QoE-driven computation offloading for Edge Computing

Luo, Jie; Deng, Xiaoheng; Zhang, Honggang; Qi, Huamei

doi:10.1016/j.sysarc.2019.01.019

Cited by 48 publications

(21 citation statements)

References 14 publications

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“…However, the QoS cannot always reflect the actual service quality experienced by the end-users. Authors in [21] focused more on quality of experience (QoE) that influence the interactivity of the services experienced by end-users.…”

Section: Related Workmentioning

confidence: 99%

SDN-Enabled Adaptive and Reliable Communication in IoT-Fog Environment Using Machine Learning and Multiobjective Optimization

Akbar

Jan

Wang

2021

IEEE Internet Things J.

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The Internet of Things (IoT) has inspired the development of emerging new applications. Backed by the resourceful fog computing, the IoT devices are capable to meet the demands of tasks, even the most computationally-intensive ones. However, many existing IoT applications are unable to perform well, while communicating with the fog server due to different QoS requirements. Constantly changing traffic demands of applications is another challenge to consider. The demand for real-time applications includes communicating over a path that is less prone to delay. Moreover, applications that offload computationally-intensive tasks to the fog server need a reliable path that has lower probability of link failure. This results in a trade-off among continuously changing objectives, i.e., minimizing the end-to-end delay and maximizing the reliability of paths between IoT devices and the fog server. To tackle this problem, we propose a novel approach that considers an SDNenabled multi-hop scenario for the IoT-Fog environment. We evaluated the reliability level of links by employing the real-life network statistics recorded for a period of over 5 months. We use a multi-objective optimization (MOO) algorithm to search the Pareto-optimal paths by considering the two conflicting objectives. Our experimental evaluation considers two different applications -a real-time application (App-1) using UDP sockets and a task offloading application (App-2) using TCP sockets. Our results show that: (i) using MOO, the trade-off between the two objectives can be optimized, and (ii) the SDN controller was able to make adaptive decision on-the-fly to choose the best path from the Pareto-optimal set. The App-1, communicating over the selected path finished its execution in 13% less time than communicating over the shortest path. The App-2 had 41% less packet loss using the selected path compared to using the shortest path.

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Section: Related Workmentioning

confidence: 99%

SDN-Enabled Adaptive and Reliable Communication in IoT-Fog Environment Using Machine Learning and Multiobjective Optimization

Akbar

Jan

Wang

2021

IEEE Internet Things J.

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“…The terminal handover in SDNbased WLANs can effectively improve the QoS and the resource utilization of WLANs. As a new computing paradigm, software-defined networking can also be applied to cellular networks [10], D2D heterogeneous networks [11], MESH networks [12], cloud computing [13], and edge computing [14] to facilitate resource allocation, provide service quality assurance, and improve user experience.…”

Section: Background and Motivationmentioning

confidence: 99%

Terminal handover in software-defined WLANs

Chen

Duan

Zhang

2020

J Wireless Com Network

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With the development of wireless LAN (WLAN) technology, wireless access points (APs) are deployed increasingly more and the number of mobile terminals is increasing. It is normal for a mobile terminal to hand over its AP. The traditional terminal handover method cannot effectively control the handover delay and balance the network load due to the inherent drawbacks such as closed encapsulation and the lack of centralized control. In this paper, we introduce the innovative network architecture of software-defined networking (SDN), propose a software-defined WLAN architecture, and propose a seamless handover method and a load balancing handover method of the softwaredefined WLAN. Experimental results show that the proposed handover method effectively reduces the handover delay, balances the network load, and improves the quality of service (QoS) and resource utilization of the network.

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“…en, the partial computation offloading algorithm with low time complexity was given to achieve the Nash equilibrium. In [23], the authors captured a user-centric view to tackle the offloading scheduling problem via jointly allocating communication and computation resources with consideration of the QoE of users where they formulated the design as a mix-integer nonlinear programming problem and solved it in an efficient way by the branch-and-bound method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Under this condition, it is very important to address the task scheduling problem in the edge computing. At present, there have been some task scheduling methods [17][18][19][20][21][22][23], including artificial intelligence (AI) based ones [24,25], but they usually cannot obtain the fast response speed and the low energy consumption. As a conclusion, it is necessary to explore the new method to address the task scheduling problem generated from the live broadcasting optimization.…”

Section: Introductionmentioning

confidence: 99%

In-Network Caching and Edge Computing-Based Live Broadcasting Optimization for Football Competitions

2020

Mobile Information Systems

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The business of football competitions is called the number one sport in the world, thanks to more than one billion people’s attention. With the development of big convergence media, the live broadcasting of football competitions gradually becomes industrialization and commercialization, which has a direct relationship with economic growth. For the live broadcasting of football competitions, the users focus more on quality of experience, i.e., definition and instantaneity. In terms of such two metrics, the current live broadcasting schemes are difficult to cover them well. Therefore, this paper exploits the emerging in-network caching and edge computing technologies to optimize the live broadcasting of football competitions, shorten for IELB. At first, the live broadcasting optimization framework based on in-network caching and edge computing is presented. Then, the auction-based method is used to address the task scheduling problem in the edge computing. In addition, a video compression algorithm based on adaptive convolution kernel is introduced to accelerate the video transmission and guarantee users to obtain the contents of football competitions as quickly as possible. The proposed IELB has been verified based on the collected real football competitions dataset by evaluating response time, and the experimental results demonstrate that IELB is feasible and efficient.

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QoE-driven computation offloading for Edge Computing

Cited by 48 publications

References 14 publications

SDN-Enabled Adaptive and Reliable Communication in IoT-Fog Environment Using Machine Learning and Multiobjective Optimization

SDN-Enabled Adaptive and Reliable Communication in IoT-Fog Environment Using Machine Learning and Multiobjective Optimization

Terminal handover in software-defined WLANs

In-Network Caching and Edge Computing-Based Live Broadcasting Optimization for Football Competitions

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