Workload scheduling toward worst‐case delay and optimal utility for single‐hop Fog‐IoT architecture

Deng, Yanhua; Chen, Zhigang; Zhang, Deyu; Zhao, Ming

doi:10.1049/iet-com.2018.5077

Cited by 46 publications

(33 citation statements)

References 25 publications

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“…The research community have conducted the task scheduling from various aspects, like makespan reduction, resource sharing, energy saving, fault tolerance and so on. In addition to the scheduling in cloud data centers, it is also a hot topic in Internet of Things (IoT) system [8][9][10], and Mobile Edge Computing (MEC) [11,12]. In this paper, we focus on the big data analysis task scheduling issue in cloud data centers.…”

Section: Related Workmentioning

confidence: 99%

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Wang

Abawajy

et al. 2020

Energies

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Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

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Section: Related Workmentioning

confidence: 99%

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Wang

Abawajy

et al. 2020

Energies

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“…Task scheduling is a traditional topic that involves transferring tasks to the external platform due to the limited computational power, storage and energy of the mobile device [20], which can improve computing efficiency, reduce task completion time, and utilize resources efficiently from other devices in the system [22,23]. Therefore, it has been extensively studied in wireless networks [24,25] With the development of computing-intensive and time-sensitive applications, task scheduling is becoming a research hotspot for MEC. Mao et al develop a dynamic task scheduling method for an MEC system with an energy harvest mobile device and an edge server [25].…”

Section: Mobile Edge Computingmentioning

confidence: 99%

An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

Liu

Chen

et al. 2019

Electronics

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With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots; it can also predict the location of the vehicle and return the result to the vehicle.

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“…Chen and Zhang [15,16] proposed multi-user MEC computing offloading problem in multi-channel wireless environment, and designed distributed game theory offloading scheme. Chen [17] employed a method of offloading to a mobile cloud network by predicting bandwidth and computing rate to minimize resource consumption and meet delay requirements.Deng [18] proposed a workload dynamic scheduling algorithm, which can maximize the average throughput utility while guaranteeing the task processing delay in the worst case. The above papers only consider the computational offloading of performance problems, since the efficiency will be greatly reduced in case of connection interruption problems.…”

Section: Related Workmentioning

confidence: 99%

“…In this part, we compare with the following algorithms: (i) the offloading game algorithm in [16] called Zhang’s algorithm; (ii) the resource scheduling algorithm in [18] called Deng’s algorithm. Figure 11 shows the completion time and energy consumption of the task at different input data sizes.…”

Section: Performance Analysismentioning

confidence: 99%

An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges

Cui

Zhao

Wong

2019

Sensors

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Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.

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Workload scheduling toward worst‐case delay and optimal utility for single‐hop Fog‐IoT architecture

Cited by 46 publications

References 25 publications

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges

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