A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang, Shudong; Li, Yanqing; Pang, Shanchen; Lu, Qinghua; Zhao, Jianli

doi:10.1155/2020/3967847

Cited by 14 publications

(14 citation statements)

References 28 publications

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“…Moreover, this scheme is equally beneficial for network operators and third-party service providers. Unsimilar, Wang et al [40] proposed a novel strategy for task scheduling to the Edge Cloud paradigm by introducing a cataclysm strategy based on Catastrophic Genetic Algorithms that improve the makespan.…”

Section: Scheduling Problems In the Edge Cloudmentioning

confidence: 99%

“…These are not for a specific problem and find solutions near to optimal. Here, we have categorized the meta-heuristic scheduling schemes into PSO Based Approach [31], Artificial Fish Swarm Based Approach [52], and Genetic Algorithm Based Approach [40].…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

“…The following work depicted the GA based scheduling strategies. Wang et al [40] discusses a technique that is based on Genetic Algorithm (GA). The penalty function is used for the determination of time execution based on delays.…”

Section: Genetic Algorithm Based Approachmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

Asghar

Jadoon

2022

Preprint

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After the advent of the Internet of Things and 5G networks, edge computing became the 1 center of attraction. The tasks demanding high computation are generally offloaded to the cloud since 2 the edge is resource-limited. The Edge Cloud is a promising platform where the devices can offload 3 delay-sensitive workloads. In this regard, scheduling holds great importance in offloading decisions 4 in the Edge Cloud collaboration. The ultimate objectives of scheduling are the quality of experience, 5 minimizing latency, and increasing performance. An abundance of efforts on scheduling has been 6 done in the past. In this paper, we have surveyed proposed scheduling strategies in the context of 7 edge cloud computing in various aspects such as advantages and demerits, QoS parameters, and fault 8 tolerance. We have also surveyed such scheduling approaches to evaluate which one is feasible under 9 what circumstances. We first classify all the algorithms into heuristic algorithms and meta-heuristics, 10 and we subcategorize algorithms in each class further based on extracted attributes of algorithms. 11 We hope that this survey will be very thoughtful in the development of new scheduling techniques. 12 Issues, challenges, and future directions have also been examined.

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Section: Scheduling Problems In the Edge Cloudmentioning

confidence: 99%

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

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A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

Asghar

Jadoon

2022

Preprint

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“…It can decide whether to offload to clouds depending on network condition and input size. Wang et al [36] proposed a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to satisfy the latency constraint. In [37], a novel DNN architecture was design for edge-cloud collaborative inference.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Task Migration Scheduling Approach for Edge-Cloud Collaborative Inference

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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Deep Neural Network (DNN) models have achieved excellent performance in many inference tasks and have been widely used in many intelligent applications. However, DNN models often require a lot of computational resources to complete the inference tasks, which hinders the deployment of such models to resource-constrained edge devices. In order to extend the application scenarios of DNN models, the edge-cloud collaborative inference methods, represented by model partition, have attracted much research attention in recent years. In scenarios that have multiple edge devices deployed, the edge-cloud collaborative inference method requires partial migration of tasks, but traditional scheduling methods only migrate tasks at the task level. In this paper, we propose two task scheduling methods, which can solve the problem of partial migration of tasks in multiedge scenarios. The first scheduling method is based on the optimal cutting of a single DNN. The cutting positions of all the models are the same, regardless of the influence of external factors. This method is suitable for chain and directed acyclic graph- (DAG-) type DNNs. The second scheduling method takes external factors such as congestion and queuing delay at the cloud side into consideration, which dynamically selects the cutting position of each DNN to optimize the overall delay and thus is applicable to chain DNN models. The experimental results show that, compared with the baseline method, our proposed scheduling method can reduce the delay by up to 6.48x.

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“…In these works, every user integrates data coming from some sensors which can be executed locally or offloaded to the edge/cloud servers. The authors of [19] proposed a method by employing a genetic algorithm that aims to provide an optimal task allocation subject to pre-set deadline constraints. The aim of this study was to minimize the execution time of all tasks.…”

Section: Related Workmentioning

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)

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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

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A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Cited by 14 publications

References 28 publications

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions

An Adaptive Task Migration Scheduling Approach for Edge-Cloud Collaborative Inference

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

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