Declarative, Secure, Convergent, Edge Computation

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Declarative, Secure, Convergent Edge Computation Christopher Meiklejohn Erlang Factory 2016, March 10th, 2016 1

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Example Application Hospital Refrigerators 2

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Hospital Refrigerators Typical Topology 3

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Internet 5

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Internet HDFS 6

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Internet HDFS Hadoop Client Client Internet 7

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Internet HDFS Hadoop Client Client Internet Spark Client Client Internet 8

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Hospital Refrigerators Ideal Execution 9

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Internet HDFS Spark 10

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Internet HDFS Spark 35F 11

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Internet HDFS Spark 35F 12

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Internet HDFS Spark 35F 40F 13

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Internet HDFS Spark 35F 40F 14

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Client Internet HDFS Spark 35F 40F 15

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Problem Connectivity 16

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Internet HDFS Spark 17

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Internet HDFS Spark 35F 18

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Internet HDFS Spark 35F 19

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Internet HDFS Spark 35F 40F 20

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Internet HDFS Spark 35F 40F 21

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Internet HDFS Spark 35F 40F 40F 22

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Internet HDFS Spark 35F 40F 40F 23

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Solution Local Decisions 24

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Internet HDFS Spark 25

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Internet HDFS Spark 35F 26

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Internet HDFS Spark 35F 27

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Internet HDFS Spark 35F 40F 28

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Internet HDFS Spark 35F 40F 29

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Solution Transitive Dissemination 30

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Internet HDFS Spark 31

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Internet HDFS Spark 35F 32

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Internet HDFS Spark 35F 33

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Internet HDFS Spark 35F 40F 34

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Internet HDFS Spark 35F 40F 35

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Internet HDFS Spark 35F 40F Client 36

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Problem State Transmission 37

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Internet 38

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Internet 35F 39

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Internet 35F 35F 35F 35F 40

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Internet 35F 35F 35F 35F 41

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Solution Aggregate Dissemination 42

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Internet 43

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Internet OK NO OK 44

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Internet OK OK OK OK OK NO OK OK OK 45

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Internet OK OK OK OK OK NO OK OK OK HDFS OK OK OK OK OK OK OK OK NO 46

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Internet OK OK OK OK OK NO OK OK OK OK OK OK OK OK OK OK OK NO = = HDFS OK OK OK OK OK OK OK NO 47

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Internet OK OK OK OK OK NO OK OK OK ? OK OK OK OK OK OK OK OK NO = = 48

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Internet OK OK OK OK OK NO OK OK OK ? OK OK OK OK OK OK OK OK NO = = 49

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Local Computation • Reduce state transmission  Perform some local computation to reduce transmitted state on the wire 50

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Local Computation • Reduce state transmission  Perform some local computation to reduce transmitted state on the wire • Make local decisions  Make decisions based on results of local computation 50

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Databases Consistency Models 51

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Databases Strong Consistency 52

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R1 C1 C2 53

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R1 C1 C2 Value 1 54

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R1 C1 C2 Read Value 1 Value 1 Value 1 55

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R1 C1 C2 Read Value 1 Value 1 Value 1 Value 1 56

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R1 C1 C2 CAS Value 1 with Value 2 Value 2 Value 1 Value 2 57

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R1 C1 C2 CAS Value 1 with Value 3 Value 2 Value 1 Value 2 58

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I won’t diagram the Paxos protocol 59

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R2 C1 C2 Value 2 Value 1 Value 2 R1 R3 Paxos 60

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Databases Eventual Consistency 61

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R1 R2 R3 C1 C2 62

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R1 R2 R3 C1 C2 Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] 63

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R1 R2 R3 C1 C2 Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] 64

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R1 R2 R3 C1 C2 Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] 65

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R1 R2 R3 C1 C2 Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Value 1 @ [1, 0, 0] Write 2 66

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R1 R2 R3 C1 C2 Value 2 @ [2, 0, 0] Value 2 @ [2, 0, 0] Value 2 @ [2, 0, 0] Value 2 @ [2, 0, 0] Value 1 @ [1, 0, 0] Write 2 Write Value 2 @ [2, 0, 0] 67

