

33 Causal ordering A strict partial order Antisymmetrical Transitive Irreflexive Also known as: the happened-before relation Rules: send(m) receive(m) e1 e2 if e1 and e2 are local events on the same machine and e1 occurred before e2 according to the local time Transitive closure

34 Consistent cuts (using partial order) If e2 is after the cut and e1 before the cut, then e2 e1 P1 Time P2 m1 m3 P3 m2

35 Consistent cuts (using partial order) If e2 is after the cut and e1 before the cut, then e2 e1 P1 Time P2 m1 m3 P3 m2

36 Consistent cuts (using partial order) If e2 is after the cut and e1 before the cut, then e2 e1 P1 Time P2 m1 m3 P3 m2 Consistent!

37 Consistent cuts (using partial order) If e2 is after the cut and e1 before the cut, then e2 e1 P1 Time P2 m1 m3 P3 m2 Consistent!

38 Consistent cuts (using partial order) If e2 is after the cut and e1 before the cut, then e2 e1 P1 Time P2 m1 m3 P3 m2 Inconsistent! Consistent!

39 Lamport timestamps Timestamps should follow the partial event ordering A B => C(A) < C(B) Not vice versa! Timestamps always increase Lamport s Algorithm: Each processor i maintains a logical clock Ci Whenever an event occurs locally, Ci = Ci+1 When i sends message to j, piggyback Ci When j receives message from i Cj = max(ci, Cj)+1

40 Failure Failure

41 Failure models We will look into more detail into failure and related notions in lecture 5 For now... Distributed systems can fail in nodes or channels Node/channel failures: Crash Omission timing Byzantine (arbitrary)

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