Hwajung Lee ITEC452 Distributed Computing Lecture 6 Mutual Exclusion Sequential and concurrent events. Understanding logical clocks and vector clocks.

Physical clock synchronization
Question 1. Why is physical clock synchronization important? Question 2. With the price of atomic clocks or GPS coming down, should we care about physical clock synchronization?

Classification Types of Synchronization External Synchronization
Internal Synchronization Phase Synchronization Types of clocks Unbounded 0, 1, 2, 3, . . . Bounded 0,1, 2, M-1, 0, 1, . . . Unbounded clocks are not realistic, but are easier to deal with in the design of algorithms. Real clocks are always bounded.

Terminologies What are these? Drift rate  Clock skew 
Resynchronization interval R Max drift rate  implies: (1- ) ≤ dC/dt < (1+ ) Challenges (Drift is unavoidable) Accounting for propagation delay Accounting for processing delay Faulty clocks

Internal synchronization
Step 1. Read every clock in the system. Step 2. Discard outliers and substitute them by the value of the local clock. Step 3. Update the clock using the average of these values. Berkeley Algorithm A simple averaging algorithm that guarantees mutual consistency |c(i) - c(j)| < 

Lamport and Melliar-Smith’s averaging algorithm handles byzantine clocks too Assume n clocks, at most t are faulty Step 1. Read every clock in the system. Step 2. Discard outliers and substitute them by the value of the local clock. Step 3. Update the clock using the average of these values. Synchronization is maintained if n > 3t Why? Bad clock A faulty clocks exhibits 2-faced or byzantine behavior

Lamport & Melliar-Smith’s algorithm (continued) The maximum difference between the averages computed by two non-faulty nodes is (3td / n) To keep the clocks synchronized, 3td / n < d So, 3t < n k B a d c l o c k s

Cristian’s method External Synchronization
Client pulls data from a time server every R unit of time, where R <  / 2. For accuracy, clients must compute the round trip time (RTT), and compensate for this delay while adjusting their own clocks. (Too large RTT’s are rejected) Time server

Network Time Protocol (NTP)
Tiered architecture Broadcast mode - least accurate Procedure call - medium accuracy Peer-to-peer mode - upper level servers use this for max accuracy Time server Level 0 Level 1 Level 1 Level 1 Level 2 Level 2 Level 2 The tree can reconfigure itself if some node fails.

P2P mode of NTP  = (T2 -T4 -T1 +T3) / 2 - (TPQ - TQP) / 2
Let Q’s time be ahead of P’s time by . Then T2 = T1 + TPQ +  T4 = T3 + TQP -  y = TPQ + TQP = T2 +T4 -T1 -T3 (RTT)  = (T2 -T4 -T1 +T3) / 2 - (TPQ - TQP) / 2 T2 T3 Q P T1 T4 x Between y/2 and -y/2 So, x- y/2 ≤  ≤ x+ y/2 Ping several times, and obtain the smallest value of y. Use it to calculate 

Problems with Clock adjustment
1. What problems can occur when a clock value is Advanced from 171 to 174? 2. What problems can occur when a clock value is Moved back from 180 to 175? 1.What happened to the instant 172 and 173? 2. The instants appear twice

Mutual Exclusion CS p0 CS p1 p2 CS CS p3

Why mutual exclusion? Some applications are: Resource sharing
Avoiding concurrent update on shared data Controlling the grain of atomicity Medium Access Control in Ethernet Collision avoidance in wireless broadcasts

Specifications ME1. At most one process in the CS. (Safety property)
ME2. No deadlock. (Safety property) ME3. Every process trying to enter its CS must eventually succeed. This is called progress. (Liveness property) Progress is quantified by the criterion of bounded waiting. It measures a form of fairness by answering the question: Between two consecutive CS trips by one process, how many times other processes can enter the CS? There are many solutions, both on the shared memory model and the message-passing model

Message passing solution: Centralized decision making
Client do true  send request; reply received  enter CS; send release; <other work> od server busy: boolean queue release req Server do request received and not busy  send reply; busy:= true request received and busy  enqueue sender release received and queue is empty  busy:= false release received and queue not empty  send reply to the head of the queue od reply clients

Comments - Centralized solution is simple.
- But the server is a single point of failure. This is BAD. - ME1-ME3 is satisfied, but FIFO fairness is not guaranteed. Why? Can we do better? Yes!