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R1 R2 R3 C1 C2 Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Write 3 68

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R1 R2 R3 C1 C2 Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] 69

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R1 R2 R3 C1 C2 Value 4 @ [2, 0, 1] Value 4 @ [2, 0, 1] Value 4 @ [2, 0, 1] Value 4 @ [2, 0, 1] Value 2 @ [2, 0, 0] Value 3 @ [1, 0, 1] Write 4 70

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Eventual Consistency As The Model 71

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Clients Own Their Data 72

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OK OK OK 73

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OK OK OK OK OK OK OK OK OK 74

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OK OK OK OK OK OK OK OK OK 75

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OK OK OK OK OK OK OK 76

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OK OK OK OK OK OK OK OK OK 77

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Computations Mergability & Provenance 78

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A A B C 79

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A A B C D’ D’’ F F 80

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A A B C D’ D’’ D’ D’’ D   F F 81

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D’’ A A B C D’ D’ D’’ D   D D Merge F F 82

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Example Application Preliminary Results 83

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Preliminary Results • Conﬂict-Free Replicated Data Types  Distributed data structures designed for convergence   [Shapiro et al., 2011] 84

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Preliminary Results • Conﬂict-Free Replicated Data Types  Distributed data structures designed for convergence   [Shapiro et al., 2011] • Lattice Processing  Make decisions based on results of local computation  [Meiklejohn & Van Roy, 2015] 84

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Conﬂict-Free   Replicated Data Types • Collection of types  Sets, counters, registers, ﬂags, maps 85

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Conﬂict-Free   Replicated Data Types • Collection of types  Sets, counters, registers, ﬂags, maps • Strong Eventual Consistency  Objects that receive the same updates, regardless of order, will reach equivalent state 85

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RA RB RC

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RA RB RC {1} (1, {a}, {}) add(1)

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RA RB RC {1} (1, {a}, {}) add(1) {1} (1, {c}, {}) add(1)

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RA RB RC {1} (1, {a}, {}) add(1) {1} (1, {c}, {}) add(1) {} (1, {c}, {c}) remove(1)

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RA RB RC {1} (1, {a}, {}) add(1) {1} (1, {c}, {}) add(1) {} (1, {c}, {c}) remove(1) {1} {1} {1} (1, {a, c}, {c}) (1, {a, c}, {c}) (1, {a, c}, {c})

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Lattice Processing • Distributed, deterministic dataﬂow  Distributed, dataﬂow programming model 91

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Lattice Processing • Distributed, deterministic dataﬂow  Distributed, dataﬂow programming model • Convergent data structures  Data abstraction is the CRDT 91

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Lattice Processing • Distributed, deterministic dataﬂow  Distributed, dataﬂow programming model • Convergent data structures  Data abstraction is the CRDT • Enables composition  Composition preserves SEC 91

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92 %% Create initial set. S1 = declare(set), %% Add elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

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93 %% Create initial set. S1 = declare(set), %% Add elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

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94 %% Create initial set. S1 = declare(set), %% Add elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

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95 %% Create initial set. S1 = declare(set), %% Add elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

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96 %% Create initial set. S1 = declare(set), %% Add elements to initial set and update. update(S1, {add, [1,2,3]}), %% Create second set. S2 = declare(set), %% Apply map operation between S1 and S2. map(S1, fun(X) -> X * 2 end, S2).

