基于三維的柴油機(jī)氣缸體三面鉆削組合機(jī)床總體及夾具設(shè)計(jì)

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The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply and demand for resources and allocating them for applications based on the users quality of services requirements. The framework requires economy driven deadline and budget constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users requirements are met. In this paper, we propose a new scheduling algorithm, called DBC cost-time optimisation, which extends the DBC cost-optimisation algorithm to optimise for time, keeping the cost of computation at the minimum. The superiority of this new scheduling algorithm, in achieving lower job completion time, is demonstrated by simulating the World-Wide Grid and scheduling task-farming applications for different deadline and budget scenarios using both this new and the cost optimisation scheduling algorithms. 1 Introduction Computational Grids 1 and peer-to-peer (P2P) computing 2 networks are emerging as next generation computing platforms for solving large-scale computational and data intensive problems in science, engineering, and commerce. They enable the sharing, selection and aggregation of a wide variety of geographically distributed resources including supercomputers, storage systems, databases, data sources, and specialized devices owned by different organizations. However, resource management and application scheduling is a complex undertaking due to large-scale heterogeneity present in resources, management policies, users, and applications requirements in these environments 14. The resources are heterogeneous in terms of their architecture, power, configuration, and availability. They are owned and managed by different organizations with different access policies and cost models that vary with time, users, and priorities. Different applications have different computational models that vary with the nature of the problem. The resource owners and consumers/end-users have different goals, objectives, strategies, and demand patterns. In our earlier work 56, we investigated the use of economics as a metaphor for management of resources in Grid computing environments. The computational economy framework provides a mechanism for regulating the supply-and-demand for resources and allocating them to applications based on the users quality of services requirements 14. It also offers an incentive to resource owners for sharing resources on the Grid and end-users trade-off between the timeframe for result delivery and computational expenses. A Grid scheduler, often called resource broker, acts as an interface between the user and distributed resources and hides the complexities of Grid computing 4. It performs resource discovery, negotiates for access costs using trading services, maps jobs to resources (scheduling), stages the application and data for processing (deployment), starts job 2 execution, and finally gathers the results. It is also responsible for monitoring and tracking application execution progress along with adapting to the changes in Grid runtime environment, variation in resource share availability, and failures. In our Grid economy framework, the resource brokers use economy driven deadline and budget constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users requirements are met. In our early work 6, we developed three scheduling algorithms for cost, time, and time-variant optimisation strategies that support deadline and budget constraints. We implemented them within the Nimrod-G broker and explored their capability for scheduling task-farming or parameter-sweep and data-intensive computing applications such as drug design 10 on the WWG (World-Wide Grid) 7 testbed resources. To meet users quality of service requirements, the broker leases Grid resources and services dynamically at runtime depending on their capability, cost, and availability. In this work, we propose a new scheduling algorithm, called DBC cost-time optimisation, which extends the DBC cost-optimisation algorithm to optimise for time keeping the cost of computation at the minimum. Resources with the same cost are grouped together and time-optimisation scheduling strategy is applied while allocating jobs to a group. We demonstrate the ability of this new scheduling algorithm by implementing it within the economic Grid resource broker simulator built using the GridSim toolkit 3. The performance of this new algorithm is evaluated by scheduling a synthetic task farming application on simulated WWG testbed resources for different deadline and budget scenarios. We then compare and contrast the results of scheduling with the cost optimisation algorithm. A number of projects