Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing

Alameddine, Hyame Assem; Sharafeddine, Sanaa; Sebbah, Samir; Ayoubi, Sara; Assi, Chadi

doi:10.1109/jsac.2019.2894306

Cited by 276 publications

(108 citation statements)

References 25 publications

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“…After duplicating VNFs and designing the latency map, the VNF forwarder distribute the newly arrived packets according to the application latency conditions. To cope with tasks offloading, application resource allocation and task scheduling problems in a MEC environment, a dynamic task offloading, and scheduling approach is de- [88] • Sun et al [90] • Li et al [92] • Zhu et al [94] • • • Dinh et al [93] • • Nam et al [114] • Ren et al [115] • Yang et al [116] • Son et al [44] • Alameddine [117] • Chen et al [118] • Li et al [119] • • • Chen et al [120] • • • Ahn et al [121] • Li et al [122] • Liang et al [123] • Yang et al [124] • Liu et al [125] • • • Blanco et al [126] • Zhang et al [127] • Chen et al [128] • Yang et al [129] • Xu et al [130] • [113] • • Tun et al [144] • signed in [117] for IoT applications. This approach aims to maximize the number of accepted tasks to be handled by IoT applications hosted on MEC servers, while meeting latency requirements.…”

Section: A Optimization Approaches For Latencymentioning

confidence: 99%

“…bandwidth and CPU). In [43] a VNF placement approach is proposed which considers the existence of edge servers and cloud servers in order to provide network services Optimization approach SDN NFV SFC 5G Slicing Yala et al [38] • • Cziva et al [40] • • Yang et al [41] • • Leivadeas et al [43] • Kaur et al [61] • [114] • • Ren et al [115] • Yang et al [116] • • Son et al [44] • • • Alameddine et al [117] • • Chen et al [118] • Li et al [119] • • • Chen et al [120] • Ahn et al [121] • • Li et al [122] • • Liang et al [123] • Blanco et al [126] • • Yang et al [124] • Liu et al [125] • Zhang et al [127] • Chen et al [128] • • Guo et al [131] • Tran et al [132] • Zhang et al [133] • Zhao et al [135] • Wang et al [143] • • • Sanchez et al [111] • Tun et al [145] • Martiradonna et al [146] • Taleb et al [113] • • • Mena et al [103] • Wang t al. [112] • Xiang et al [147] • • Tun et al [144] •…”

Section: Optimization Approaches For Combined Resourcesmentioning

confidence: 99%

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Multi-Access Edge Computing: A Survey

et al. 2020

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Multi-access Edge Computing (MEC) is a key solution that enables operators to open their networks to new services and IT ecosystems to leverage edge-cloud benefits in their networks and systems. Located in close proximity from the end users and connected devices, MEC provides extremely low latency and high bandwidth while always enabling applications to leverage cloud capabilities as necessary. In this paper, we illustrate the integration of MEC into a current mobile networks' architecture as well as the transition mechanisms to migrate into a standard 5G network architecture. We also discuss SDN, NFV, SFC and network slicing as MEC enablers. Then, we provide a state-of-the-art study on the different approaches that optimize the MEC resources and its QoS parameters. In this regard, we classify these approaches based on the optimized resources and QoS parameters (i.e., processing, storage, memory, bandwidth, energy and latency). Finally, we propose an architectural framework for a MEC-NFV environment based on the standard SDN architecture.

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Section: A Optimization Approaches For Latencymentioning

confidence: 99%

Section: Optimization Approaches For Combined Resourcesmentioning

confidence: 99%

Multi-Access Edge Computing: A Survey

et al. 2020

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“…The cooperation computing has been investigated among the works [17]- [19] under different network environments, while the impact of user mobility has not been addressed. Additionally, in [12], [20], and [21], task scheduling, i.e., ordering the task execution sequences, is also evaluated in the offloading decision making process. In those works, the task scheduling problem is formulated into a mixed-integer programming problem, and heuristic algorithms are proposed to obtain nearoptimal solutions efficiently.…”

Section: A Mobile Edge Computingmentioning

confidence: 99%

Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

Gao

Zhao

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

173

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Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.

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“…In terms of time delay, Alameddine et al [16] jointly tackled task offloading and order of execution by considering different requirements and task latency. They mainly considered five delays: uploading, edge-to-edge, waiting, processing, and downloading.…”

Section: Mobile Edge Computingmentioning

confidence: 99%

“…The details of the proposed algorithm are summarized in Algorithm 1. Get f according to (16), (17) or (39).…”

Section: Algorithm and Complexity Analysismentioning

confidence: 99%

Joint time delay and energy optimization with intelligent overclocking in edge computing

Wang

Xiong

Chen

et al. 2020

Sci. China Inf. Sci.

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Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing

Cited by 276 publications

References 25 publications

Multi-Access Edge Computing: A Survey

Multi-Access Edge Computing: A Survey

Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

Joint time delay and energy optimization with intelligent overclocking in edge computing

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