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Selective Hearing • Epidemic broadcast protocol  Runtime system for application state & scope 97

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Selective Hearing • Epidemic broadcast protocol  Runtime system for application state & scope • Peer-to-peer dissemination  Pairwise synchronization between peers without a central coordinator • No ordering guarantees on messages  Programming model can tolerate message reordering and duplication 97

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What can we build? Leaderboard 98

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Leaderboard • Mobile game platform  Local leaderboard tracking top-k highest scored games 99

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Leaderboard • Mobile game platform  Local leaderboard tracking top-k highest scored games • Clients will go oﬄine  Clients have limited connectivity and the system still needs to make progress while clients are ofﬂine 99

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Client 1 Leaderboard Client 3 Leaderboard Client 2 Leaderboard Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT 100

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Leaderboard • Peer-to-peer dissemination  Nodes periodically “merge” their state with a random peer 101

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Leaderboard • Peer-to-peer dissemination  Nodes periodically “merge” their state with a random peer • Complexity in the data type  Each node tracks a top-k set of its own games in a bounded set 101

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102 %% Create a leaderboard datatype. L = declare({top_k, [2]}). %% Update leaderboard. update({set, Name, Score}, L).

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103 %% Create a leaderboard datatype. L = declare({top_k, [2]}). %% Update leaderboard. update({set, Name, Score}, L).

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104 %% Create a leaderboard datatype. L = declare({top_k, [2]}). %% Update leaderboard. update({set, Name, Score}, L).

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What if we want to enhance the behavior? 105

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What if we want to enhance the behavior? 105 Without the creation of a new datatype

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What can we build? Per-User Leaderboard 106 EdgeCom 2016

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Per-User Leaderboard • Enhance existing design  Only the top score for each user at each device 107

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Per-User Leaderboard • Enhance existing design  Only the top score for each user at each device • Minimize transmitted state  Prevent transmission of state that is not necessary to perform the computation • Compose data types  Build a per-user leaderboard through the composition of existing types 107

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Client1 Scores Local Top-K Fold Global Top-K Fold 109 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Client1 Scores Local Top-K Fold Global Top-K Fold 110 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Client1 Scores Local Top-K Fold Global Top-K Fold 111 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Client1 Scores Local Top-K Fold Global Top-K Fold 112 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Client1 Scores Local Top-K Fold Global Top-K Fold 113 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Client1 Scores Local Top-K Fold Global Top-K Fold 114 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Client1 Scores Local Top-K Fold Global Top-K Lasp Operation Input User-Maintained CRDT Output Lasp-Maintained CRDT Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold 115 Client1 Scores Local Top-K Fold Global Top-K Fold Client3 Scores Local Top-K Fold Global Top-K Fold Client2 Scores Local Top-K Fold Global Top-K Fold

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Per-User Leaderboard • Dynamically scoped variables  Variable which take different values depending on where it is executing 116

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Per-User Leaderboard • Dynamically scoped variables  Variable which take different values depending on where it is executing • Dynamically scoped fold operation  Perform a distributed “reduce” operation that combines the state of a dynamically scoped variables across 116

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117 %% Create a global leaderboard. G = declare({top_k, [10]}). %% Create a local leaderboard. L = declare_dynamic({top_k, [10]}). %% Create a set of scores. S = declare_dynamic(set). %% Compute local top-k list. fold(S, fun max_by_name/2, L). %% Compute global top-k list. fold_dynamic(L, fun max_by_name/2, G).

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118 %% Create a global leaderboard. G = declare({top_k, [10]}). %% Create a local leaderboard. L = declare_dynamic({top_k, [10]}). %% Create a set of scores. S = declare_dynamic(set). %% Compute local top-k list. fold(S, fun max_by_name/2, L). %% Compute global top-k list. fold_dynamic(L, fun max_by_name/2, G).

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119 %% Create a global leaderboard. G = declare({top_k, [10]}). %% Create a local leaderboard. L = declare_dynamic({top_k, [10]}). %% Create a set of scores. S = declare_dynamic(set). %% Compute local top-k list. fold(S, fun max_by_name/2, L). %% Compute global top-k list. fold_dynamic(L, fun max_by_name/2, G).

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120 %% Create a global leaderboard. G = declare({top_k, [10]}). %% Create a local leaderboard. L = declare_dynamic({top_k, [10]}). %% Create a set of scores. S = declare_dynamic(set). %% Compute local top-k list. fold(S, fun max_by_name/2, L). %% Compute global top-k list. fold_dynamic(L, fun max_by_name/2, G).

