is equal to this resource type are considered compatible.The %Service 1 Service 2Service 3Service 4 second is the pattern match:if the weak point is already a W2 99.9% Service 5 Service 6 Semvice 7 Host redundant HA solution(e.g.,a cluster),then HA patterns that can generate this HA solution are considered as compatible. From the list of matched HA patterns (perhaps under the EAR1(APP)EAR2(APP) EAR3 (APP)DB1 (APP) guidance of the software architect),one is selected and config- ured with the HA enhancement parameters.Then,the pattern transformation and configuration logic is used to generate an IHSServer WASServer WASServer D82 Server HA solution that is deployable in IT environments. LinuxOS LinuxOs AIXOS windosOs III.ALGORITHM PowerServer X86Server C1 C2 C3 C4 A key contribution of our weak-point analysis methodology (a)SOA deployment topology example is to attach availability requirements to the business workflow and then to map these workflows to the IT infrastructure, C1 C2 C3 C4 carrying the availability requirements down to the level of the 1 1 individual IT resources,where they can be analyzed.In this 1 1 0 1 section,we describe a methodology for making HA enhance- (b)Workflow-resource relationship matrix ment recommendations to meet the business-level availability objectives,while keeping the overall cost close to the mini- Fig.7. SOA deployment topology example for problem definition mum.In this methodology,the current availability capability for each workflow is first calculated according to the compo- nent failure behavior parameters obtained from historical data and experience:Mean Time Between Failures (MTBF),Mean Time To Repair(MTTR),etc.Secondly,it is checked whether We denote the n business workflows over the SOA de- the availability requirements for each business workflow have been satisfied.Thirdly,for the affected workfows,the relevant ployment topology as W1.W2,W3....,Wn.These workflows are IT resources are identified as availability weak points.and specified with availability requirements P.B2.P3.....P,where appropriate HA patterns are recommended in order to meet 0<P<1.We also assume that there are m IT resources, the availability requirements. denoted by C1,C2.....Cm.We construct a workflow-resource relationship matrix where IT resources depended upon by A.Availability Optimization Problem each workflow are identified;each matrix entry Ri.;expresses the dependency degree from workflow W;to IT resource C A simple example is depicted in Fig.7(a)to illustrate the as previously described.For instance,if IT resource C is availability optimization problem.In this example,we have referenced three times by a given business workflow Wi,its two business workflows(Wi and W2)and four underlying IT dependency degree Rii will be recorded as 3. resources (C1,C2,C3 and C).The workflow specification module constructs the workflow-resource relationship matrix A resource vector can be defined for a given workfow (see Fig.7(b)).In this example,finding an optimized HA ,Was(C1,R,1）,(C2,R,2〉，，(Cm,R,m.For simplicity, enhancement recommendation requires the iterative testing of we express this as (Rj.1.Rj.2....Rj.m).Based on the above various HA solutions and different redundancy degrees:for definition,for a given IT resource Ci,its intrinsic availability each IT resource,various HA solutions could be applicable,capability (e.g.,based on historical experience)is denoted by and for the redundancy-based HA solution,different redun- P(Ci),and its cost is denoted by h(Ci,ni)where ni is the dancy degrees should be explored. redundancy degree (e.g.,cluster size)of this resource.For More generally,to make an optimal HA enhancement a given workflow Wi,its current availability capability can recommendation for an SOA deployment topology,three op- be calculated by P(W;).These calculation functions will be timization dimensions should be iteratively explored:i)every discussed in Section III.B. IT resource in the deployment topology could be an HA enhancement candidate;ii)a variety of HA solutions could The optimization problem becomes finding a cost- be applied to a given IT resource;and iii)each of these HA effective HA enhancement recommendation described as solutions could rely on different redundancy degrees.Such an ((Ci,n1),(C2.n2).....