REaltime ACtive Heterogeneous Systems - Where Did We Reach After REACH?

Kudrass, Thomas

doi:10.1007/978-3-642-17226-7_3

Cited by 1 publication

(4 citation statements)

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“…EDF (Earliest Deadline First) is one of the best classic algorithms that have so far been introduced for rule scheduling in real-time systems [2], [5], [7]. The EDF based approach, which is constrained approach, schedules active rules based on their deadlines in RADBs (Real-time Active Databases).…”

Section: Edf Based Scheduling Approachmentioning

confidence: 99%

“…At instant k, based on the assumption that the actions belonging to are active, the selection probabilities of the actions are updated according to relations ( 6), (7), and ( 8): (6) a) The favorable response from the environment in the response of :…”

Section: How Does a Learning Automaton Help Us Improve The Rule Scheduling Process?mentioning

confidence: 99%

“…The three first criteria are used to evaluate the performance and effectiveness of rule scheduling approaches, and the latter two criteria are used to evaluate the efficiency of rule scheduling approaches. These criteria are formally defined in Table (7). In this table, T is the total cumulative time that an approach has been spent on scheduling active rules in the operational environment of ADSS, and T* is the total time spent on the execution of the action sections of active rules during a test.…”

Section: The Proposed Evaluation Frameworkmentioning

confidence: 99%

“…Update the selection probability of each of the rules whose action sections are members of , according to relation(7) Update the sum of the selection probabilities of the rules whose action sections are members of , according to relation(6) Update the selection probability of each of the rules whose action sections are members of , according to relation(8) Select one of the activated rules according to the probability of their selection (at an instant k)Yes NoIs the condition section of the selected rule evaluated to true? : The selection probability of the action (rule ) calculated by E X -SJF PRO -V.2.8 at the end of in scale The selection probability of the action (rule ) calculated by the learning automaton at the end ofThe selection probability of the action (rule ) used by the learning automaton at the beginning of :…”

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confidence: 99%

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A new approach to active rule scheduling

Rasoolzadegan

Alesheykh

Meybodi

2015

Engineering Applications of Artificial Intelligence

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Active database systems (ADSs) react automatically to the occurrence of predefined events by defining a set of active rules. One of the main modules of an ADS is the rule scheduler, which has a significant impact on the effectiveness and efficiency of ADSs. During the rule scheduling process, the rule scheduler is responsible for choosing one of the activated or ready-to-be-executed rules to evaluate its condition section or execute its action section, respectively. This process continues until there is no rule to be evaluated or executed. In this research, we evaluate and compare existing rule scheduling approaches in a laboratory environment based on a three-tier architecture. There are criteria used for the evaluation and comparison of rule scheduling approaches: Average Response Time, Throughput, Response Time Standard Deviation, Time Overhead per Transaction, and CPU Utilization. The three first criteria are used to evaluate the effectiveness, and the latter two criteria are used to evaluate the efficiency of rule scheduling approaches. In this paper, a new approach, referred to as EX¬-SJF¬EsTLA, is proposed to improve the rule scheduling process, using a learning automaton. In our laboratory environment, EX¬-SJF¬EsTLA is compared with those rule scheduling approaches that are unconstrained as EX¬-SJF¬EsTLA is. Unconstrained scheduling approaches serially schedule the rules that do not have any priorities or deadlines. The results of experiments revealed that the proposed approach improved the rule scheduling process according to the evaluation criteria.

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Section: Edf Based Scheduling Approachmentioning

confidence: 99%

Section: How Does a Learning Automaton Help Us Improve The Rule Scheduling Process?mentioning

confidence: 99%

Section: The Proposed Evaluation Frameworkmentioning

confidence: 99%

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confidence: 99%

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