DMPO: Dynamic mobility-aware partial offloading in mobile edge computing

Yu, F. Richard; Chen, Haopeng; Xu, Jinqing

doi:10.1016/j.future.2018.07.032

Cited by 55 publications

(35 citation statements)

References 11 publications

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“…In [103], an online energy-aware mobility management scheme was developed, accounting for the radio handover and computation migration cost. An effective mobility-aware offloading decision algorithm was proposed in [108], by integrating mobility prediction. In [109], performance optimization under a long-term cost budget constraint was investigated.…”

Section: A Vehicle As a Clientmentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

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Section: A Vehicle As a Clientmentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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“…Yu et al [11] proposed a dynamic mobility-aware partial offloading (DMPO) method to save energy consumption under delay constraint, which the short-term mobility prediction characteristic is used and combined with the mobile communication path decision. This algorithm predicts the time of handover and assigns a specific data size in each time slot to achieve the purpose of minimizing energy consumption.…”

Section: Related Workmentioning

confidence: 99%

A Probability Preferred Priori Offloading Mechanism in Mobile Edge Computing

Wang

Liao

et al. 2020

IEEE Access

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Mobile edge computing (MEC) can provide computation and storage capabilities via edge servers which are closer to user devices (UDs). The MEC offloading system can be viewed as a system where each UD is covered by single or multiple edge servers. Existing works prefer a posterior design when task offloads, which can lead to increased workloads. To investigate the task offloading of edge computing in multi-coverage scenario and to reduce the workload during task offloading, a probability preferred priori offloading mechanism with joint optimization of offloading proportion and transmission power is presented in this paper. We first set up an expectation value which is determined by the offloading probability of heterogeneous edge servers, and then we form a utility function to balance the delay performance and energy consumption. Next, a distributed PRiori Offloading Mechanism with joint Offloading proportion and Transmission (PROMOT) power algorithm based on Genetic Algorithm (GA) is proposed to maximize the utility of UD. Finally, simulation results verify the superiority of our proposed scheme as compared with other popular methods. INDEX TERMS Probability preferred, workload, offloading, genetic algorithm, mobile edge computing.

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“…In [10], the decision is made by the orchestrator, while in our method, the decision is autonomously made by the mobile node itself dealing with heterogeneous operators of MEC servers. In [11], the authors proposed a centralized offloading algorithm implemented in CONCERT architecture (SDN for MEC) proposing a shortterm prediction of an offloading decision including the amount of data to be offloaded together with the communication path. Their goal is to minimize the energy consumption while satisfying the delay constraint.…”

Section: Related Work and Contributionmentioning

confidence: 99%

On the Optimality of Task Offloading in Mobile Edge Computing Environments

Alghamdi

Anagnostopoulos

Pezaros

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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Mobile Edge Computing (MEC) has emerged as new computing paradigm to improve the QoS of users' applications. A challenge in MEC is computation (task/data) offloading, whose goal is to enhance the mobile devices' capabilities to face the requirements of new applications. Computation offloading faces the challenges of where and when to offload data to perform computing (analytics) tasks. In this paper, we tackle this problem by adopting the principles of Optimal Stopping Theory contributing with two time-optimized sequential decision making models. A performance evaluation is provided using real world data sets compared with baseline deterministic and stochastic models. The results show that our approach optimizes such decision in single user and competitive users scenarios. Index Terms-Mobile edge computing, tasks offloading, optimal stopping theory, sequential decision making.

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DMPO: Dynamic mobility-aware partial offloading in mobile edge computing

Cited by 55 publications

References 11 publications

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Mobile Edge Intelligence and Computing for the Internet of Vehicles

A Probability Preferred Priori Offloading Mechanism in Mobile Edge Computing

On the Optimality of Task Offloading in Mobile Edge Computing Environments

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