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121 %% Create a global leaderboard. G = declare({top_k, [10]}). %% Create a local leaderboard. L = declare_dynamic({top_k, [10]}). %% Create a set of scores. S = declare_dynamic(set). %% Compute local top-k list. fold(S, fun max_by_name/2, L). %% Compute global top-k list. fold_dynamic(L, fun max_by_name/2, G).

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122 %% Create a global leaderboard. G = declare({top_k, [10]}). %% Create a local leaderboard. L = declare_dynamic({top_k, [10]}). %% Create a set of scores. S = declare_dynamic(set). %% Compute local top-k list. fold(S, fun max_by_name/2, L). %% Compute global top-k list. fold_dynamic(L, fun max_by_name/2, G).

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Let’s look at a larger application 123

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Let’s look at a larger application 123 With visible   non-monotonicity

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What can we build? Advertisement Counter 124

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Advertisement Counter • Mobile game platform selling advertisement space  Advertisements are paid according to a minimum number of impressions 125

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Advertisement Counter • Mobile game platform selling advertisement space  Advertisements are paid according to a minimum number of impressions • Clients will go oﬄine  Clients have limited connectivity and the system still needs to make progress while clients are ofﬂine 125

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Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Riot Ads Rovio Ads Product Read 50,000 Remove Increment Union 127 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 1 Client 128 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads ovio Ad ounter 1 ovio Ad ounter 2 Riot Ad ounter 1 Riot Ad ounter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Ro C Rovio Ad Counter 1 Ro C Rovio Ad Counter 1 Ro C Rovio Ad Counter 1 Ro C Client Side, Sing 129 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product move Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 130 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads Contracts Ads Contracts Ads With Contracts Filter Product Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 131 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 132 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Riot Ads Rovio Ads Fil Product Read 50,000 Remove Increment Union 133 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client 134 Ads Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 2 Contracts Ads Contracts Ads With Contracts Riot Ads Rovio Ads Filter Product Read 50,000 Remove Increment Read Union Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Rovio Ad Counter 1 Rovio Ad Counter 2 Riot Ad Counter 1 Client Side, Single Copy at Client

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Advertisement Counter • Completely monotonic  Disabling advertisements and contracts are all modeled through monotonic state growth 135

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Advertisement Counter • Completely monotonic  Disabling advertisements and contracts are all modeled through monotonic state growth • Arbitrary distribution  Use of convergent data structures allows computational graph to be arbitrarily distributed 135

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Client3 Lasp Operation User-Maintained CRDT Lasp-Maintained CRDT Riot Ad Counter 1 Client1 Client2 Rovio Ad Counter 1 Rovio Ad Counter 2 Rovio Ad Counter 1 Rovio Ad Counter 2 Rovio Ad Counter 1 Riot Ad Counter 2 Riot Ad Counter 1 Riot Ad Counter 1 Ads With Contracts Ads With Contracts Ads With Contracts Server Ads With Contracts Server Computation! 136

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Advertisement Counter • “Servers” as peers to “clients”  Servers are peers to clients that perform additional computation 139

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Advertisement Counter • “Servers” as peers to “clients”  Servers are peers to clients that perform additional computation • Any node can disable an advertisement under this model given enough information • “Servers” as trusted nodes  Serve as a location for performing “exactly once” side- effects 139

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We’ve build up from zero synchronization 140

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We’ve build up from zero synchronization 140 Instead of working to remove synchronization

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Challenges Looking Ahead 141

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Causality State Explosion 142

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Set Counter

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Set Counter {1} (1, {c}, {}) 1 {(c, 1)}, {}

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Set Counter {1} (1, {c}, {}) 1 {(c, 1)}, {} {} (1, {c}, {c}) {(c, 1)}, {(c, 1)} 0

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Set Counter {1} (1, {c}, {}) 1 {(c, 1)}, {} {} (1, {c}, {c}) {(c, 1)}, {(c, 1)} 0 {1} (1, {a, c}, {c}) {1} 1 1 {(c, 1), (a, 1}}, {(c, 1)}