Decentralized solution 1: Lamport’s algorithm
{Life of each process} 1. Broadcast a timestamped request to all. Request received  enqueue sender in local Q;. Not in CS  send ack In CS  postpone sending ack (until exit from CS). 3. Enter CS, when (i) You are at the head of your own local Q (ii) You have received ack from all processes 4. To exit from the CS, (i) Delete the request from Q, and (ii) Broadcast a timestamped release 5. Release received  remove sender from local Q. Completely connected topology Can you show that it satisfies all the properties (i.e. ME1, ME2, ME3) of a correct solution?

Decentralized solution 1
{Lamport’s algorithm} 1. Broadcast a timestamped request to all. 2. Request received  enqueue it in local Q. Not in CS  send ack, else postpone sending ack until exit from CS. 3. Enter CS, when (i) You are at the head of your Q (ii) You have received ack from all 4. To exit from the CS, (i) Delete the request from your Q, and (ii) Broadcast a timestamped release 5. When a process receives a release message, it removes the sender from its Q. Completely connected topology

Analysis of Lamport’s algorithm
Can you show that it satisfies all the properties (i.e. ME1, ME2, ME3) of a correct solution? Observation. Processes taking a decision to enter CS must have identical views of their local queues, when all acks have been received. Proof of ME1. At most one process can be in its CS at any time. Suppose not, and both j,k enter their CS. This implies  j in CS  Qj.ts.j < Qk.ts.k  k in CS  Qk.ts.k < Qj.ts.j Impossible.

Proof of ME2. (No deadlock) The waiting chain is acyclic. i waits for j i is behind j in all queues (or j is in its CS) j does not wait for i Proof of ME3. (progress) New requests join the end of the queues, so new requests do not pass the old ones

Proof of FIFO fairness. timestamp (j) < timestamp (k)  j enters its CS before k does so Suppose not. So, k enters its CS before j. So k did not receive j’s request. But k received the ack from j for its own req. This is impossible if the channels are FIFO . Message complexity = 3(N-1) (N-1 requests + N-1 ack + N-1 release) Req (30) j k ack Req (20)

Decentralized algorithm 2
{Ricart & Agrawala’s algorithm} What is new? 1. Broadcast a timestamped request to all. 2. Upon receiving a request, send ack if -You do not want to enter your CS, or -You are trying to enter your CS, but your timestamp is higher than that of the sender. (If you are already in CS, then buffer the request) 3. Enter CS, when you receive ack from all. 4. Upon exit from CS, send ack to each pending request before making a new request. (No release message is necessary)

Ricart & Agrawala’s algorithm
ME1. Prove that at most one process can be in CS. ME2. Prove that deadlock is not possible. ME3. Prove that FIFO fairness holds even if channels are not FIFO Message complexity = 2(N-1) (N-1 requests + N-1 acks - no release message) TS(j) < TS(k) Req(k) Ack(j) j k Req(j)

Unbounded timestamps Timestamps grow in an unbounded manner.
This makes real implementation impossible. Can we somehow bounded timestamps? Think about it.

Decentralized algorithm 3
{Maekawa’s algorithm} - First solution with a sublinear O(sqrt N) message complexity. - “Close to” Ricart-Agrawala’s solution, but each process is required to obtain permission from only a subset of peers

Maekawa’s algorithm With each process i, associate a subset Si.Divide the set of processes into subsets that satisfy the following two conditions: i  Si i,j :  i,j  n-1 :: Si Sj ≠  Main idea. Each process i is required to receive permission from Si only. Correctness requires that multiple processes will never receive permission from all members of their respective subsets. S1 S0 0,1,2 1,3,5 2,4,5 S2

Maekawa’s algorithm Example. Let there be seven processes 0, 1, 2, 3, 4, 5, 6 S0 = {0, 1, 2} S1 = {1, 3, 5} S2 = {2, 4, 5} S3 = {0, 3, 4} S4 = {1, 4, 6} S5 = {0, 5, 6} S6 = {2, 3, 6}