(Cm,nm))where Ci denotes the original iterative exploration requires exponential computation time. IT resource,and n denotes the new redundancy degree Due to this computation complexity,it is inapplicable for for Ci.This enhancement recommendation keeps the overall large-scale IT infrastructures,which are frequently used in cost minimal while satisfying all availability requirements for real-world SOA environments. each business workflow.Formally,the availability optimization Let us now rigorously define this availability optimization problem is defined as a constrained optimization problem as problem. follows:is equal to this resource type are considered compatible. The second is the pattern match: if the weak point is already a redundant HA solution (e.g., a cluster), then HA patterns that can generate this HA solution are considered as compatible. From the list of matched HA patterns (perhaps under the guidance of the software architect), one is selected and config￾ured with the HA enhancement parameters. Then, the pattern transformation and configuration logic is used to generate an HA solution that is deployable in IT environments. III. ALGORITHM A key contribution of our weak-point analysis methodology is to attach availability requirements to the business workflow and then to map these workflows to the IT infrastructure, carrying the availability requirements down to the level of the individual IT resources, where they can be analyzed. In this section, we describe a methodology for making HA enhance￾ment recommendations to meet the business-level availability objectives, while keeping the overall cost close to the mini￾mum. In this methodology, the current availability capability for each workflow is first calculated according to the compo￾nent failure behavior parameters obtained from historical data and experience: Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), etc. Secondly, it is checked whether the availability requirements for each business workflow have been satisfied. Thirdly, for the affected workflows, the relevant IT resources are identified as availability weak points, and appropriate HA patterns are recommended in order to meet the availability requirements. A. Availability Optimization Problem A simple example is depicted in Fig. 7(a) to illustrate the availability optimization problem. In this example, we have two business workflows (W1 and W2) and four underlying IT resources (C1, C2, C3 and C4). The workflow specification module constructs the workflow-resource relationship matrix (see Fig. 7(b)). In this example, finding an optimized HA enhancement recommendation requires the iterative testing of various HA solutions and different redundancy degrees: for each IT resource, various HA solutions could be applicable, and for the redundancy-based HA solution, different redun￾dancy degrees should be explored. More generally, to make an optimal HA enhancement recommendation for an SOA deployment topology, three op￾timization dimensions should be iteratively explored: i) every IT resource in the deployment topology could be an HA enhancement candidate; ii) a variety of HA solutions could be applied to a given IT resource; and iii) each of these HA solutions could rely on different redundancy degrees. Such an iterative exploration requires exponential computation time. Due to this computation complexity, it is inapplicable for large-scale IT infrastructures, which are frequently used in real-world SOA environments. Let us now rigorously define this availability optimization problem. Fig. 7. SOA deployment topology example for problem definition We denote the n business workflows over the SOA de￾ployment topology as W1,W2,W3,...,Wn. These workflows are specified with availability requirements P1,P2,P3,...,Pn, where 0 < Pi < 1. We also assume that there are m IT resources, denoted by C1,C2,...,Cm. We construct a workflow-resource relationship matrix where IT resources depended upon by each workflow are identified; each matrix entry Rj,i expresses the dependency degree from workflow Wj to IT resource Ci as previously described. For instance, if IT resource Ci is referenced three times by a given business workflow Wj , its dependency degree Rj,i will be recorded as 3. A resource vector can be defined for a given workflow Wj as (hC1,Rj,1i, hC2,Rj,2i, ..., hCm,Rj,mi). For simplicity, we express this as (Rj,1,Rj,2,...,Rj,m). Based on the above definition, for a given IT resource Ci , its intrinsic availability capability (e.g., based on historical experience) is denoted by P(Ci), and its cost is denoted by h(Ci ,ni) where ni is the redundancy degree (e.g., cluster size) of this resource. For a given workflow Wj , its current availability capability can be calculated by P(Wj ). These calculation functions will be discussed in Section III.B. The optimization problem becomes finding a cost￾effective HA enhancement recommendation described as (hC1,n0 1 i,hC2,n0 2 i,...,hCm,n0 mi) where Ci denotes the original IT resource, and n 0 i denotes the new redundancy degree for Ci . This enhancement recommendation keeps the overall cost minimal while satisfying all availability requirements for each business workflow. Formally, the availability optimization problem is defined as a constrained optimization problem as follows: