Resource Allocation Algorithms for Event-Based Enterprise Systems Ph

Resource Allocation Algorithms for Event-Based Enterprise Systems Ph. D Candidate: Alex K. Y. Cheung Supervisor: Hans-Arno Jacobsen Ph. D Thesis Presentation University of Toronto March 28, 2011 MIDDLEWARE SYSTEMS RESEARCH GROUP

Ph. D Thesis Presentation, Alex Cheung © 2011 2 Introduction to Distributed Contentbased Publish/Subscribe broker subscriber publisher brand = ‘Honda’ cashback > $2000 brand = ‘Honda’ cashback = $6000 >= $0 multicast Advertisement path Subscription path Publication path subscriber brand= ‘Honda’ cashback > $4000

Ph. D Thesis Presentation, Alex Cheung © 2011 Desirable Properties of Distributed Content-based Publish/Subscribe • Decoupling of data sources and sinks § Ease of component addition and removal • Flexible routing based on message content § Efficient use of network resources • Distributed broker overlay network § Scalable § Fault tolerant 3

Ph. D Thesis Presentation, Alex Cheung © 2011 Applications of Publish/Subscribe • • Network and systems monitoring [Mukherjee 1994] Business activity monitoring [Fawcett et al. 1999] Business process execution [Schuler et al. 2001] Workflow management [Cugola et al. 2001] Multiplayer online games [Bharambe et al. 2002] RSS filtering [Petrovic et al. 2005; Rose et al. 2007] Automated service composition [Hu et al. 2008] Resource discovery [Yan et al. 2009] 4

Ph. D Thesis Presentation, Alex Cheung © 2011 Real Deployments of Distributed Publish/Subscribe • Goo. PS ▫ Google’s pub/sub messaging middleware to integrate web applications (such as Gmail, Google Docs, Google Calendar) on a world-wide scale supporting millions of users ▫ Hundreds of brokers with tens of thousands of pub/sub clients • Yahoo Message Broker ▫ Yahoo’s pub/sub middleware to integrate applications with their database system, PNUTS • Super. Montage ▫ Tibco’s pub/sub distribution network for Nasdaq’s quote and order-processing system • GDSN (Global Data Synchronization Network) ▫ A global pub/sub network that allows retailers and suppliers (i. e. , Walmart, Target, Metro, etc. ) to exchange timely and accurate supply chain data 5

Ph. D Thesis Presentation, Alex Cheung © 2011 Contributions • Load Balancing in Content-based Publish/Subscribe Systems (ACM TOCS’ 10) • Publisher Placement Algorithms in Contentbased Publish/Subscribe (IEEE ICDCS’ 10) • Green Resource Allocation Algorithms in Content-based Publish/Subscribe (IEEE ICDCS’ 11) 6

Ph. D Thesis Presentation, Alex Cheung © 2011 Problem • Brokers located at different geographical areas may suffer from uneven load distribution due to ▫ Heterogeneous servers ▫ Network congestion ▫ Different densities and interests of end-users • Consequences ▫ Overloaded brokers introduce high delivery delays that may ultimately crash from running out of memory ▫ System that does not scale with the added resources 7

Ph. D Thesis Presentation, Alex Cheung © 2011 Visualizing the Problem S P S

Ph. D Thesis Presentation, Alex Cheung © 2011 Overview of Load Balancing Approach load-accepting

Ph. D Thesis Presentation, Alex Cheung © 2011 10 B 12 Evaluation • Implemented on a real open source pub/sub system called PADRES • Planet. Lab and a cluster testbed • Local and global load balancing • Homogeneous and heterogeneous servers • Compared against a naive approach B 22 B 32 B 42 B 52 B 62 P P P B 10 B 20 B 30 B 40 B 50 B 60 S S S B 11 B 21 B 31 B 41 B 51 B 61 Global LB Setup

Ph. D Thesis Presentation, Alex Cheung © 2011 Summary • Load balancing enables the pub/sub system to scale with the number of resources • Load balancing solutions that are unaware of subscription load and relationships are ineffective ▫ Long response time ▫ Unstable system 11

Ph. D Thesis Presentation, Alex Cheung © 2011 Contributions • Load Balancing in Content-based Publish/Subscribe Systems (ACM TOCS’ 10) • Publisher Placement Algorithms in Contentbased Publish/Subscribe (IEEE ICDCS’ 10) • Green Resource Allocation Algorithms in Content-based Publish/Subscribe (IEEE ICDCS’ 11) 12

Ph. D Thesis Presentation, Alex Cheung © 2011 Problem 13 P • Publishers can join anywhere or to the closest broker in the overlay • Consequences ▫ High delivery delay Sluggish system ▫ High resource usage in terms of matching, network bandwidth, and subscription storage High IT costs S S

Ph. D Thesis Presentation, Alex Cheung © 2011 Approach 14 P • Adaptively move publisher to area of matching subscribers • Two unique solutions ▫ POP (Publisher Optimistic Placement) Decision is based on the average number of downstream publication deliveries ▫ GRAPE (Greedy Relocation Algorithm for Publishers of Events) Decision is based on the end-to-end delivery delay, total broker message rate, and user specified inputs including the minimization metric (load/delivery delay) and weight S S

