• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4103-4121
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• 2018

Quality of Service (QoS) awareness is recognized as a key point for the success of Internet of Things (IOT).Realizing the full potential of the Internet of Things requires, a real-time task scheduling algorithm must be designed to meet the QoS need. In order to schedule tasks with diverse QoS requirements in cloud environment efficiently, we propose a task scheduling strategy based on dynamic priority and load balancing (DPLB) in this paper. The dynamic priority consisted of task value density and the urgency of the task execution, the priority is increased over time to insure that each task can be implemented in time. The scheduling decision variable is composed of time attractiveness considered earliest completion time (ECT) and load brightness considered load status information which by obtain from each virtual machine by topic-based publish/subscribe mechanism. Then sorting tasks by priority and first schedule the task with highest priority to the virtual machine in feasible VMs group which satisfy the QoS requirements of task with maximal. Finally, after this patch tasks are scheduled over, the task migration manager will start work to reduce the load balancing degree.The experimental results show that, compared with the Min-Min, Max-Min, WRR, GAs, and HBB-LB algorithm, the DPLB is more effective, it reduces the Makespan, balances the load of VMs, augments the success completed ratio of tasks before deadline and raises the profit of cloud service per second.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.10
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• pp.855-868
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• 2004

There are several types of singularities in controlling robotic manipulators: kinematic singularity, algorithmic singularity, semi-kinematic singularity, semi-algorithmic singularity, and representation singularity. The kinematic and algorithmic singularities have been investigated intensively because they are not predictable or difficult to avoid. The problem with these singularities is an unnecessary performance reduction in non-singular region and the difficulty in performance tuning. Tn this paper, we propose a method of avoiding kinematic and algorithmic singularities by applying a task reconstruction approach while maximizing the task performance by calculating singularity measures. The proposed method is implemented by removing the component approaching the singularity calculated by using singularity measure in real time. The outstanding feature of the proposed task reconstruction method (TR-method) is that it is based on a local task reconstruction as opposed to the local joint reconstruction of many other approaches. And, this method has dynamic task priority assignment feature which ensures the system stability under singular regions owing to the change of task priority. The TR-method enables us to increase the task controller gain to improve the task performance whereas this increase can destabilize the system for the conventional algorithms in real experiments. In addition, the physical meaning of tuning parameters is very straightforward. Hence, we can maximize task performance even near the singular region while simultaneously obtaining the singularity-free motion. The advantage of the proposed method is experimentally tested by using the 7-dof spatial manipulator, and the result shows that the new method improves the performance several times over the existing algorithms.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.11
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• pp.573-580
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• 2001

When tasks access shared resources in real-time systems, the unbounded priority inversion may occur. In such cases it is impossible to guarantee the schedulability of real-time tasks. Several resource access protocols have been proposed to bound the duration of priority inversion and sufficient conditions are given to guarantee the schedulability of periodic task set. In this paper, we show an improved sufficient condition for schedulability when the dynamic priority ceiling protocol is used. Our approach exploits the fact that a lower priority task can continue to execute as far as the higher priority tasks do not miss their deadlines. This permitting execution time of the higher priority tasks for a lower priority task can be excluded from the worst-case blocking time of the higher priority tasks. Since the worst-case blocking time of tasks can be reduced, the sufficient condition for schedulability of dynamic priority ceiling protocol becomes further tight.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1265-1278
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• 2017

Dynamic task scheduling is one of the most popular research topics in the cloud computing field. The cloud scheduler dynamically provides VM resources to variable cloud tasks with different scheduling strategies in cloud computing. In this study, we utilized a valid model to describe the dynamic changes of both computing facilities (such as hardware updating) and request task queuing. We built a novel approach called Policy Iteration Scheduling (PIS) to globally optimize the independent task scheduling scheme and minimize the total execution time of priority tasks. We performed experiments with randomly generated cloud task sets and varied the performance of VM resources using Poisson distributions. The results show that PIS outperforms other popular schedulers in a typical cloud computing environment.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.7
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• pp.22-28
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• 2009

As multiprocessors have been widely adopted in embedded systems, task computation energy consumption should be minimized with several low power techniques supported by the multiprocessors. This paper proposes an energy-aware task scheduling algorithm that adopts both dynamic voltage scaling and power shutdown in multiprocessor environments. Considering the timing and energy overhead of power shutdown, the proposed algorithm performs an iterative task assignment and task ordering for multiprocessor systems. In this case, the iterative priority-based task scheduling is adopted to obtain the best solution with the minimized total energy consumption. Total energy consumption is calculated by considering a linear programming model and threshold time of power shutdown. By analyzing experimental results for standard task graphs based on real applications, the resource and timing limitations were analyzed to maximize energy savings. Considering the experimental results, the proposed energy-aware task scheduling provided meaningful performance enhancements over the existing priority-based task scheduling approaches.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.499-506
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• 2019

