Collaborative Service Placement, Task Scheduling, and Resource Allocation for Task Offloading With Edge-Cloud Cooperation

Fan, Wenhao; Zhao, Liang; Liu, Xun; Su, Yi; Li, Shenmeng; Wu, Fan; Liu, Yuanan

doi:10.1109/tmc.2022.3219261

Cited by 43 publications

(5 citation statements)

References 33 publications

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“…We assume that there is just one location [23] where the tasks 𝕤 can be carried out, which can be stated as follows:…”

Section: Expositionmentioning

confidence: 99%

“…Hence, the offloading decisions can be redefined as In this situation, when each Fog node runs independently, our primary aim is to reduce each Fog node's service delay, resulting in a reduction in overall service delay. The service type arrangement described in [13,29,23], we set the number of service types to 𝕊 = 8. When 𝕥 = 1, the largest number of combinations possible in Eq.…”

Section: Non-cooperative Fog Nodesmentioning

confidence: 99%

“…Every Fog node has a defined coverage of the geographical region, and mobile IoT users can assign the Fog node certain tasks for computation process. More specifically, the Fog node has to deploy certain services to carry out certain computational operations [23]. Initially, we cache fractional services to Fog nodes from the Cloud server (caching of services).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Mahapatra,

Pradhan,

Majhi

et al. 2024

Preprint

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Cloud computing including mobile IoT devices infrastructure have grown to be a key component of future high-performance computing networks because they can supply distributed, hierarchical, and fine-grained resources. The secret is to combine the optimization of compute offloading and service caching. However, dynamic tasks, heterogeneous resources, and coupled decisions pose three major obstacles to the joint service caching and compute offloading dilemma. In this paper, we study how Dew-assisted mobile IoT devices and Fog-Cloud computing networks can benefit from the simultaneous processing for caching of various services and computational offloading. We formulate the optimization problem specifically as minimizing the NP-hard long-term average service delay. We provide thorough theoretical analysis and divide the topic into two smaller issues, i.e., processing for computation offloading and service caching. To answer the defined problem, where several Dew-assisted mobile IoT devices and a Cloud VM jointly determine the caching-action and offloading-action, respectively, we developed a unique Distributed-Deep Reinforcement Learning (DDRL) technique. The suggested framework beats multiple existing techniques regarding the average service delay across several scenarios, according to the findings of trace-driven simulations. When compared to methods based on reinforcement learning, our framework delivers an impressive 39% reduction in the average service delay and an approximate 37% improvement in convergence based on a practical real-world environment.

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“…We assume that there is just one location [23] where the tasks 𝕤 can be carried out, which can be stated as follows:…”

Section: Expositionmentioning

confidence: 99%

Section: Non-cooperative Fog Nodesmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Mahapatra,

Pradhan,

Majhi

et al. 2024

Preprint

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show abstract

“…Yang et al proposed the novel cloud-edge-end orchestrated computing scheme for reducing energy consumption and obtaining optimal policies by repairing missing values in [7]. In [8], Fan et al proposed the task unloading and resource scheduling plan to realize the overall task handling latency optimization in cloud-edgeend network. However, despite the rapid progress achieved by aforementioned studies, several open issues remain unaddressed, which are summarized as below.…”

Section: Introductionmentioning

confidence: 99%

Cloud-edge-end Collaborative Multi-service Resource Management for IoT-based Distribution Grid

WANG,

WEN,

et al. 2024

IEICE Trans. Fundamentals

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The rapid advancement of cloud-edge-end collaboration offers a feasible solution to realize low-delay and low-energy-consumption data processing for internet of things (IoT)-based smart distribution grid. The major concern of cloud-edge-end collaboration lies on resource management. However, the joint optimization of heterogeneous resources involves multiple timescales, and the optimization decisions of different timescales are intertwined. In addition, burst electromagnetic interference will affect the channel environment of the distribution grid, leading to inaccuracies in optimization decisions, which can result in negative influences such as slow convergence and strong fluctuations. Hence, we propose a cloud-edge-end collaborative multi-timescale multi-service resource management algorithm. Large-timescale device scheduling is optimized by sliding window pricing matching, which enables accurate matching estimation and effective conflict elimination. Small-timescale compression level selection and power control are jointly optimized by disturbance-robust upper confidence bound (UCB), which perceives the presence of electromagnetic interference and adjusts exploration tendency for convergence improvement. Simulation outcomes illustrate the excellent performance of the proposed algorithm.

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“…Table 1 summarizes some research with optimization objectives and scheduling approaches. ✓ ✓ [20] ✓ ✓ [23] ✓ ✓ [24] ✓ ✓ ✓ [26] ✓ ✓ [27] ✓ ✓…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Xu,

Li,

Zhang

et al. 2024

Processes

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Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

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Collaborative Service Placement, Task Scheduling, and Resource Allocation for Task Offloading With Edge-Cloud Cooperation

Cited by 43 publications

References 33 publications

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Intelligent Caching and Offloading Technique for Dew-assisted Mobile IoTs: A Distributed Deep Reinforcement Learning Framework

Cloud-edge-end Collaborative Multi-service Resource Management for IoT-based Distribution Grid

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

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