are investigating scheduling on distributed systems 14. They include Grid scheduling systems such as AppLeS 11 and NetSolve 12, which use system-centric scheduling strategies; and REXEC 13, which supports computational economy-based resource management in a single administrative domain cluster computing environments. The rest of this paper is organized as follows. Section 2 presents the GridSim broker architecture and internal components that simulate and manage the execution of task farming applications along with scheduling algorithm. The simulation of heterogeneous resources with different capabilities and access costs, creation of synthetic application, and evaluation of proposed cost-time optimisation scheduling algorithm against the cost optimisation algorithm are discussed in Section 3. The final section summarises the paper along with suggestions for future work. 2 Grid Broker Simulation and DBC Cost-Time Optimisation Algorithm The GridSim toolkit 3 is used to simulate Grid environment and a Nimrod-G like deadline and budget constrained scheduling system called economic Grid resource broker. The simulated Grid environment contains multiple resources and user entities with different requirements. The user and broker entities extend the GridSim class. All the users create experiments that each of which contains application specification (a set of Gridlets that represent application jobs with different processing) and quality of service requirements (deadline and budget constraints with optimisation strategy). When the simulation starts, the user entity creates an instance of its own broker entity and passes a request for processing application jobs. 2.1 Broker Architecture The broker entity architecture and its interaction with other entities is shown in Figure 1. The key components of the broker are: experiment interface, resource discovery and trading, scheduling flow manager backed with scheduling heuristics and algorithms, Gridlets dispatcher, and Gridlets receptor. A detailed discussion on the broker implementation using the GridSim toolkit can be found in 3. However, to enable the understanding of the broker framework in which the new scheduling algorithm is implemented, we briefly present its operational model: 1. The user entity creates an experiment that contains application description (a list of Gridlets to be processed) and user requirements to the broker via the experiment interface. 2. The broker resource discovery and trading module interacts with the GridSim GIS entity to identify contact information of resources and then interacts with resources to establish their configuration and access cost. It creates a Broker Resource list that acts as a placeholder for maintaining resource properties, a list of Gridlets committed for execution on the resource, and the resource performance data as predicted through the measurement and extrapolation methodology. 3. The scheduling flow manager selects an appropriate scheduling algorithm for mapping Gridlets to resources depending on the user requirements (deadline and budget limits; and optimisation strategycost, cost-time, time, or time variant). Gridlets that are mapped to a specific resource are added to the Gridlets list in the Broker Resource. 3 4. For each of the resources, the dispatcher selects the number of Gridlets that can be staged for execution according to the usage policy to avoid overloading resources with single user jobs. 5. The dispatcher then submits Gridlets to resources using the GridSims asynchronous service. 6. When the Gridlet processing completes, the resource returns it to the brokers Gridlet receptor module, which then measures and updates the runtime parameter, resource or MI share available to the user. It aids in predicting the job consumption rate for making scheduling decisions. 7. The steps, 36, continue until all the Gridlets are processed or the broker exceeds the deadline or budget limits. At the end, the broker returns updated experiment data along with processed Gridlets back to the user entity. R1Rm.CT optimizeCost optimizeTime optimizeNone Opt.Resource Discovery and TradingGridlet ReceptorDispatcher. . . .16427Experiment Interface35Scheduling Flow ManagerR1R2RnUserEntity(Broker Resource List and Gridlets Q)GISBroker EntityGrid Resources Figure 1: Economic Grid resource broker architecture and its interaction with other entities. 