Boone, Bas

Van Hoecke, Sofie

Van Seghbroeck, Gregory

English

Ghent University Academic Bibliography

SALSA: QoS-aware load balancing forautonomous service brokeringBas Boone, Sofie Van Hoecke, Gregory Van SeghbroeckSupervisor(s): Bart Dhoedt, Filip De TurckI. INTRODUCTIONNowadays, many new applications are con-structed through integration of already avail-able service components. The approach ismade possible through the Service OrientedArchitecture (SOA) and “Software as a Ser-vice” paradigm, typically using web servicetechnologies to publish, discover and integrateservice components. This technology also al-lows to replicate web services on new serversto scale in response to the needed demands. In-stead of hard coding service calls in the cus-tomer’s source code, brokers provide dynamicservice selection to automatically select andseamlessly link the services in order to meetthe business system requirement, optimize re-sponse times or reduce the costs. By using webservice brokers, customers only have to interactwith the service broker, hiding the complexityof selecting the appropriate service.In a commercial application typically a Ser-vice Level Agreement (SLAs) can be me-diated between the customers and the ser-vice providers defining the functional and non-functional requirements such as the levels ofavailability, performance and billing. Often, aservice provider also wants to service a classof customers on a best effort basis. In the caseof performance, the SLA usually specifies con-straints on the response time. If no special pre-cautions are taken, unexpected request patternscan drive a web server into overload, leading to—————————————————–B. Boone is with the Information Technology De-partment, Ghent University (UGent), Gent, Belgium.E-mail: bas.boone@intec.ugent.be .poor performance since the server is unable tokeep up with the demands. Service providerscan solve this problem by over-dimensioningtheir resources and provide dedicated serversfor premium customers to meet their SLAs. Ifwe want to avoid using dedicated servers, intel-ligent autonomous service brokering is needed.II. SALSABrokerQoS-awareQoS-unawareFigure 1. Objective of the Simulated Annealing LoadSpreading AlgorithmWe have examined two requirements for ser-vice brokers: on one hand, the broker should beable to autonomously guarantee constraints onthe response time for premium clients by fulfill-ing a n-percentile on the response time, i.e. thevalue for which at most n% of the responsetimes are fulfilled in less than that value. Onthe other hand, brokering should be transpar-ent for the actual servers executing the service:they should not need to be changed.In order to fulfill these requirements, theSimulated Annealing Load Spreading Algo-rithm (SALSA) presented here can load bal-ance requests, and selectively drop some re-quests from the default users to reduce the webservers’ load in order to guarantee SLA to pre-mium customers and provide best effort to de-fault customers (see Figure 1).The web servers are modeled as M/M/1 sys-tems using standard queueing theory [2], andthe broker is modeled as a statistical switch: itforwards requests randomly to a server with agiven probability, and it drops default requestswith a certain probability. The SALSA algo-rithm determines the best possible values forthese probabilities, given the arrival rate of de-fault and premium requests, and the process-ing intensity of the different web services. Inorder to do this, we define a score functionwhich considers average waiting times, over-loaded servers, exceeded waiting time thresh-olds for premium clients, and the number ofdropped default requests. This score function isoptimized for the switching probabilities usingSimulated Annealing [3], a generic optimiza-tion heuristic.III. EVALUATIONWe implemented the SALSA algorithm andperformed simulations to test the results, us-ing a theoretically derived optimality criterion.These tests confirmed that SALSA is able tofind optimal solutions. The results were com-pared with those of Weighted Round Robin(WRR), nowadays the most commonly usedload balancing algorithm, and it was found thatSALSA was able to guarantee the threshold re-quirement for higher arrival rates than WRR.We also implemented a testbed evaluation.Our test setup consisted of a load generator, twoweb servers and a service broker implementedusing Apache Synapse. Using this setup, wecompared the performance of SALSA to WRR,with different arrival rate patterns. Some re-sults are shown in Table 1, where λd and λprepresent default resp. premium arrival rates. Itcan be seen from this table that WRR slightlyoutperforms SALSA in underloaded circum-stances. This is normal, since SALSA requiresmore processing, and has to slightly overesti-mate arrival rates in order to make statisticalguarantees. However, in situations with higherarrival rates, SALSA is able to guarantee a 95-percentile waiting time, where WRR is not.Crossingλd λp Algorithm threshold (%)40 20SALSA 1,66WRR 0,7110 40SALSA 2,28WRR 0,5950 50SALSA 0,47WRR 28,7040-80 20-40SALSA 2,16WRR 17,15Table 1. Comparing SALSA to WRR.IV. CONCLUSIONSBy using the SALSA algorithm, requiringslightly more processing than weighted round-robin, brokers can guarantee a n-th percentileresponse time to their premium users, whileproviding best effort to the default customers.As service-oriented architectures have largelydistributed topologies, SOA broker architec-tures can benefit from our SALSA algorithm asthe service providers can be QoS unaware, re-leased from mediating SLAs, and don’t have tobe a priori over-dimensioned.REFERENCES[1] B. A. Shirazi, A. R. Hurson, K. M. Kavi, Eds.,Scheduling and load-Balancing in Parallel andDistributed Systems, IEEE CS Press, 1995.[2] D. Gross, C. Harris, Fundamentals of QueueingTheory, 3rd ed, 1998.[3] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Opti-mization by Simulated Annealing, Science, Num-ber 4598, 220, 4598:671-680, 1983.

SALSA: QoS-aware load balancing for autonomous service brokering

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SALSA: QoS-aware load balancing for autonomous service brokering

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