Maekawa’s algorithm Version 1 {Life of process I} 1. Send timestamped request to each process in Si. 2. Request received  send ack to process with the lowest timestamp. Thereafter, "lock" (i.e. commit) yourself to that process, and keep others waiting. 3. Enter CS if you receive an ack from each member in Si. 4. To exit CS, send release to every process in Si. 5. Release received  unlock yourself. Then send ack to the next process with the lowest timestamp. S0 = {0, 1, 2} S1 = {1, 3, 5} S2 = {2, 4, 5} S3 = {0, 3, 4} S4 = {1, 4, 6} S5 = {0, 5, 6} S6 = {2, 3, 6}

Maekawa’s algorithm-version 1
ME1. At most one process can enter its critical section at any time. Let i and j attempt to enter their Critical Sections Si Sj ≠  there is a process k  Si Sj Process k will never send ack to both. So it will act as the arbitrator and establishes ME1 S0 = {0, 1, 2} S1 = {1, 3, 5} S2 = {2, 4, 5} S3 = {0, 3, 4} S4 = {1, 4, 6} S5 = {0, 5, 6} S6 = {2, 3, 6}

Maekawa’s algorithm-version 1
ME2. No deadlock. Unfortunately deadlock is possible! Assume 0, 1, 2 want to enter their critical sections. From S0= {0,1,2}, 0,2 send ack to 0, but 1 sends ack to 1; From S1= {1,3,5}, 1,3 send ack to 1, but 5 sends ack to 2; From S2= {2,4,5}, 4,5 send ack to 2, but 2 sends ack to 0; Now, 0 waits for 1, 1 waits for 2, and 2 waits for 0. So deadlock is possible! S0 = {0, 1, 2} S1 = {1, 3, 5} S2 = {2, 4, 5} S3 = {0, 3, 4} S4 = {1, 4, 6} S5 = {0, 5, 6} S6 = {2, 3, 6}

Maekawa’s algorithm-Version 2
Avoiding deadlock If processes receive messages in increasing order of timestamp, then deadlock “could be” avoided. But this is too strong an assumption. Version 2 uses three additional messages: - failed - inquire - relinquish S0 = {0, 1, 2} S1 = {1, 3, 5} S2 = {2, 4, 5} S3 = {0, 3, 4} S4 = {1, 4, 6} S5 = {0, 5, 6} S6 = {2, 3, 6}

New features in version 2 Send ack and set lock as usual. If lock is set and a request with larger timestamp arrives, send failed (you have no chance). If the incoming request has a lower timestamp, then send inquire (are you in CS?) to the locked process. - Receive inquire and at least one failed message  send relinquish. The recipient resets the lock. S0 = {0, 1, 2} S1 = {1, 3, 5} S2 = {2, 4, 5} S3 = {0, 3, 4} S4 = {1, 4, 6} S5 = {0, 5, 6} S6 = {2, 3, 6}

Comments Let K = |Si|. Let each process be a member of D subsets. When N = 7, K = D = 3. When K=D, N = K(K-1)+1. So K is of the order √N - The message complexity of Version 1 is 3√N. Maekawa’s analysis of Version 2 reveals a complexity of 7√N Sanders identified a bug in version 2 …

Token-passing Algorithms
Suzuki-Kasami algorithm The Main idea Completely connected network of processes There is one token in the network. The holder of the token has the permission to enter CS. Any other process trying to enter CS must acquire that token. Thus the token will move from one process to another based on demand. I want to enter CS I want to enter CS

Suzuki-Kasami Algorithm
req last Process i broadcasts (i, num) Each process maintains -an array req: req[j] denotes the sequence no of the latest request from process j (Some requests will be stale soon) Additionally, the holder of the token maintains -an array last: last[j] denotes the sequence number of the latest visit to CS from for process j. - a queue Q of waiting processes req Sequence number of the request queue Q req req req req: array[0..n-1] of integer last: array [0..n-1] of integer

Suzuki-Kasami Algorithm
When a process i receives a request (k, num) from process k, it sets req[k] to max(req[k], num). The holder of the token --Completes its CS --Sets last[i]:= its own num --Updates Q by retaining each process k only if 1+ last[k] = req[k] (This guarantees the freshness of the request) --Sends the token to the head of Q, along with the array last and the tail of Q In fact, token  (Q, last) Req: array[0..n-1] of integer Last: Array [0..n-1] of integer

