An Improved Task Scheduling Algorithm for Intelligent Control in Tiny Mechanical System

Wireless sensor network (WSN) has been already widely used in many fields in terms of industry, agriculture, and military, and so forth. The basic composition is WSN nodes that are capable of performing processing, gathering information, and communicating with other connected nodes in the network. The main components of a WSN node are microcontroller, transceiver, and some sensors. Undoubtedly, it also can be added with some actuators to form a tiny mechanical system. Under this case, the existence of task preemption while executing operating system will not only cost more energy for WSN nodes themselves, but also bring unacceptable system states caused by vibrations. However for these nodes, task I/O delays are inevitable due to the existence of task preemption, which will bring extra overhead for the whole system, and even bring unacceptable system states caused by vibrations. This paper mainly considers the earliest deadline first (EDF) task preemption algorithm executed in WSN OS and proposes an improved task preemption algorithm so as to lower the preemption overhead and I/O delay and then improve the system performance. The experimental results show that the improved task preemption algorithm can reduce the I/O delay effectively, so the real-time processing ability of the system is enhanced.

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Adaptive Scheduling for Real-Time Distributed Systems

Advances in Data Mining and Database Management - Biologically-Inspired Techniques for Knowledge Discovery and Data Mining ◽

10.4018/978-1-4666-6078-6.ch011 ◽

2014 ◽

pp. 236-248 ◽

Cited By ~ 1

Author(s):

Apurva Shah

Keyword(s):

Data Mining ◽

Distributed Systems ◽

Real Time ◽

Scheduling Algorithm ◽

Fast Response ◽

Biologically Inspired ◽

Real Time Processing ◽

Time Systems ◽

Earliest Deadline ◽

Single Processor

Biologically inspired data mining techniques have been intensively used in different data mining applications. Ant Colony Optimization (ACO) has been applied for scheduling real-time distributed systems in the recent time. Real-time processing requires both parallel activities and fast response. It is required to complete the work and deliver services on a timely basis. In the presence of timing, a real-time system's performance does not always improve as processor and speed increases. ACO performs quite well for scheduling real-time distributed systems during overloaded conditions. Earliest Deadline First (EDF) is the optimal scheduling algorithm for single processor real-time systems during under-loaded conditions. This chapter proposes an adaptive algorithm that takes advantage of EDF- and ACO-based algorithms and overcomes their limitations.

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Research and Design on the New Type Wheel Loader Electronic Scale

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.2667 ◽

2013 ◽

Vol 694-697 ◽

pp. 2667-2670

Author(s):

Cheng Ji Ouyang ◽

Hai Xian Xu ◽

Hong Juan Zhang ◽

Bin Huang

Keyword(s):

Operating System ◽

Real Time ◽

Can Bus ◽

Real Time Processing ◽

Wheel Loader ◽

Time Processing ◽

Processing Ability ◽

Communication Module ◽

New Type ◽

Research And Design

Specific to the problems of current loader weighing device, for example, lack of accuracy and poor real-time processing ability, this paper designed a new type loader electronic scale and introduced μC/OS-II operating system into this system. Besides, GPRS communication module is added and the digital sensor is installed. At the same time, CAN bus is applied to carry out information transmission, which effectively improves the accuracy, real-time processing ability and anti-interference ability of loader electronic scales.

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Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841125 ◽

2018 ◽

pp. 96-101

Author(s):

Shailendra Raghuvanshi ◽

Priyanka Dubey

Keyword(s):

Load Balancing ◽

Task Scheduling ◽

Waiting Time ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Cloud Environment ◽

Task Scheduling Algorithm ◽

Machine Utilization ◽

Independent Tasks ◽

Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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