lecture 9
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Lecture 9. Other models: Monitoring models Reliability and fault-tolerance models Performance models. Scheduling policies. Security models. Student presentations and midterm. I expect a progress report the week after the Spring break (March 18 – 24). - PowerPoint PPT PresentationTRANSCRIPT
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Lecture 9
Other models:Monitoring modelsReliability and fault-tolerance modelsPerformance models. Scheduling policies.Security models
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Student presentations and midterm
I expect a progress report the week after the Spring break (March 18 – 24).
The final project report is due the week before last.
Midterm: two weeks from today – Material – Chapters 1,2, and 3 up to the last
lecture.Open book.3 questions: 30 minutes
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Monitoring models
A monitor could be a process responsible to establish the global state of a System.Intrusion – Heissenber’s uncertainty for quantum processes.Run: a total ordering of all events in the global history of a process.Cut: a subset of the local history of all processes.Frontier of a cut: the last event of every process in the cut.
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Consistent and inconsistent cuts
Consistent cut: a cut that agrees with causality.
Inconsistent cut: violates causality.
Causal history of an event: the smallest cut including the event.
The snapshot algorithm of Chandy and Lamport.
Checkpointing in parallel and distributed computing.
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Consistent and inconsistent cuts
m2m1
m3
p1
p2
p3
m4
m5
e1
1 e2
1 e3
1 e4
1 e5
1 e6
1
e1
2 e2
2 e3
2 e4
2
e1
3 e2
3 e3
3 e4
3 e5
3
e5
2 e6
2
C1 C 2
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Causal history
m2m1
m3
p1
p2
p3
m4
m5
e1
1 e2
1 e3
1 e4
1 e5
1 e6
1
e1
2 e2
2 e3
2 e4
2
e1
3 e2
3 e3
3 e4
3 e5
3
e5
2 e6
2
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The snapshot protocol (Chandy&Lamport) p0 p1
p5
p4 p3
p2
1 1 1
1
1
2 2
2
2
2
2
2
2
2 2
2
2
2 2 2
2
222
2
222
2
2
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Reliability and fault-tolerance models
A failure at time t is un undesirable event characterized by its:Manifestation – incorrect timing or value of
variablesConsistency – the system may fail in a
consistent or in an inconsistent state.Effects – benign/ malignOccurrence mode: singular or repeated
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Failure modes for processes [P] and for communication channels [C]
Crash - [P&C]
FailStop - [P]
Send Omissions - [P]
Receive omissions - [P]
General omissions – [P&C]
Byzantine – [P&C]
Arbitrary with message authentication - [P]
Timing – [P]
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Collective communication
Broadcast and multicast.
Applications:Routing in mobile ad hoc networks.Routing in the Internet to disseminate topological
information – flooding algorithms.Used to achieve consensus.Multicasting of audio and video streams to
reduce the bandwidth.Parallel algorithms – barrier synchronization.
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Collective communication
ApplicationProcess
CollectiveCommunication
Process
pi
ApplicationProcesspj
CollectiveCommunication
Process
RoutingProcess
CollectiveCommunication
Process
r i
Channel Channel
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Properties of a broadcast algorithm (I)
Validity – if a correct cc-process broadcasts a message m all correct cc-processes eventually deliver m.
Agreement - if a correct cc-process delivers message m all correct cc-processes eventually deliver m.
Integrity – every correct cc-process delivers m once and only once and only if the message was broadcast by a cc-process
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Properties of a broadcast algorithm (II)FIFO order – if a correct cc-process broadcasts a message m before m’ then no correct cc-processes delivers m’ unless it has previously delivered m.
Causal order - if a correct cc-process broadcasts m that causally precedes m’ then no correct cc-processes delivers m’ unless it has previously delivered m.
Total order – if two correct cc-processes p and q both deliver messages m and m’ then p delivers m before m’ if and only if q delivers m before m’.
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Broadcast primitives and their relationships
Total Order
ReliableBroadcast
FIFOBroadcast
CausalBroadcast
FIFO Order
Causal Order
AtomicBroadcast
FIFO AtomicBroadcast
Causal AtomicBroadcast
FIFO Order
Causal Order
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Performance modelsResource sharing!!!
Arrival process – distribution of inter-arrival times or arrival rates.
Service process – distribution of service times or inter-departure times.
Number of servers
Quantities of interest: Time in system, T Waiting time W Number in system, N
Little’s law: N = T
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0 1 2 k k+1k-1.............. ..............
(a)
(b)
S
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Performance models
Types of systemsDeterministic D/D/1Markov arrival, Markov service - M/M/1 Markov arrival, general service – M/G/1Batch arrival.
Server utilization : ratio of arrival rate to service rate.Stability: <= 1 necessary but not sufficient Time in system is finiteNumber in system is finite
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Performance models
When utilization tends to 1 time in system becomes unbounded.
Network congestion.
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1
1
S
Tc
Mw
(b)
T
1
(a)
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C/2C/2
throughput perconnection
delay
input data rateper connection
input data rateper connection
H1H2
b
c
d e fa
chin1
chin2
chout
Internet
C
(a)
(b) (c)
H1, H2 - hosts;a, f - edge routers;b,c,d,e - internal routers;chin1- communication channel from b to d;chin2- communication channel from c to d;chout - communication channel from d to e, with capacity C;two conections: - one from b to e, through d; - one from c to e, through d;
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Scheduling policies/algorithmsStatic/Dynamic algorithms
Centralized/Distributed
Policies:FCFSLCFSPriorityRound-RobinWeighted Fair Queuing
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Service policies for the server with vacation model
Exhaustive
Gated
Semi-gated
K-limitted
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Scheduling on a grid
Resources under the control of different administrative authorities.
Resource reservations.
Market-based scheduling algorithms.
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Scheduling on a grid
Internet
ResourceAgent
Resource
ResourceAgent
Resource
ResourceAgent
Resource
ResourceAgent
Resource
ResourceAgent
Resource
Process Process Process ProcessProcessProcessProcess
Scheduler
Scheduler
Scheduler
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Security models
Problems and solutions:Confidentiality encriptionAuthentication authentication servicesAuthorization (controlled access to system
resources) access control
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Secret key and public key cryptographyPlaintext
Ciphertext
Encrypt withsecret key
Decrypt withsecret key
(a)
Encrypt withpublic key of the
recipient
Decrypt with theprivate key of the
recipient
(b)
Plaintext
Ciphertext
Plaintext
Plaintext
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Major challenges in distributed systems
Concurrency
Mobility
28Observational
ModelsDenotational
Models
InterleavedModels
TrueConcurrency
Models
LinearModels
BranchingTime
Models
VirtualMobilityModels
PhysicalMobilityModels
Observability
Type ofConcurrency
Description
Distributed SystemsModels
Concurrency Mobility