Suzuki-Kasami’s algorithm
{Program of process j} Initially, i: req[i] = last[i] = 0 * Entry protocol * req[j] := req[j] + 1 Send (j, req[j]) to all Wait until token (Q, last) arrives Critical Section * Exit protocol * last[j] := req[j] k ≠ j: k  Q  req[k] = last[k] + 1  append k to Q; if Q is not empty  send (tail-of-Q, last) to head-of-Q fi * Upon receiving a request (k, num) * req[k] := max(req[k], num)

Example 1 2 4 3 initial state req=[1,0,0,0,0] req=[1,0,0,0,0]
last=[0,0,0,0,0] 1 2 req=[1,0,0,0,0] 4 req=[1,0,0,0,0] 3 req=[1,0,0,0,0] initial state

Example 1 2 4 3 1 & 2 send requests req=[1,1,1,0,0] req=[1,1,1,0,0]
last=[0,0,0,0,0] 1 2 req=[1,1,1,0,0] 4 req=[1,1,1,0,0] 3 req=[1,1,1,0,0] 1 & 2 send requests

Example 1 2 4 3 0 prepares to exit CS req=[1,1,1,0,0] req=[1,1,1,0,0]
last=[1,0,0,0,0] Q=(1,2) 1 2 req=[1,1,1,0,0] 4 req=[1,1,1,0,0] 3 req=[1,1,1,0,0] 0 prepares to exit CS

Example 1 2 4 3 0 passes token (Q and last) to 1 req=[1,1,1,0,0]
2 req=[1,1,1,0,0] 4 req=[1,1,1,0,0] 3 req=[1,1,1,0,0] 0 passes token (Q and last) to 1

Example 1 2 4 3 0 and 3 send requests req=[2,1,1,1,0] last=[1,0,0,0,0]
2 req=[2,1,1,1,0] 4 req=[2,1,1,1,0] 3 req=[2,1,1,1,0] 0 and 3 send requests

Example 1 2 4 3 1 sends token to 2 req=[2,1,1,1,0] req=[2,1,1,1,0]
2 req=[2,1,1,1,0] last=[1,1,0,0,0] Q=(0,3) 4 req=[2,1,1,1,0] 3 req=[2,1,1,1,0] 1 sends token to 2

Raymond’s tree-based algorithm
1 4 1 4,7 1,4 1,4,7 want to enter their CS

Raymond’s Algorithm 1 4 1,4 4,7 2 sends the token to 6

Raymond’s Algorithm 6 forwards the token to 1 4 4 4 4,7
The message complexity is O(diameter) of the tree. Extensive empirical measurements show that the average diameter of randomly chosen trees of size n is O(log n). Therefore, the authors claim that the average message complexity is O(log n)

Distributed snapshot on the internet?
-- How many messages are in transit on the internet? -- What is the global state of a distributed system of N processes? How do we compute these?

One-dollar bank Let a $1 coin circulate in a network of a million banks. How can someone count the total $ in circulation? If not counted “properly,” then one may think the total $ in circulation to be one million.

Importance of snapshots
Major uses in - deadlock detection - termination detection - rollback recovery - global predicate computation

Consistent cut If this is not true, then the cut is inconsistent
A cut is a set of events. (a  consistent cut C)  (b happened before a)  b  C If this is not true, then the cut is inconsistent a b c g d P1 m e f P2 P3 k i h j Cut 1 Cut 2 (Consistent) (Not consistent)

Consistent snapshot The set of states immediately following a consistent cut forms a consistent snapshot of a distributed system. A snapshot that is of practical interest is the most recent one. Let C1 and C2 be two consistent cuts and C1  C2. Then C2 is more recent than C1. Analyze why certain cuts in the one-dollar bank are inconsistent.

Consistent snapshot How to record a consistent snapshot? Note that 1. The recording must be non-invasive 2. Recording must be done on-the-fly. You cannot stop the system.

Hwajung Lee ITEC452 Distributed Computing Lecture 6 Mutual Exclusion Sequential and concurrent events. Understanding logical clocks and vector clocks.

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Hwajung Lee ITEC452 Distributed Computing Lecture 6 Mutual Exclusion Sequential and concurrent events. Understanding logical clocks and vector clocks.

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