An intelligent robot should decide its behavior accordingly to the dynamic changes in the environment and user's requirements by evaluating options to choose the best one for the current situation. Many intelligent robot systems that use the Procedural Reasoning System (PRS) accomplishes such task management functions by defining the priority functions in the task model and evaluating the priority functions of the applicable tasks in the current situation. The priority functions, however, are defined locally inside of the plan, which exhibits limitation for the tasks for multi-domain services because global contexts for overall prioritization are hard to be expressed in the local priority functions. Furthermore, since the prioritization functions are not defined as an explicit module, reuse or extension of the them for general context is limited. In order to remove such limitations, we propose a policy-based meta-planning for general task management for multi-domain services, which provides the ability to explicitly define the utility of a task in the meta-planning process and thus the ability to evaluate task priorities for general context combining the modular priority functions. The ontological specification of the model also enhances the scalability of the policy model. In the experiments, adaptive behavior of a robot according to the policy model are confirmed by observing the appropriate tasks are selected in dynamic service environments.

• ETRI Journal
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• v.29 no.3
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• pp.259-269
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• 2007

The ubiquitous flexible operating system (UbiFOS) is a real-time operating system designed for cost-conscious, low-power, small to medium-sized embedded systems such as cellular phones, MP3 players, and wearable computers. It offers efficient real-time operating system services like multi-task scheduling, memory management, inter-task communication and synchronization, and timers while keeping the kernel size to just a few to tens of kilobytes. For flexibility, UbiFOS uses various task scheduling policies such as cyclic time-slice (round-robin), priority-based preemption with round-robin, priority-based preemptive, and bitmap. When there are less than 64 tasks, bitmap scheduling is the best policy. The scheduling overhead is under 9 ${\mu}s$ on the ARM926EJ processor. UbiFOS also provides the flexibility for user to select from several inter-task communication techniques according to their applications. We ported UbiFOS on the ARM9-based DVD player (20 kB), the Calm16-based MP3 player (under 7 kB), and the ATmega128-based ubiquitous sensor node (under 6 kB). Also, we adopted the dynamic power management (DPM) scheme. Comparative experimental results show that UbiFOS could save energy up to 30% using DPM.

• ETRI Journal
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• v.29 no.3
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• pp.270-280
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• 2007

In Linux, real-time tasks are supported by separating real-time task priorities from non-real-time task priorities. However, this separation of priority ranges may not be effective when real-time tasks make the system calls that are taken care of by the kernel threads. Thus, Linux is considered a soft real-time system. Moreover, kernel threads are configured to have static priorities for throughputs. The static assignment of priorities to kernel threads causes trouble for real-time tasks when real-time tasks require kernel threads to be invoked to handle the system calls because kernel threads do not discriminate between real-time and non-real-time tasks. We present a dynamic kernel thread scheduling mechanism with weighted average priority inheritance protocol (PIP), a variation of the PIP. The scheduling algorithm assigns proper priorities to kernel threads at runtime by monitoring the activities of user-level real-time tasks. Experimental results show that the algorithms can greatly improve the unexpected execution latency of real-time tasks.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.103-109
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• 2019

The automotive electronic system should provide a method that can be safely performed by loading a number of application programs having time constraints in several electronic control devices. In this paper, we propose a timing protection mechanism for AUTOSAR, which is a real - time operating system specification for automotive field, in order to observe the deadline of each task when scheduling real - time tasks. We propose a dynamic non-preemption algorithm to guarantee a flexible deadline for fixed priority or dynamic priority tasks, and a location where execution time can be monitored for errors, and suggest ways to implement the AUTOSAR time protection mechanism.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4A
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• pp.393-401
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• 2006

Energy consumption is an important design parameter for battery-operated embedded systems. Dynamic power management is one of the most well-known low-power design techniques. This paper proposes an online realtime scheduling algorithm, which we call energy-aware realtime scheduling using slack stealing (EARSS). The proposed algorithm gives the highest priority to the task with the largest degree of device overlap when the slack time exists. Scheduling result enables an efficient power management by reducing the number of state transitions. Experimental results show that the proposed algorithm can save the energy by 23% on average compared to the DPM-enabled system scheduled by the EDF algorithm.