2.2 Deadline and Budget Constrained Cost-Time Optimisation Scheduling Algorithm We have simulated deadline and budget constrained (DBC) scheduling algorithms, cost-optimisation 3, time-optimisation, and time-variant optimisation, presented in 6. A new scheduling algorithm, called cost-time optimisation, proposed in this paper is shown in Figure 2. It extends the cost-optimisation algorithm to optimise the time without incurring additional processing expenses. This is accomplished by applying the time-optimisation algorithm to schedule task-farming or parameter-sweep application jobs on distributed resources having the same processing cost. The performance evaluation of this new algorithm is presented in the next section. 3 Scheduling Simulation and Performance Evaluation To simulate application scheduling in GridSim environment using the economic Grid broker requires the modeling and creation of GridSim resources and applications that model jobs as Gridlets. In this section, we present resource and application modeling along with the results of scheduling experiments with quality of services driven application processing. 3.1 Resource Modeling We modeled and simulated a number of time- and space-shared resources with different characteristics, configuration, and capability as those in the WWG testbed. We have selected the latest CPUs models AlphaServer ES40, Sun Netra 20, Intel VC820 (800EB MHz, Pentium III), and SGI Origin 3200 1X 500MHz R14k released by their manufacturers Compaq, Sun, Intel, and SGI respectively. The processing capability of these PEs in simulation time-unit is modeled after the base value of SPEC CPU (INT) 2000 benchmark ratings published in 8. To enable the users to model and express their application processing requirements in terms of MI (million instructions) or MIPS (million instructions per second) on the standard machine, we assume the MIPS rating of PEs is same as the SPEC rating. Table 1 shows the characteristics of resources simulated and their PE cost per time unit in G$ (Grid 4 dollar). The simulated resources resemble the WWG testbed resources used in the Nimrod-G scheduling experiments reported in 9. The access cost of PE in G$/time-unit not necessarily reflects the cost of processing when PEs have different capability. The brokers need to translate it into the G$ per MI for each resource. Such translation helps in identifying the relative cost of resources for processing Gridlets on them. It can be noted some of the resources in Table 1 have the same MIPS per G$. For example, both R4 and R8 have the same cost and so resources R2, R3, and R10. Figure 2: Deadline and budget constrained (DBC) scheduling with cost-time optimisation. Algorithm: DBC_Scheduling_with_Cost_Time_Optimisation() 1. RESOURCE DISCOVERY: Identify the resources and their capability using the Grid information services. 2. RESOURCE TRADING: Identify the cost of all resources and the capability to be delivered per cost-unit. The resource cost can be expressed in units such as processing cost-per-MI, cost-per-job, CPU cost per time unit, etc. and the scheduler needs to choose suitable unit for comparison. 3. If the user supplies D and B-factors, then determine the absolute deadline and budget based on the capability of resources and their cost, and the application processing requirements (e.g., total MI required). 4. SCHEDULING: Repeat while there exists unprocessed jobs and the current time and processing expenses are within the deadline and budget limits. It is triggered for each scheduling event or whenever a job completes. The event period is a function of deadline, job processing time, rescheduling overhead, resource share variation, etc.: SCHEDULE ADVISOR with Policy a. For each resource, predict and establish the job consumption rate or the available resource share through the measure and extrapolation strategy taking into account the time taken to process previous jobs. b. SORT the resources by increasing order of cost. If two or more resources have the same cost, order them such that powerful ones (e.g., higher job consumption rate or resource share availability, but the first time based on the total theoretical capability, say the total MIPS) are preferred first. c. Create resource groups containing resources with the same cost. d. SORT the resource groups with the increasing order of cost. e. If any of the resource has jobs assigned to it in the previous scheduling event, but not dispatched to the resource for execution and there is variation in resource availability, then move appropriate number of jobs to the Unassigned-Jobs-List. This helps in updating the whole schedule based on the latest resource availability information. f. Repeat the following steps for each resource group as long as there exists unassigned jobs: i. Repeat the following steps for each job in the Unassigned-Jobs-List depending on the processing cost and the budget availability: It uses the time optimisation strategy. Select a job from the Unassigned-Jobs-List. For each resource, calculate/predict the job completion time taking into account previously assigned jobs and the job completion rate and resource share availability. Sort resources by the increasing order of completion time. Assign the job to the first resource and remove it from the Unassigned-Jobs-List if the predicted job completion time is less than the deadline. 