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COMMUNICATION PRIMITIVES Of distributed systems .ppt

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The document covers multicast communication in distributed systems, highlighting its importance in cloud systems and applications like online scoreboards and stock exchanges. It explains three key ordering types: FIFO, causal, and total ordering, along with their implementations and the concept of reliable multicast, especially in the presence of failures. Additionally, the document discusses methods to ensure reliable delivery, emphasizing the role of receivers in the multicast process.

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CS 425 / ECE 428 Distributed Systems Fall 2023 Indranil Gupta (Indy) W/ Aishwarya Ganesan Lecture 16: Multicast All slides © IG
Multicast Problem 2
• Multicast  message sent to a group of processes • Broadcast  message sent to all processes (anywhere) • Unicast  message sent from one sender process to one receiver process Other Communication Forms 3
• A widely-used abstraction by almost all cloud systems • Storage systems like Cassandra or a database – Replica servers for a key: Writes/reads to the key are multicast within the replica group – All servers: membership information (e.g., heartbeats) is multicast across all servers in cluster • Online scoreboards (ESPN, French Open, FIFA World Cup) – Multicast to group of clients interested in the scores • Stock Exchanges – Group is the set of broker computers – Groups of computers for High frequency Trading • Air traffic control system – All controllers need to receive the same updates in the same order Who Uses Multicast? 4
• Determines the meaning of “same order” of multicast delivery at different processes in the group • Three popular flavors implemented by several multicast protocols 1. FIFO ordering 2. Causal ordering 3. Total ordering Multicast Ordering 5
• Multicasts from each sender are received in the order they are sent, at all receivers • Don’t worry about multicasts from different senders • More formally – If a correct process issues (sends) multicast(g,m) to group g and then multicast(g,m’), then every correct process that delivers m’ would already have delivered m. 1. FIFO ordering 6
M1:1 and M1:2 should be received in that order at each receiver Order of delivery of M3:1 and M1:2 could be different at different receivers FIFO Ordering: Example P2 Time P1 P3 M1:1 M1:2 P4 M3:1
• Multicasts whose send events are causally related, must be received in the same causality-obeying order at all receivers • Formally – If multicast(g,m)  multicast(g,m’) then any correct process that delivers m’would already have delivered m. – ( is Lamport’s happens-before) 2. Causal Ordering 8
M3:1  M3:2, and so should be received in that order at each receiver M1:1  M3:1, and so should be received in that order at each receiver M3:1 and M2:1 are concurrent and thus ok to be received in different orders at different receivers Causal Ordering: Example P2 Time P1 P3 M1:1 P4 M3:1 M3:2 M2:1
• Causal Ordering => FIFO Ordering • Why? – If two multicasts M and M’ are sent by the same process P, and M was sent before M’, then M  M’ – Then a multicast protocol that implements causal ordering will obey FIFO ordering since M  M’ • Reverse is not true! FIFO ordering does not imply causal ordering. Causal vs. FIFO 10