Ph. D Thesis Presentation, Alex Cheung © 2011 Evaluation Reduced message rate by up to 85% • Implemented on the open source pub/sub system called PADRES • Planet. Lab and a cluster testbed • Enterprise and random workloads Reduced delivery delay by up to 68% 15

Ph. D Thesis Presentation, Alex Cheung © 2011 Summary • POP is suitable for pub/sub systems that strive for simplicity, such as Goo. PS • GRAPE is suitable for systems that strive to minimize in the extremes, such as system load in sensor networks or delivery delay in Super. Montage 16

Ph. D Thesis Presentation, Alex Cheung © 2011 Contributions • Load Balancing in Content-based Publish/Subscribe Systems (ACM TOCS’ 10) • Publisher Placement Algorithms in Contentbased Publish/Subscribe (IEEE ICDCS’ 10) • Green Resource Allocation Algorithms in Content-based Publish/Subscribe (IEEE ICDCS’ 11) 17

Ph. D Thesis Presentation, Alex Cheung © 2011 Problem • What is the deployment strategy for the broker overlay, publisher assignment, and subscriber assignment to minimize the broker message rate and number of allocated brokers? • Proven to be an NP-complete problem • Benefits ▫ Increase capacity of the system ▫ More efficient energy usage of the allocated servers ▫ Fewer servers mean lower investment and maintenance costs ▫ Inline with Green IT, which is also what enterprises such as Google and Yahoo are currently engaged in 18

Ph. D Thesis Presentation, Alex Cheung © 2011 Approach • 3 phase design Phase 1 Phase 2 . Record the publications delivered to each subscription into bit vectors Use information from the bit vectors to allocate subscriptions to brokers using one of 10 algorithms Phase 3 Construct the broker overlay with 3 optimization techniques and deploy the new configuration • Most compelling properties ▫ Language independent Content-based (XPath, regex, ranged, SQL, composite subscriptions, etc. ) and topic-based, such as Goo. PS ▫ Works effectively under any workload (defined or undefined) 19

Ph. D Thesis Presentation, Alex Cheung © 2011 Phase 1: Subscription Profiling e of each subscriber per advertisement tained at the subscriber’s first broker M 213 Message ID of first index Start of bit vector 0 0 0 0 1 1 1 size so shift left if next publication is bit vector range nality of bit vector corresponds to width requirement of the subscription to compute “closeness” of between wo subscriptions in the clustering thm. closeness = |si ∩ sj| 20

Ph. D Thesis Presentation, Alex Cheung © 2011 Phase 2: Subscription Allocation Algorithms • MANUAL/(AUTOMATIC) ▫ Tree with fanout of 2, manual (random) placement of clients • Fastest Broker First (FBF) ▫ Assign subscriptions randomly to the next most powerful broker • Bin Packing ▫ Like FBF, but assigns the next highest traffic subscription • PAIRWISE-N, PAIRWISE-K (related approaches in ICDCS’ 02) ▫ Subscription clustering where the number of clusters is given • CRAM (Clustering with Resource Awareness and Minimization) ▫ Dynamically determines the number of clusters ▫ Utilizes a new clustering algorithm that is more effective ▫ Evaluated with 4 different subscription closeness metrics, with one derived from Banavar et al. in ICDCS '99 21

Ph. D Thesis Presentation, Alex Cheung © 2011 22 Bin Packing S S S

Ph. D Thesis Presentation, Alex Cheung © 2011 Bin Packing’s Allocation Result S S

Ph. D Thesis Presentation, Alex Cheung © 2011 Phase 3: Broker Overlay Construction S

Ph. D Thesis Presentation, Alex Cheung © 2011 25 Bin Packing’s Final Overlay P

Ph. D Thesis Presentation, Alex Cheung © 2011 Evaluation • Implemented on the PADRES open source content-based pub/sub project • Evaluated on a cluster testbed with 80 brokers • Evaluated on Sci. Net, an HPC with 1000 brokers • Comparison against two related works (Riabov et al. ICDCS’ 02, Banavar et al. ICDCS’ 99) • Homogeneous and heterogeneous scenarios • Workload saturates the initial deployment (MANUAL) 26

Ph. D Thesis Presentation, Alex Cheung © 2011 27 Evaluation Results on Sci. Net

Ph. D Thesis Presentation, Alex Cheung © 2011 Summary • CRAM combines the benefits of ▫ Subscription clustering ▫ Resource awareness from Bin Packing by simultaneously reducing both ▫ Broker message rates ▫ Number of allocated brokers • Bit vectors are powerful ▫ Language independent (XPath, regex, topics) ▫ Effective with any workload distribution 28

Ph. D Thesis Presentation, Alex Cheung © 2011 Conclusions • Load balancing increases ▫ Availability by circumventing overloads ▫ Scalability of the system • Publisher placement algorithms reduce ▫ Broker input load by up to 68% ▫ Broker message rate by up to 85% ▫ Delivery delay by up to 68% • Resource allocation algorithms reduce ▫ Average broker message rate by up to 92% ▫ Number of allocated brokers by up to 91% 29

Ph. D Thesis Presentation, Alex Cheung © 2011 Future Work • Self-tuning of load balancing parameters • React dynamically by growing and shrinking the network in incremental steps • Improve runtime of the CRAM algorithm by parallelization or reducing its computational complexity • Model workload with more sophisticated methods, such as stochastic processes, to improve accuracy of load estimation • Address fault resiliency in each approach 30