5. DISPATCHER with Policy Repeat the following steps for each resource if it has jobs to be dispatched: Identify the number of jobs that can be submitted without overloading the resource. Our default policy is to dispatch jobs as long as the number of user jobs deployed (active or in queue) is less than the number of PEs in the resource. 5 Table 1: World-Wide Grid testbed resources simulated using GridSim. Resource Name in Simulation Simulated Resource Characteristics Vendor, Resource Type, Node OS, No of PEs Equivalent Resource in Worldwide Grid (Hostname, Location) A PE SPEC/ MIPS Rating Resource Manager Type Price (G$/PE time unit) MIPS per G$ R0 Compaq, AlphaServer, CPU, OSF1, 4 VPAC, Melb, Australia 515 Time-shared 8 64.37 R1 Sun, Ultra, Solaris, 4 AIST, Tokyo, Japan 377 Time-shared 4 94.25 R2 Sun, Ultra, Solaris, 4 AIST, Tokyo, Japan 377 Time-shared 3 125.66 R3 Sun, Ultra, Solaris, 2 AIST, Tokyo, Japan 377 Time-shared 3 125.66 R4 Intel, Pentium/VC820, Linux, 2 CNR, Pisa, Italy 380 Time-shared 1 380.0 R5 SGI, Origin 3200, IRIX, 6 ZIB, Berlin, Germany 410 Time-shared 5 82.0 R6 SGI, Origin 3200, IRIX, 16 ZIB, Berlin, Germany 410 Time-shared 5 82.0 R7 SGI, Origin 3200, IRIX, 16 Charles U., Czech Republic 410 Space-shared 4 102.5 R8 Intel, Pentium/VC820, Linux, 2 Portsmouth, UK 380 Time-shared 1 380.0 R9 SGI, Origin 3200, IRIX, 4 Manchester, UK 410 Time-shared 6 68.33 R10 Sun, Ultra, Solaris, 8, ANL, Chicago, USA 377 Time-shared 3 125.66 3.2 Application Modeling We have modeled a task farming application that consists of 200 jobs. In GridSim, these jobs are packaged as Gridlets whose contents include the job length in MI, the size of job input and output data in bytes along with various other execution related parameters when they move between the broker and resources. The job length is expressed in terms of the time it takes to run on a standard resource PE with SPEC/MIPS rating of 100. Gridlets processing time is expressed in such a way that they are expected to take at least 100 time- units with a random variation of 0 to 10% on the positive side of the standard resource. That means, Gridlets job length (processing requirements) can be at least 10,000 MI with a random variation of 0 to 10% on the positive side. This 0 to 10% random variation in Gridlets job length is introduced to model heterogeneous tasks similar to those present in the real world parameter sweep applications. 3.3 Scheduling Experiments We performed both cost and cost-time optimisation scheduling experiments with different values of deadline and budget constraints (DBC) for a single user. The deadline is varied in simulation time from 100 to 3600 in steps of 500. The budget is varied from G$ 5000 to 22000 in steps of 1000. A trace of resource selection and allocation using cost and cost-time optimisation scheduling strategies shown in Figure 3 indicates their impact on the application processing completion time. When the deadline is tight (e.g., 100), there is high demand for all the resources in short time, the impact of cost and cost-time scheduling strategies on the completion time is similar as all the resources are used up as long as budget is available to process all jobs within the deadline (see Figure 3a and Figure 3b). However, when the deadline is relaxed (e.g., 3100), it is likely that all jobs can be completed using the first few cheapest resources. In this experiment there were resources with the same cost and capability (e.g., R4 and R8), the cost optimisation strategy selected resource R4 to process all the jobs (see Figure 3c); whereas the cost-time optimisation strategy selected both R4 and R8 (see Figure 3d) since both resources cost the same price and completed the experiment earlier than the cost-optimisation scheduling. This situation can be observed clearly in scheduling experiments with a large budget for different deadline values (see Figure 4). Note that 6 the left most solid curve marked with the label “All” in the resources axis in Figure 4 represents the aggregation of all resources. As the deadline increases, the cost optimisation algorithm predominantly scheduled jobs on the resource R4 (see Figure 4a) whereas, the cost-time optimisation algorithm scheduled jobs on resources R4 and R8 (see Figure 4b), the first two cheapest resources with the same cost. Therefore, the application scheduling using the cost-time optimisation