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Security Computing at the Edge 147

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OK OK OK 148

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Computations Expressiveness 149

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How restrictive is a programming model where operations must be associative, commutative, and idempotent? 150

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What’s new? 151

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Since Erlang Factory 2015 • Erlang 18, rebar3, Common Test  Faster test suite, on Erlang 18 with rebar3 152

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Since Erlang Factory 2015 • Erlang 18, rebar3, Common Test  Faster test suite, on Erlang 18 with rebar3 • No Riak Core  Plumtree distribution replaces the Riak Core distribution system 152

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Since Erlang Factory 2015 • Erlang 18, rebar3, Common Test  Faster test suite, on Erlang 18 with rebar3 • No Riak Core  Plumtree distribution replaces the Riak Core distribution system • No NIFs  Allows us to cross-compile to other platforms and not worry about NIF scheduling 152

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Semantics Improvements • Delta-State Based Conﬂict-Free Replicated Data Types  Optimized state dissemination by shipping deltas   [Almeida et al., 2016] 153

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Semantics Improvements • Delta-State Based Conﬂict-Free Replicated Data Types  Optimized state dissemination by shipping deltas   [Almeida et al., 2016] • Causal CRDTs  Optimized, garbage-free data structure support  [Almeida et al., 2016; Meiklejohn 2016, in review] • Fold  New semantics for a more expressive fold operation for arbitrary computation over sets  [Meiklejohn 2016, in review] 153

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Runtime Improvements • Delta-State Based Anti-Entropy  Optimized AAE mechanism based on deltas  [Almeida et al., 2016] 154

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Runtime Improvements • Delta-State Based Anti-Entropy  Optimized AAE mechanism based on deltas  [Almeida et al., 2016] • Mesos and Docker Enabled  Run large-scale Lasp clusters on Mesos with Marathon  [Meiklejohn and Yoo 2016, in review] • Loquat  Epidemic broadcast, partially replicated with Decentralized Information Flow Control  [Meiklejohn 2016, in review] 154

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Lasp Simulator • Docker Containers  Docker containers for EPMD and Lasp runtime system 155

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Lasp Simulator • Docker Containers  Docker containers for EPMD and Lasp runtime system • Service Discovery  Mechanisms for clustering Erlang nodes based on either Marathon application deﬁnitions of Mesos-DNS 155

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Lasp Simulator • Plumtree VM-to-VM  VM-to-VM communication performed using the Plumtree epidemic broadcast protocol 156

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Lasp Simulator • Plumtree VM-to-VM  VM-to-VM communication performed using the Plumtree epidemic broadcast protocol • Clients-as-processes  Multiple clients per virtual machine, acting as mobile/IoT devices that periodically simulate and can be partitioned • “Design in the small, run in the large”  Runtime conﬁguration change for client-to-VM ratio, allows for single laptop design of multi-machine evaluations of the programming model 156

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What’s next? 160

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Google Summer of Code (and my Ph.D.!) • Partial Evaluation  Optimize execution based on analysis and annotations where we can determine local-vs-remote usage 161

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Google Summer of Code (and my Ph.D.!) • Partial Evaluation  Optimize execution based on analysis and annotations where we can determine local-vs-remote usage • Optimizations  General optimizations of the Erlang implementation of Lasp to improve performance of the runtime system 161

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How do I learn more? 162

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Publications • “Lasp: A Language for Distributed, Coordination-Free Programming”   ACM SIGPLAN PPDP 2015 • “Selective Hearing: An Approach to Distributed, Eventually Consistent Edge Computation”  IEEE W-PSDS 2015 • “The Implementation and Use of a Generic Dataﬂow Behaviour in Erlang”  ACM SIGPLAN Erlang Workshop ’15 • “Lasp: A Language for Distributed, Eventually Consistent Computations with CRDTs"  PaPoC 2015 • “Declarative, Sliding Window Aggregations for Computations at the Edge"  IEEE EdgeCom 2016 163

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164 Christopher Meiklejohn @cmeik http://www.lasp-lang.org Thanks!