Fexible QoS Management of Web Services Orchestrations

Transcription

1 Fexible QoS Management of Web Services Orchestrations Ajay Kattepur 1 1 Equipe ARLES, INRIA Paris-Rocquencourt, France. Collaborators: Albert Benveniste (INRIA), Claude Jard (INRIA / Uni. Nantes), Sidney Rosario (INRIA / Univ. Texas-Austin), John Thywissen (Univ. Texas-Austin). 1 / 37

2 Dell Supply Chain 1 Efficient Inventory Management of Dell s Production 1 R. Kapunscinski, R. Q. Zhang, P. Carbonneau, R. Moore, and B. Reeves, Inventory Decisions in Dells Supply Chain, Interfaces, vol. 34, 3, pp , / 37

3 Outline 1 Overview Web Services QoS in Web Services Orchestrations 2 QoS Algebraic Model 3 Optimization tools for QoS in Orchestrations 4 Improving Service Level Agreements 5 Conclusions/Research Directions 3 / 37

4 Outline Overview 1 Overview Web Services QoS in Web Services Orchestrations 2 QoS Algebraic Model 3 Optimization tools for QoS in Orchestrations 4 Improving Service Level Agreements 5 Conclusions/Research Directions 4 / 37

5 Web Services Overview Web Services Distributed system: connection and coordination of components over standard networking protocols Web services are software components over the internet: Platform and language agnostic Describe functionality offered Pass control flow to other such components 5 / 37

6 Web Service Composition Overview Web Services Composition of Services: Business processes including humans, software components, computational devices. 6 / 37

7 Overview Web Services Orchestrations and Choreographies Orchestration: centralized handling of control flow Choreographies: tracks the message sequences among components 7 / 37

8 Orc 2 Overview Web Services Orc is a simple yet powerful concurrent programming language Fundamental declaration used is a site Orc has the following combinators to create expressions: The parallel combinator F G The sequential combinator F>x>G or F G The pruning combinator F<x<G or F G The otherwise combinator F;G Sites to handle timeouts, recursions, semaphores and channels 2 J. Misra and W. R. Cook, Computation Orchestration: A Basis for Wide-area Computing, Springer J. of Software and Systems Modeling, vol. 6, 1, pp , / 37

9 Overview QoS in Web Services Orchestrations QoS QoS in Services lifecycle: discovery, selection and substitution Multi-dimensional, Probabilistic models QoS Composition: 9 / 37

10 SLA Overview QoS in Web Services Orchestrations Contract Composition: Resource management when sub-contracting atomic services Contract types: Hard Contracts: Service responds with latency 2000 ms. in 90% of cases (WSLA standards) Soft Contracts: Compares probabilistic distributions (percentiles in QML) Consequences: Negotiations, Monitoring, Optimized resource management 10 / 37

11 Outline QoS Algebraic Model 1 Overview Web Services QoS in Web Services Orchestrations 2 QoS Algebraic Model 3 Optimization tools for QoS in Orchestrations 4 Improving Service Level Agreements 5 Conclusions/Research Directions 11 / 37

12 QoS Algebraic Model Motivation 3 Monotonicity - A better performing sub-contracted service does not deteriorate end-to-end QoS Abstract Algebraic framework for QoS composition - encompasses control flow dependencies 3 A. Benveniste, C. Jard, A. Kattepur, S. Rosario and J. A. Thywissen, QoS-Aware Management of Monotonic Service Orchestrations, Formal Methods in System Design (under review), / 37

13 QoS Algebra QoS Algebraic Model Algebra Q = (D,,, ) Incrementing QoS: q 1 = q 0 δq 1 Synchronizing tokens: Supremum associated with partial order : q 0 q 1 Competition policy: If (q 0 δq 1 ) (q 0 δq 1 ) implies that t 1 fires with competition q 1 = (q 0 δq 1 ) (q 0 δq 1 ) 13 / 37

14 Upgrading Orc QoS Algebraic Model Generic Rules to handle QoS in Orc: -- SLADeclaration.orc def bestqos(comparer, publisher) = head(sortby(comparer, publisher)) def class ResponseTime() = def QoS(sitex, d) = Rclock().time()-d >q> q def QoSOplus(rt1, rt2) = rt1+rt2 def QoSCompare(rt1, rt2) = rt1 <= rt2 def QoSCompete(rt1, rt2) = bestqos(qoscompare, [rt1, rt2]) def QoSVee(rt1, rt2) = max(rt1, rt2) stop 14 / 37

