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IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.5, May 2008

Figure 5 IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.5, May 2008

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a task. A deadline by itself cannot express both urgency and importance. In this paper, we consider the time/utility functions (or TUFs) that express the utility of completing a task as an application-specific function of when a task completes. This formulation was initially proposed by Jensen in [2], [3]. In this paper we specify the deadline constraint of a task as a binary-valued, downward step shaped TUF. As illustrated in Fig 1, a TUF decouples importance and urgency- i.e., urgency is measured as a deadline on the X-axis and importance is denoted by utility on the Y-axis. Fig. | The Step TUF that specify deadline and importance [1].
a task. A deadline by itself cannot express both urgency and importance. In this paper, we consider the time/utility functions (or TUFs) that express the utility of completing a task as an application-specific function of when a task completes. This formulation was initially proposed by Jensen in [2], [3]. In this paper we specify the deadline constraint of a task as a binary-valued, downward step shaped TUF. As illustrated in Fig 1, a TUF decouples importance and urgency- i.e., urgency is measured as a deadline on the X-axis and importance is denoted by utility on the Y-axis. Fig. | The Step TUF that specify deadline and importance [1].
Fig. 2 The calculation of Potential Utility Density (or PUD).
Fig. 2 The calculation of Potential Utility Density (or PUD).
Table 1: Performance analysis technique used in the UA scheduling algorithms 3.1 Simulation Model
Table 1: Performance analysis technique used in the UA scheduling algorithms 3.1 Simulation Model
3.2 Performance Metrics
3.2 Performance Metrics
During the execution time of a task, it may request one or more shared resources. Fig 5 shows the possibility of having a nested requested time interval. In Fig 5, a task request three resources, which are R1, R3 and R5. The Fig 4 shows the entities involve in our simulation study. It consists of a stream of 1000s task that are exponentially generated, an unordered task list, the proposed scheduler and a set of resources. The Held_ Resource entity denotes the set of resources that is currently held by a task. These tasks are assumed to be independent of each other.
During the execution time of a task, it may request one or more shared resources. Fig 5 shows the possibility of having a nested requested time interval. In Fig 5, a task request three resources, which are R1, R3 and R5. The Fig 4 shows the entities involve in our simulation study. It consists of a stream of 1000s task that are exponentially generated, an unordered task list, the proposed scheduler and a set of resources. The Held_ Resource entity denotes the set of resources that is currently held by a task. These tasks are assumed to be independent of each other.
Table 2 summarized the details task settings in our simulation. Given the task execution time C_AVG and a load factor load, the average task inter-arrival time iat can be calculated as C_AVG/load. The iat is applied to exponential distribution as depicted in Table 2. The maximum utility of each task follows normal distribution. task requests resource R5, then request resource R3 before it releases resource R5. Thus, the time interval of task holding resource R5 and R3 are considered as nested.
Table 2 summarized the details task settings in our simulation. Given the task execution time C_AVG and a load factor load, the average task inter-arrival time iat can be calculated as C_AVG/load. The iat is applied to exponential distribution as depicted in Table 2. The maximum utility of each task follows normal distribution. task requests resource R5, then request resource R3 before it releases resource R5. Thus, the time interval of task holding resource R5 and R3 are considered as nested.
Related topics:
Computer ScienceInformation Technology ManagementReal Time SystemsComputer AlgorithmsScheduling Algorithm
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