Constructing Scalable Overlays for Pub/Sub With Many Topics

Constructing Scalable Overlays for Pub/Sub With Many Topics Problems, Algorithms, and Evaluation G. Chockler, R. Melamed, Y. Tock, IBM Haifa Research Lab R. Vitenberg, University of Oslo

Publish/Subscribe (Pub/Sub) {A,B,C,E,} Subscription(N1)={B,C,D} N2 {A,D} N1 N3 M1 Message Bus M1 {A,X} Publish(M1, A) N5 M1 N4 {A,B,X}

Scalability of Pub/Sub • Most traditional pub/sub systems are geared towards small scale deployment • E.g., Isis MDS, TIB, MQSeries, Gryphon • New generation of applications… • Large data centers: Amazon, Google, Yahoo, EBay,… • RSS, feed/news readers, on-line stock trading and banking • Web 2.0, Second Life • …drive dramatic growth in scale • 10,000s of nodes, 1000s of topics, Internet-wide distribution • Emerging systems address this trend using P2P techniques

Overlay-Based Pub/Sub Relay {A,B,C,E} {B,C,D} (M1, A) N2 {A,D} N1 N3 (M1, A) (M1, A) (M1, A) • SCRIBE • Corona • Feedtree • Sub-2-Sub • TERA • ... N5 (M1, A) {A,X} N4 {A,B,X}

Overlay Topologies for Pub/Sub • “Good”overlay will allow for efficient and simple publication routing • Small routing tables, low load on relays, • low latency • Ideally, overlay is topic-connected: i.e., one connected component for each topic-induced sub-graph • Most existing implementations construct topic-connected overlays

Topics B,C,X,E are connected Topics A and D are disconnected Topic-Connectivity {A,B,C,E} {B,C,D} N2 {A,D} N1 N3 N5 {A,X} N4 {A,B,X}

Node degree grows linearly with the subscription size • Roughly twice as big as the average subscription size for rings/trees Topic-Connectivity: Simple Solution {A,B,C,E} {B,C,D} N2 {A,D} N1 N3 N5 {A,X} N4 {A,B,X}

Scalability of the Simple Solution • Negative impact on performance due to • CPU load: neighbor monitoring, message processing • Connection maintenance and header overhead • Memory overhead: per-link state associated with routing and/or compression schemes being used, etc. • Scalability barrier for large systems offering a wide range of subscription choices Can we do better?

The Min-TCO Problem • Minimum Topic-Connected Overlay (Min-TCO) problem: • For a set of nodes V, set of topics T, and Interest: V  T {true, false} • Construct a topic-connected overlay G with the minimum possible number of edges (or average degree) • TCO (decision version): • Decide whether there is a topic-connected overlay consisting of k edges (for a given k)

Complexity of TCO {B,C,D} {A,B} Lemma: TCO(V,T,Interest,k)NP Proof: Topic connectivity is verifyable in polynomial time Lemma: TCO(V,T,Interest,k) is NP-hard Proof: • Define an auxiliary problem Single Node TCO (SN-TCO) which is to decide if there is a topic-connected overlay in which the degree of single given node  d • Set Cover is polynomially reducible to SN-TCO • SN-TCO is polynomially reducible to TCO Theorem: TCO is NP-complete N5 N2 {A,D} N3 N1 N4 {A,B,C,D} {A,C}

Approximating Min-TCO • The idea: exploiting subscription overlaps • Connecting the nodes with overlapping interests improves connectivity of several topics at once • Greedy Merge (GM) algorithm: • Start from a singleton connected component for each (v, t)  V  T • At each iteration: add an edge that reduces the number of connected components for the biggest number of topics • Stop, once there is a single connected component for each topic

Greedy Merge {B,C,D} {A,B,C,E} N1 N2 {A,D} N3 N5 {A,X} N4 {A,B,X}

Average degree of 2 vs. almost 3 for ring-per-topic! Greedy Merge {B,C,D} {A,B,C,E} N1 N2 {A,D} N3 N5 {A,X} N4 {A,B,X}

GM Running Time • O(|V|4|T|) • At most |V|2 iterations • At most |V|2 edges inspected at each iteration • At most |T| steps to inspect an edge • Can be optimized to run in O(|V|2|T|) • For each e  V  V, weight(e) = the number of connected components merged by e • At each iteration, output the heaviest edge and adjust the other edge weights accordingly • Stop once there are no more edges with weight > 0

Approximability Results Lemma: • The number of edges in the overlay constructed by GM  log(|V||T|) OPT Proof: Similar to that of the approximation ratio of the greedy algorithm for Set Cover • There exists an input on which GM’s output meets this ratio Theorem: No algorithm can approximate Min-TCO within a constant factor (unless P=NP) Proof: Existence of such an algorithm would imply existence of the constant factor approximation for Set Cover which is known to be impossible (unless P=NP)

Practical Benefits

More Overlay Design Problems • Filtering: Given an upper bound d on the node degree, minimize the number of relays used to connect each topic • Captures the cases when full topic-connectivity is infeasible because of resource constraints • Diameter: Given an upper bound d on the node degree, minimize the diameter of each topic in the overlay • Latency optimal routing under resource constraints • …

Conclusions • Initiated formal study of the problem of designing efficient and scalable overlay topologies for pub/sub • Defined a representative problem (Min-TCO) capturing the cost of constructing topic-connected overlays • NP-Completeness, polynomial approximation, inapproximability results • Empirical evaluation showed effectiveness of our approximation algorithm on practical inputs

Future Directions • Study dynamic case • Investigate other overlay design problems • Study distributed case • Partial knowledge of other node interest • Dynamically changing interest assignments

Thank You!

Constructing Scalable Overlays for Pub/Sub With Many Topics