15 QoS Algebraic Model Dell Contract Composition 15 / 37

16 QoS Algebraic Model Probabilistic Contracts 4 Contracts: Generated from First Order Stochastic Dominance: G F x D, G(x) F(x) (1) Monitoring: Kolmogorov-Smirnov statistic δ(f,g) = sup(f(x) G(x)) (2) }{{} x X Negotiation: Assumption Distribution = Guarantee Distribution Number of Hits Response Time (seconds) 4 S. Rosario, A. Benveniste, S. Haar, and C. Jard, Probabilistic QoS and Soft Contracts for Transaction-Based Web Services Orchestrations, IEEE Trans. on Services Computing, vol. 1, no. 4, pp , / 37

17 QoS Algebraic Model Dell Contract Composition Supplier Refueling reflects Customer QoS Cumulative Density Supplier 1 Replenishment (Contract) Supplier 2 Replenishment (Contract) Revolver 1 Delay (measurement) Revolver 2 Delay (measurement) Supply Chain 1 Contract Supply Chain 2 Contract Customer Contract Offer Value 17 / 37

18 Outline Optimization tools for QoS in Orchestrations 1 Overview Web Services QoS in Web Services Orchestrations 2 QoS Algebraic Model 3 Optimization tools for QoS in Orchestrations 4 Improving Service Level Agreements 5 Conclusions/Research Directions 18 / 37

19 Motivation 5 Optimization tools for QoS in Orchestrations QoS uses exhaustive search; difficulties: Directories of large sets of services Tradeoff between QoS metrics (random variables) Integrate mathematical optimization packages Generic techniques for comparing QoS metrics - total ordering. 5 A. Kattepur, A. Benveniste and C. Jard, Optimizing Decisions in Web Services Orchestrations, International Conference on Service Oriented Computing (ICSOC), / 37

20 Optimization tools for QoS in Orchestrations Analytical Hierarchy Process (AHP) 6 Generate totally ordered cost functions in multi-criterion decisions. Comparison between metrics done using subjective classification: 6 T. L. Saaty, How to make a decision: The analytic hierarchy process, Eur. J. of Operational Research, vol. 48, 1, pp. 9 26, / 37

21 AHP example Optimization tools for QoS in Orchestrations Principal eigenvector of the positive reciprocal matrix W is normalized to provide linear weights. W = δ $ ζ λ ρ δ $ ζ 1/5 1/ λ 1/3 1/ ρ 1/5 1/5 1/2 1/3 1 (3) Consistency of evaluation through highest Eigenvalue c max Perron Frobenius Theorem For a given positive matrix W, the only positive vector υ and only positive constant c that satisfy Wυ = cυ: - υ that is a positive multiple of the principle eigenvector of W. - c is the principal Eigenvalue of W. 21 / 37

22 Optimization tools for QoS in Orchestrations Dell Logistics Optimization t λ t δ cust Unit of time with t 1,2,...T hours Number of queries per unit time that the plant requests the revolver Waiting time for the plant µ t Stock level for an item in the revolver at time t µ c Critical stock levels of the item in the revolver µ max Maximum stock level allowed in the revolver ρ β δ sup υ µc,...υ β Inventory polling period of the supplier Size of the refueling batch from the supplier Delay period for refueling the revolver Normalized eigenvector from the consistent AHP matrix 22 / 37

23 Optimization tools for QoS in Orchestrations Dell Logistics Optimization Supply-side stock control formulated as a linear program: subject to: min υ µc µ c +υ µmax µ max +υ β β (4) 0 µ c µ max (Critical stock level constraint) 0 β µ max (Refueling batch constraint) µ max µ c +(λ t δ sup ) = β (Inventory level constraint) 0 µ max K λ t (Maximum stock level constraint) w µc w µmax w β Normalized Weights 1 1 w µc w µmax w β c max = , CI = , CR = / 37

24 Optimization tools for QoS in Orchestrations Dell Logistics Optimization Frequency Frequency Customer Demand λ t (items / hour) Supply Refuel Delay δ sup (hours) Frequency Freqency Critical Revolver Level µ c Maximum Revolver Level µ max Batch Size β (items) Number of items Optimal setting of parameters in the Dell Supply Chain. 24 / 37