• Group = set of your friends on a social network • A friend sees your message m, and she posts a response (comment) m’ to it – If friends receive m’ before m, it wouldn’t make sense – But if two friends post messages m” and n” concurrently, then they can be seen in any order at receivers • A variety of systems implement causal ordering: Social networks, bulletin boards, comments on websites, etc. Why Causal at All? 11
• Also known as “Atomic Broadcast” • Unlike FIFO and causal, this does not pay attention to order of multicast sending • Ensures all receivers receive all multicasts in the same order • Formally – If a correct process P delivers message m before m’ (independent of the senders), then any other correct process P’ that delivers m’ would already have delivered m. 3. Total Ordering 12
The order of receipt of multicasts is the same at all processes. M1:1, then M2:1, then M3:1, then M3:2 May need to delay delivery of some messages Total Ordering: Example P2 Time P1 P3 M1:1 P4 M3:1 M3:2 M2:1 13
• Since FIFO/Causal are orthogonal to Total, can have hybrid ordering protocols too – FIFO-total hybrid protocol satisfies both FIFO and total orders – Causal-total hybrid protocol satisfies both Causal and total orders Hybrid Variants 14
• That was what ordering is • But how do we implement each of these orderings? Implementation? 15
• Each receiver maintains a per-sender sequence number (integers) – Processes P1 through PN – Pi maintains a vector of sequence numbers Pi[1…N] (initially all zeroes) – Pi[j] is the latest sequence number Pi has received from Pj FIFO Multicast: Data Structures 16
• Send multicast at process Pj: – Set Pj[j] = Pj[j] + 1 – Include new Pj[j] in multicast message as its sequence number • Receive multicast: If Pi receives a multicast from Pj with sequence number S in message – if (S == Pi[j] + 1) then • deliver message to application • Set Pi[j] = Pi[j] + 1 – else buffer this multicast until above condition is true FIFO Multicast: Updating Rules 17
P2 Time P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] FIFO Ordering: Example 18
P2 Time P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] Deliver! P1, seq: 1 [1,0,0,0] Deliver! ? [1,0,0,0] FIFO Ordering: Example
P2 Time P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] Deliver! P1, seq: 1 [1,0,0,0] Deliver! [0,0,0,0] Buffer! P1, seq: 2 [1,0,0,0] [2,0,0,0] FIFO Ordering: Example [1,0,0,0] Deliver this! Deliver buffered <P1, seq:2> Update [2,0,0,0]
P2 Time P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] Deliver! P1, seq: 1 [1,0,0,0] Deliver! [0,0,0,0] Buffer! P1, seq: 2 [1,0,0,0] [2,0,0,0] [2,0,0,0] Deliver! [1,0,0,0] Deliver this! Deliver buffered <P1, seq:2> Update [2,0,0,0] FIFO Ordering: Example
P2 Time P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] Deliver! P1, seq: 1 [1,0,0,0] Deliver! [0,0,0,0] Buffer! P1, seq: 2 [1,0,0,0] [2,0,0,0] [2,0,0,0] Deliver! [1,0,0,0] Deliver this! Deliver buffered <P1, seq:2> Update [2,0,0,0] P3, seq: 1 [2,0,1,0] [2,0,1,0] Deliver! [2,0,1,0] Deliver! ? FIFO Ordering: Example
P2 Time P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] Deliver! P1, seq: 1 [1,0,0,0] Deliver! [0,0,0,0] Buffer! P1, seq: 2 [1,0,0,0] [2,0,0,0] [2,0,0,0] Deliver! [1,0,0,0] Deliver this! Deliver buffered <P1, seq:2> Update [2,0,0,0] P3, seq: 1 [2,0,1,0] [2,0,1,0] Deliver! [2,0,1,0] Deliver! [1,0,1,0] Deliver! [2,0,1,0] Deliver! FIFO Ordering: Example
• Ensures all receivers receive all multicasts in the same order • Formally – If a correct process P delivers message m before m’ (independent of the senders), then any other correct process P’ that delivers m’ would already have delivered m. Total Ordering 24
