Enterprise Service Computing Scientific_1

Zhao, Jeng, An, Cao, Bryant, Hauser, & Tao Figure 9. The business-process model of a supply chain Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis-sion of Idea Group Inc. is prohibited. Algnng Busness Processes wth Enterprse Servce Computng Infrastructure Figure 10. The SDM of the business-process model of Figure 9 Figure 11. The causality tree for ShippingRate Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Zhao, Jeng, An, Cao, Bryant, Hauser, & Tao gular boxes) and each has its own in- and outflows. We omitted the part of handling payment for simplicity. The stock “Order Backlog” in Figure 10 corresponds to the data repository “Order Backlog” in Figure 9. The activity “Place Order” by the customer will have the effect of increasing the stock “Order Backlog” and chang-ing the inflow “Demand Rate”. Similarly, the activity “Shipping out Products” after the storage would have the effect of decreasing the stock and changing the outflow “Fulfillment Rate.” The stock “Finished Product” is the result of the activity “Make Product” and will be reduced by the activity “Shipping out Products.” The stock “Part WIP” is increased by “Order Parts” and is reduced by “Receive Parts”, and the stock “Part Inventory” will be increased due to the effect of “Receive Parts” and reduced by the activity “Make Product.” It is less clear how the activities in a business-process model affect in- and outflow rates for each stock. In the business-process model, dependencies can be expressed graphically only through data streams. Any additional dependencies could be buried in the input criterion and output criterion of the data stream for each task. It is pos-sible to use built-in expressions or plugged-in programming languages to express the input and output criteria. On the other hand, the dependencies in an SDM can be expressed graphically with the notion of polarity, wherein the positive polarity (denoted by a plus sign) represents reinforcement feedback loops and the negative polarity (denoted by a minus sign) represents those feedback loops that can reach equilibrium. The influence map can be created by connecting direction links. Based on the visual influence map, low-level metrics can be synthesized by capturing the causal relationships in the form of mathematical formulas. The key to bridging the gap between the business operations and the ITsystems is to establish the dynamic causal relationships between IT-level and business-level performance metrics in the mathematical formulas. Figure 11 gives an example causality tree for “Ship-pingRate.” The business-level variables are shown in yellow rectangular boxes; the IT-level variables are shown in green rectangular boxes. This causality tree corresponds to the business activities between “Check Order and Product Status” and “Shipping out Product” shown in Figure 9. We discuss this causality tree in a bottom-up order. Let us assume the above inventory system is monitored and controlled by a main process wherein inventory processes can handle client requests through network connections one at a time (this example is adjusted from Diao, Hellerstein, Parekh, & Bigus, 2003). Therefore, the number of inventory processes is constrained by the maximum number of connections, say “MaxClientConnections,” provided by the system. The controller monitors connections and manages their life cycles. If the connection has been idle over a certain amount of time, say “ConnectionLife-UpperBound,” the connection is terminated or returned to the connection pool. A higher “MaxClientConnections” value allows the system to process more inventory Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis-sion of Idea Group Inc. is prohibited. Algnng Busness Processes wth Enterprse Servce Computng Infrastructure requests and increases both CPU (central processing unit) and memory utilization. Decreasing the value of “ConnectionLifeUpperBound” potentially allows inventory processes to be more active, leading to higher CPU and memory utilization. The above description can be formulated as follows (Diao et al.): CPU = A * CPU + A * MEM + B * MaxClientConnections + B * Con-nectionLifeUpperBoundk and MEM = A * CPU + A * MEM + B * MaxClientConnections + B * Con-nectionLifeUpperBoundk, whereCPU andMEM representthevalues ofCPU andmemoryutilizationatthekth timeinterval.ThemetricsMaxClientConnections andConnectionLifeUpperBound representthevaluesof“MaxClientConnections”and“ConnectionLifeUpperBound” at the kth time interval. The entries A and B represent modeling parameters at the IT level that can be obtained using statistical methods. In every time period, the controller has to decide how much of the available resources (CPU, memory) to allocate to each inventory process. How to realize this control strategy is beyond the scope of this chapter. Here, we only show how the business-level and IT-level metrics can be linked using the causality relationships of an SDM. The metrics “MinimalProcessTime” at the business level depends on the metrics at the IT level such as CPU and memory utilization. Good “MinimalProcessTime” is ensured by reserving sufficient capability to handle workload spikes. A particular IT-system configuration by tuning parameters such as “MaxClientConnections” and “ConnectionLifeUpperBound” gives a particular “MinimalProcessTime.” “DesiredShippingTime” can be assigned a proper value based on the average values of real processing time. The “DesiredShippingRate” is determined by “OrderBack-log” and “DesiredShippingTime”: DesiredShi pingRate= De OrderBacklgTime. The “MaximalShippingRate” is determined by “FinishedProduct” and “Minimal-ProcessingTime”: Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Zhao, Jeng, An, Cao, Bryant, Hauser, & Tao FinishedProduct MinimalProcessingTime Finally, the “ShippingRate” is determined by “DesiredShippingRate” and “Maxi-malShippingRate”: ShippingRate = min DesiredShippingRate,MaximalShippingRate . An SDM tends to represent a deterministic model and is used to study the overall behavior in a certain given timescale. Compared to the business-process model, the SDM aims to cover the detailed product-shipping activities by mimicking real-world events on a smaller scale. The assigned values in an SDM can be obtained from the simulation results of the business-process model or the average of the real data that are identified in the business artifacts of the business-process model. We give a few more examples of how to synthesize low-level metrics to meet overall service requirements. The stock “Finished Product” is increased through the activity “Make Product” by using “Product Demand” that comes from “Forecast Demand” and “Adjust Product Based on Finished Product.” The forecast model we have here is the exponential smoothing of historical data: SmoothedDemandRate = Smooth(DemandRate,SmoothFactor), where the function smooth is one of the built-in functions in the SDM tool. The adjustment from “Finished Product” introduces a negative feedback loop to rebal-ance the amount of products we should assemble: DesiredProductInventory =smoothedDemandRate*ProductInventoryCoverage DesiredAssemblingRate =SmoothedDemandRate+ DesiredProvectInventory − FinishedPr duct. The real assembling rate could be delayed: AssemblingRate = DelayFixed(DesiredAssemblingRate,DelayTime). The part usage rate is transformed from “Assembling Rate” using “Bill of Mate-rial”: Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permis-sion of Idea Group Inc. is prohibited. ... - tailieumienphi.vn