Due to the parallel computing at tiered computing nodes, fog radio access network (RAN) can facilitate computation offloading. Apart from the parallel computing, communication and computation operations can be conduct...
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Due to the parallel computing at tiered computing nodes, fog radio access network (RAN) can facilitate computation offloading. Apart from the parallel computing, communication and computation operations can be conducted simultaneously at tiered computing nodes. In this regard, both the set and the order of tasks operated at each computing node have a significant impact on the task execution delay. Hence, we jointly optimize task offloading and scheduling to minimize the average task execution delay in fog RAN, followed by an effective recursive algorithm. Through application extensions and numerical evaluations, the proposed algorithm is verified with scalability and efficacy.
Fog computing systems have been widely integrated in IoT-based applications to improve quality of services (QoS) such as low response service delays. This improvement is enabled by task offloading schemes, which perfo...
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Fog computing systems have been widely integrated in IoT-based applications to improve quality of services (QoS) such as low response service delays. This improvement is enabled by task offloading schemes, which perform task computation near the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, reducing delay remains challenging for offloading strategies owing to the resource limitations of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of the workload distribution among the heterogeneous fog devices, which severely impacts the offloading performance in terms of delay. To address these issues, this paper proposes a dynamic collaborative task offloading (DCTO) approach, which is based on the resource states of fog devices, to dynamically derive the task offloading policy. Accordingly, a task can be executed by either a single fog or multiple fog devices through the parallelcomputation of subtasks to reduce the task execution delay. Through extensive simulation analysis, the proposed offloading solution showed potential advantages in reducing the average delay significantly in systems with a high rate of service requests and heterogeneous fog environment compared with the existing solutions. In addition, the proposed scheme can be implemented online owing to its low computational complexity compared with the algorithms proposed in related works.
Fog computing systems (FCS) have been widely integrated in the IoT-based applications aiming to improve the quality of services (QoS) such as low response service delay by performing the task computation nearby the ta...
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ISBN:
(数字)9781728175683
ISBN:
(纸本)9781728175683
Fog computing systems (FCS) have been widely integrated in the IoT-based applications aiming to improve the quality of services (QoS) such as low response service delay by performing the task computation nearby the task generation sources (i.e., IoT devices) on behalf of remote cloud servers. However, to achieve the objective of delay reduction remains challenging for offloading strategies due to the resource limitation of fog devices. In addition, a high rate of task requests combined with heavy tasks (i.e., large task size) may cause a high imbalance of workload distribution among the heterogeneous fog devices. To cope with the situation, this paper proposes a dynamic task offloading (DTO) approach, which is based on the resource states of fog devices to derive the task offloading policy dynamically. Accordingly, a task can be executed by either a single fog or multiple fog devices through parallelcomputation of subtasks to reduce the task execution delay. Through the extensive simulation analysis, the proposed approaches show potential advantages in reducing the average delay significantly in the systems with high rate of service requests and heterogeneous fog environment compared with the existing solutions.
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