• Special process elected as leader or sequencer • Send multicast at process Pi: – Send multicast message M to group and sequencer • Sequencer: – Maintains a global sequence number S (initially 0) – When it receives a multicast message M, it sets S = S + 1, and multicasts <M, S> • Receive multicast at process Pi: – Pi maintains a local received global sequence number Si (initially 0) – If Pi receives a multicast M from Pj, it buffers it until it both 1. Pi receives <M, S(M)> from sequencer, and 2. Si + 1 = S(M) • Then deliver message M to application and set Si = Si + 1 Sequencer-based Approach 25
• Multicasts whose send events are causally related, must be received in the same causality-obeying order at all receivers • Formally – If multicast(g,m)  multicast(g,m’) then any correct process that delivers m’ would already have delivered m. – ( is Lamport’s happens-before) Causal Ordering 26
• Each receiver maintains a vector of per-sender sequence numbers (integers) – Similar to FIFO Multicast, but updating rules are different – Processes P1 through PN – Pi maintains a vector Pi[1…N] (initially all zeroes) – Pi[j] is the latest sequence number Pi has received from Pj Causal Multicast: Datastructures 27
• Send multicast at process Pj: – Set Pj[j] = Pj[j] + 1 – Include new entire vector Pj[1…N] in multicast message as its sequence number • Receive multicast: If Pi receives a multicast from Pj with vector M[1…N] (= Pj[1…N]) in message, buffer it until both: 1. This message is the next one Pi is expecting from Pj, i.e., • M[j] = Pi[j] + 1 2. All multicasts, anywhere in the group, which happened-before M have been received at Pi, i.e., • For all k ≠ j: M[k] ≤ Pi[k] • i.e., Receiver satisfies causality 3. When above two conditions satisfied, deliver M to application and set Pi[j] = M[j] Causal Multicast: Updating Rules 28
Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Causal Ordering: Example 29
Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Deliver! [1,0,0,0] Deliver! [1,1,0,0] Causal Ordering: Example
Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Deliver! [1,0,0,0] Deliver! [1,1,0,0] [1,1,0,0] Deliver! Missing 1 from P1 Buffer! Causal Ordering: Example
Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Deliver! [1,0,0,0] Deliver! [1,1,0,0] [1,1,0,0] Deliver! Missing 1 from P1 Buffer! [1,0,0,1] Deliver! Receiver satisfies causality Deliver! Receiver satisfies causality Causal Ordering: Example
Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Deliver! [1,0,0,0] Deliver! [1,1,0,0] [1,1,0,0] Deliver! Missing 1 from P1 Buffer! [1,0,0,1] Deliver! Receiver satisfies causality Deliver! Receiver satisfies causality Missing 1 from P1 Buffer! Causal Ordering: Example
Causal Ordering: Example Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Deliver! [1,0,0,0] Deliver! [1,1,0,0] [1,1,0,0] Deliver! Missing 1 from P1 Buffer! [1,0,0,1] Deliver! Receiver satisfies causality Deliver! Receiver satisfies causality Missing 1 from P1 Buffer! Deliver P1’s multicast Receiver satisfies causality for buffered multicasts Deliver P2’s buffered multicast Deliver P4’s buffered multicast
Causal Ordering: Example Time P2 P1 P3 P4 [0,0,0,0] [0,0,0,0] [0,0,0,0] [0,0,0,0] [1,0,0,0] [1,0,0,0] Deliver! [1,0,0,0] Deliver! [1,1,0,0] [1,1,0,0] Deliver! Missing 1 from P1 Buffer! [1,0,0,1] Deliver! Receiver satisfies causality Deliver! Receiver satisfies causality Missing 1 from P1 Buffer! Deliver P1’s multicast Receiver satisfies causality for buffered multicasts Deliver P2’s buffered multicast Deliver P4’s buffered multicast Deliver!