25 Optimization tools for QoS in Orchestrations Dell Logistics Optimization Cumulative Frequency Revolver Level µ t 2000 Maximum Revolver Level µ max 1000 Critical Revolver Level µ c Number of Items Procured Distributions of the inventory levels in the Dell system. 25 / 37

26 Outline Improving Service Level Agreements 1 Overview Web Services QoS in Web Services Orchestrations 2 QoS Algebraic Model 3 Optimization tools for QoS in Orchestrations 4 Improving Service Level Agreements 5 Conclusions/Research Directions 26 / 37

27 Motivation 7 Improving Service Level Agreements Samples with lower spread are more accurate for SLAs Extreme values (eg percentile) are seldom observed - lead to imprecise SLAs Monte-Carlo techniques replaced with Importance Sampling More precise definition of QoS sampling, measurement and variance in SLA declarations 7 A. Kattepur, Importance Sampling of Probabilistic Contracts in Web Services, International Conference on Service Oriented Computing (ICSOC), / 37

28 Improving Service Level Agreements Importance Sampling Probability of a rare event P(H(Q) > Φ): P MC = 1 N N 1 H(Qi )>Φ (5) i=1 Generate samples Q 1,Q 2,...Q N from an auxiliary PDF G Q : P IS = 1 N N i=1 F Q (Q i ) H(Q i )1 H(Qi )>Φ G Q (Q i ) (6) G Q should be chosen such that it has a thicker tail than F Q for lower variance 28 / 37

29 Improving Service Level Agreements Dell Contract Composition Assumption: The demand (number of orders/hour) distributions from the Dell plant made to a particular revolver. Guarantee: The delay (hours) distribution in obtaining products from revolvers. The suppliers ensure efficient and timely refueling. 29 / 37

30 Improving Service Level Agreements Dell Contract Composition Observation Importance Sampling 200 Observations Importance Distribution 99.9 percentile percentile percentile Frequency Frequency Inter Query Interval (minutes) Response Time (minutes) Inter-query period (mins.) mean MC variance MC mean IS variance IS Inter-query periods by Monte-Carlo (MC) and Importance Sampling (IS). Percentile Latency (mins.) mean MC variance MC mean IS variance IS Latency by Monte-Carlo (MC) and Importance Sampling (IS) schemes. 30 / 37

31 Forecasting Improving Service Level Agreements Pre-identified contracts to provide an easier method of forecasting outages in web services orchestrations. Emphasize the need for precise contractual obligations. Importance Sampling can detect more accurately, changes in refueling made by Dell suppliers. 31 / 37

32 Forecasting Improving Service Level Agreements Observation Importance Sampling Threshold 1 Threshold 2 Threshold 3 Frequency 60 Frequency Number of Orders / Hour Procurement Delay (hours) Delay (hours) mean MC variance MC mean IS variance IS Original contract estimates. Delay (hours) mean MC variance MC mean IS variance IS Reformulated contract estimates providing lower probabilities of delay. 32 / 37

33 Outline Conclusions/Research Directions 1 Overview Web Services QoS in Web Services Orchestrations 2 QoS Algebraic Model 3 Optimization tools for QoS in Orchestrations 4 Improving Service Level Agreements 5 Conclusions/Research Directions 33 / 37

34 Conclusions Conclusions/Research Directions Recognize importance of accurate QoS behavior in web services - probabilistic, monotonicity, data-dependency. Integrate tools to better handle QoS - aspects, optimization. Improved Contractual Agreements - soft contracts, accelerated simulation techniques. 34 / 37

35 Research Directions Conclusions/Research Directions Business Artifacts to realistically model complex wokflow systems - choreographies among multiple parties Analytics or Simulations to gather real-time/historical data Improved planning, optimization, load balancing, resource allocation - use of mathematical tools Contractual obligations, forecasting - monitoring for outages in a probabilistic manner 35 / 37

36 QoS to Analytics Conclusions/Research Directions Big Data / Cloud Supply Chain Business Process Crowdsourcing Feedback on Model Data Analysis Statistical Models Control Flow Algorithms Adaptation/Reconfiguration Monitoring QoS Contracts Design Time Specifications QoS Contracts Workflow Models Deploy Runtime Statistical Analysis 36 / 37

37 Conclusions/Research Directions Thank you. 37 / 37