A Survey of Computation Offloading in Edge Computing

Zheng, Tao; Wan, Jian; Zhang, Jilin; Jiang, Congfeng; Jia, Gangyong

doi:10.1109/cits49457.2020.9232457

Cited by 36 publications

(26 citation statements)

References 34 publications

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“…Noor-A-Rahim et al [21] have surveyed and classified RA schemes based on the type of network they are used in DSRC, Cellular and heterogeneous vehicular networks. Zheng et al [22] present a survey on computational offloading in edge computing based on different offloading scenarios among edge servers, IoT devices, and cloud servers), factors (device, network, service and user factors) and strategies (full and partial offloading). They also discuss program partitioning and the efficient selection of program components to offload.…”

Section: Related Workmentioning

confidence: 99%

A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren

Gaurav

Sahu

et al. 2021

Complex Intell. Syst.

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The number of vehicles is increasing at a very high rate throughout the globe. It reached 1 billion in 2010, in 2020 it was around 1.5 billion and experts say this could reach up to 2–2.5 billion by 2050. A large part of these vehicles will be electrically driven and connected to a vehicular network. Rapid advancements in vehicular technology and communications have led to the evolution of vehicular edge computing (VEC). Computation resource allocation is a vehicular network’s primary operations as vehicles have limited onboard computation. Different resource allocation schemes in VEC operate in different environments such as cloud computing, artificial intelligence, blockchain, software defined networks and require specific network performance characteristics for their operations to achieve maximum efficiency. At present, researchers have proposed numerous computation resource allocation schemes which optimize parameters such as power consumption, network stability, quality of service (QoS), etc. These schemes are based on widely used optimization and mathematical models such as the Markov process, Shannon’s law, etc. So, there is a need to present an organized overview of these schemes to help in the future research of the same. In this paper, we classify state-of-the-art computation resource allocation schemes based on three criteria: (1) Their optimization goal, (2) Mathematical models/algorithms used, and (3) Major technologies involved. We also identify and discuss current issues in computation resource allocation in VEC and mention the future research directions.

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Section: Related Workmentioning

confidence: 99%

A survey on computation resource allocation in IoT enabled vehicular edge computing

Naren

Gaurav

Sahu

et al. 2021

Complex Intell. Syst.

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“…Smart systems are built using a large number of IoT devices, which generate huge amount of data in a short period of time. e generated data is sent to the cloud for aggregation, analytics, and computation [15][16][17]. Computation offloading to the cloud decreases the computation load from IoT devices.…”

Section: Related Workmentioning

confidence: 99%

“…For all K jobs, total energy consumption E total is calculated using equation (15), and the total delay for offloading the task to the cloud is computed using the following equation:…”

Section: Objective Functionmentioning

confidence: 99%

Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization

et al. 2021

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Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service (QoS) becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony (ABC) optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Round-Robin (RR) Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications.

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“…The work in [43] highlights the significance of edge computing by providing real-life scenarios that have low application response time. Authors in [44] introduces the key issues through the offloading process, such as whether, where, and what to offload. Authors in [45] analyze and compare the existing computing offloading algorithms from the perspective of the minimum latency, energy consumption and trade off between both of them.…”

Section: Related Workmentioning

confidence: 99%

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

2021

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Indoor localization has become one of the fundamental services that is required in a diverse set of applications these days, such as patient monitoring and smart parking. Highly accurate localization techniques impose high latency and high energy consumption on the underlying application system. Thus, for such indoor location-based application, offloading the computation of the localization process to a remote server with high resource capability has been recently introduced as an avenue to address such a challenge. In this paper, a computation offloading problem is formulated to find the optimal decision with regard to the operation of the localization process. This decision includes: a) Where to compute the localization task, either locally on the end device or on the edge server or on the cloud server, b) Which localization technique should be used, and finally, c) Which transmission technology is recommended to be chosen in combination with the localization technique. All these decisions are constrained by the device, and the servers resource capabilities load. They are also constrained by the fact that the localization algorithm has to satisfy a certain application QoS requirement. Within such context, three algorithms are proposed for task offload decision making. First, the Indoor Localization Latency Optimal Offloading algorithm, which finds the optimal offloading decision that minimizes the total latency of the system and is considered a benchmark for the other algorithms. Second, Indoor Localization Latency Centralized Offloading algorithm that finds a sub optimal solution with lower complexity. Third, Indoor Localization Latency Game-Theoretic Offloading decentralized algorithm that converges after finite improvement steps and achieves Nash equilibrium. Altogether, the paper finds the optimum localization strategy for all users with the minimum latency under mobile edge computing environment.

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A Survey of Computation Offloading in Edge Computing

Cited by 36 publications

References 34 publications

A survey on computation resource allocation in IoT enabled vehicular edge computing

A survey on computation resource allocation in IoT enabled vehicular edge computing

Intelligent Computation Offloading for IoT Applications in Scalable Edge Computing Using Artificial Bee Colony Optimization

A Minimized Latency Collaborative Computation Offloading Game Under Mobile Edge Computing for Indoor Localization

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