• Ordering of multicasts affects correctness of distributed systems using multicasts • Three popular ways of implementing ordering – FIFO, Causal, Total • And their implementations • What about reliability of multicasts? • What about failures? Summary: Multicast Ordering 36
• Reliable multicast loosely says that every process in the group receives all multicasts – Reliability is orthogonal to ordering – Can implement Reliable-FIFO, or Reliable-Causal, or Reliable-Total, or Reliable-Hybrid protocols • What about process failures? • Definition becomes vague Reliable Multicast 37
• Need all correct (i.e., non- faulty) processes to receive the same set of multicasts as all other correct processes – Faulty processes stop anyway, so we won’t worry about them Reliable Multicast (under failures) 38
• Let’s assume we have reliable unicast (e.g., TCP) available to us • First-cut: Sender process (of each multicast M) sequentially sends a reliable unicast message to all group recipients • First-cut protocol does not satisfy reliability – If sender fails, some correct processes might receive multicast M, while other correct processes might not receive M Implementing Reliable Multicast 39
• Trick: Have receivers help the sender 1. Sender process (of each multicast M) sequentially sends a reliable unicast message to all group recipients 2. When a receiver receives multicast M, it also sequentially sends M to all the group’s processes REALLY Implementing Reliable Multicast 40
• Not the most efficient multicast protocol, but reliable • Proof is by contradiction • Assume two correct processes Pi and Pj are so that Pi received a multicast M and Pj did not receive that multicast M – Then Pi would have sequentially sent the multicast M to all group members, including Pj, and Pj would have received M – A contradiction – Hence our initial assumption must be false – Hence protocol preserves reliability Analysis 41
• Attempts to preserve multicast ordering and reliability in spite of failures • Combines a membership protocol with a multicast protocol • Systems that implemented it (like Isis Systems) have been used in NYSE, French Air Traffic Control System, Swiss Stock Exchange Virtual Synchrony or View Synchrony 42
• Each process maintains a membership list • The membership list is called a View • An update to the membership list is called a View Change – Process join, leave, or failure • Virtual synchrony guarantees that all view changes are delivered in the same order at all correct processes – If a correct P1 process receives views, say {P1}, {P1, P2, P3}, {P1, P2}, {P1, P2, P4} then – Any other correct process receives the same sequence of view changes (after it joins the group) • P2 receives views {P1, P2, P3}, {P1, P2}, {P1, P2, P4} • Views may be delivered at different physical times at processes, but they are delivered in the same order Views 43
• A multicast M is said to be “delivered in a view V at process Pi” if – Pi receives view V, and then sometime before Pi receives the next view it delivers multicast M • Virtual synchrony ensures that 1. The set of multicasts delivered in a given view is the same set at all correct processes that were in that view • What happens in a View, stays in that View 2. The sender of the multicast message (and the send event) also belongs to that view 3. If a process Pi does not deliver a multicast M in view V while other processes in the view V delivered M in V, then Pi will be forcibly removed from the next view delivered after V at the other processes VSync Multicasts 44
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 M3 Satisfies virtual synchrony 45
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 M3 Does not satisfy virtual synchrony Crash 46
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2} View{P1,P2} M1 M2 M3 Satisfies virtual synchrony Crash 47
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 M3 Does not satisfy virtual synchrony 48
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 (not delivered at P2) M3 Satisfies virtual synchrony 49
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 M3 Does not satisfy virtual synchrony 50
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 M3 Does not satisfy virtual synchrony 51
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1,P2,P3} View{P1,P2,P3} View{P1,P2,P3} M1 M2 M3 Satisfies virtual synchrony 52
• Again, orthogonal to virtual synchrony • The set of multicasts delivered in a view can be ordered either – FIFO – Or Causally – Or Totally – Or using a hybrid scheme What about Multicast Ordering? 53
• Called “virtual synchrony” since in spite of running on an asynchronous network, it gives the appearance of a synchronous network underneath that obeys the same ordering at all processes • So can this virtually synchronous system be used to implement consensus? • No! VSync groups susceptible to partitioning – E.g., due to inaccurate failure detections About that name 54
Time P2 P1 P3 P4 View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} View{P1,P2,P3,P4} Crash View{P1} View{P2, P3} View{P2,P3} M1 M2 M3 Partitioning in View synchronous systems 55
• Multicast an important building block for cloud computing systems • Depending on application need, can implement – Ordering – Reliability – Virtual synchrony Summary 56
Announcements • HW3, MP3 released. Start now! • Midterms handed back Fri 10/11. • Deadline for regrades: 10/18 • Final exam: 12/12 7-10PM – Have a conflict with another final? Fill out the form on Piazza by TODAY! – (travel, interviews, etc., are not conflicts) 57

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COMMUNICATION PRIMITIVES Of distributed systems .ppt