Collaborative Task Scheduling for IoT-Assisted Edge Computing

Kim, Youngjin; Song, Chi-won; Han, Hyuck; Jung, Hyungsoo; Kang, Sooyong

doi:10.1109/access.2020.3041872

Cited by 34 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Clearly, this architecture is suitable for processing tasks with a small amount of computation and strict delay requirements, as shown in Figure 3 . The study [ 12 ] has adopted it.…”

Section: Computing Scenariosmentioning

confidence: 99%

“…Clearly, this architecture is suitable for processing tasks with a small amount of computation and strict delay requirements, as shown in Figure 3. The study [12] has adopted it. According to this scenario, the task can be scheduled to an edge server for handling by an edge scheduler.…”

Section: Scheduler-based Edge Computingmentioning

confidence: 99%

“…It is noted that edge computing systems can be viewed as a special class of distributed computing systems. Traditional task scheduling in distributed computing focuses on distributing and scheduling a large task into multiple similarly powerful computing nodes and do not have task off-loading issues in edging computing [ 9 , 10 , 11 , 12 ]. Edging computing arises to handle an IoT scenario where edge devices are resource-constrained and relatively independent.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Chen

Zhou

et al. 2021

Sensors

View full text Add to dashboard Cite

In edge computing, edge devices can offload their overloaded computing tasks to an edge server. This can give full play to an edge server’s advantages in computing and storage, and efficiently execute computing tasks. However, if they together offload all the overloaded computing tasks to an edge server, it can be overloaded, thereby resulting in the high processing delay of many computing tasks and unexpectedly high energy consumption. On the other hand, the resources in idle edge devices may be wasted and resource-rich cloud centers may be underutilized. Therefore, it is essential to explore a computing task collaborative scheduling mechanism with an edge server, a cloud center and edge devices according to task characteristics, optimization objectives and system status. It can help one realize efficient collaborative scheduling and precise execution of all computing tasks. This work analyzes and summarizes the edge computing scenarios in an edge computing paradigm. It then classifies the computing tasks in edge computing scenarios. Next, it formulates the optimization problem of computation offloading for an edge computing system. According to the problem formulation, the collaborative scheduling methods of computing tasks are then reviewed. Finally, future research issues for advanced collaborative scheduling in the context of edge computing are indicated.

show abstract

“…Clearly, this architecture is suitable for processing tasks with a small amount of computation and strict delay requirements, as shown in Figure 3 . The study [ 12 ] has adopted it.…”

Section: Computing Scenariosmentioning

confidence: 99%

Section: Scheduler-based Edge Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Chen

Zhou

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…He et al [22] propose an effective method to find the best solution to optimize the system allocation time, transmit power and CPU on each device, and maximize the amount of data that two users can process in a given time frame. Kim et al [23] propose a new concept of IoT-assisted edge computing, which provides edge services by integrating idle resources in IoT devices and offloading tasks to nearby IoT devices.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

Wang

2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the continuous progress of edge computing technology and the development of the Internet of Things technology, scenarios such as smart transportation, smart home, and smart medical care enable people to enjoy the smart era’s convenience. Simultaneously, with the addition of many smart devices, a large number of tasks are submitted to the edge server, making the edge server unable to meet the needs of completing tasks submitted by the smart device. Besides, if the task is submitted to the remote cloud data center, it increases the user’s additional delay and cost. Therefore, it is necessary to improve the task offloading strategy and resource allocation scheme to solve these problems. This paper first proposes a new task offloading mechanism and then proposes a two-stage Stackelberg game model to solve each participant’s interaction problem in the task offloading mechanism and ensure the maximization of their respective interests. Finally, a theoretical analysis proves the equilibrium of the two-stage Stackelberg game. Experiments are used to prove the effectiveness of the proposed mechanism. Comparative experimental results show that the proposed model can achieve better results regarding delay and energy consumption.

show abstract

“…Task scheduling problems are investigated in several aspects such as architecture, independent tasks, offloading tasks completely or partially, executing tasks sequentially or in parallel, considering services composing some tasks, task as a unit or dividing to several sub-tasks [6] [7] [8] [9].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Mahjoubi

Taheri

Grinnemo

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

View full text Add to dashboard Cite

By increasing the number of IoT-devices, cloudcomputing faces challenges for some computation and timesensitive applications. Edge-computing has emerged to enable IoT-devices offload their computation tasks. Offloading tasks is a complex and challenging issue. We propose a comprehensive model including user, edge and cloud layers for scheduling continuous offering of services. Furthermore, we modeled the tasks of service as recurrent (repetitive) with a given frequency. The serviceplacement problem is formulated as a Mixed-Integer Linear Programming problem that aims to minimize the total delay of all services. We solve the problem with CPLEX, and proposed four fast heuristics to find near-optimal solutions. We compared the results of our proposed heuristics with the result obtained with CPLEX, in terms of problem-solving speed and accuracy, as well as resource utilization of all nodes. The results show that two of our proposed heuristics produce near-optimal solutions in a fraction of the time taken by CPLEX.Index Terms-edge computing, service placement, IoT devices, cloud computing• We propose a comprehensive model that includes all layers (user, edge, and cloud). It models three main node parameters (CPU, RAM, and network capacities), as well as the link

show abstract

Collaborative Task Scheduling for IoT-Assisted Edge Computing

Cited by 34 publications

References 36 publications

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Recent Advances in Collaborative Scheduling of Computing Tasks in an Edge Computing Paradigm

Cooperative Edge Computing Task Offloading Strategy for Urban Internet of Things

Optimal Placement of Recurrent Service Chains on Distributed Edge-Cloud Infrastructures

Contact Info

Product

Resources

About