Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration

Chen, Xingyan; Xu, Changqiao; Wang, Mu; Wu, Zhonghui; Zhong, Lujie; Grieco, Luigi Alfredo

doi:10.1109/tcsvt.2020.3047859

Cited by 19 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mobile edge computing is attracting widespread attention and being applied to various scenarios, including transcoding for livecast services [15], software defined networking (SDN) [16], information centric networking (ICN) [17] and 5G environments [18]. Due to the limited resources of edge nodes, the collaboration between cloud and edge is necessary, which has attracted many researches and development efforts.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Computing Offloading With Fairness Guarantee: A Deep Reinforcement Learning Method

Hao

Zhang

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

View full text Add to dashboard Cite

Edge computing can reduce service latency and save backhaul bandwidth by completing services at network edges, providing support for diverse computation-intensive and delaysensitive services. However, it is not practical to support all services at edge nodes due to the limited network resources. The decision that which services can be provided locally and which services should been offloaded to cloud significantly impacts the user experience. Cloud-edge computing offloading becomes an important issue in edge computing. In this paper, we take the fairness into the optimization objective of computing offloading problem, and consider both computing capacity and storage space as problem constraints. The problem is formulated as a long-term average optimization problem to maximize the αfair utility function of saved time, and further translated as a Markov decision process. As the optimization problem with fairness guarantee and huge action space, we cannot solve it with traditional methods. Therefore, an innovative multi-update deep reinforcement learning algorithm is proposed which can optimize the objective with α-fair utility function and reduce dramatically the size of action space. We also prove the convergence of our algorithm theoretically. To our best knowledge, the longterm average optimization of computing offloading with fairness guarantee is rarely seen in literature. Extensive simulation experiments show that our algorithm can converge quickly and has better performance in terms of service delay and fairness.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Our goal is to optimize the fairness utility U (T s k ) not the long-term average T s k , so we need to adjust the MDP of original problem (15). It is easy to know that the past reward history of services affects the fairness of decision.…”

Section: B Model Adjustment and Analysismentioning

confidence: 99%

Computing Offloading With Fairness Guarantee: A Deep Reinforcement Learning Method

Hao

Zhang

et al. 2023

IEEE Trans. Circuits Syst. Video Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Each service station can be considered as an independent M/M/1 model based on the theorem existing in queuing networks provided that there is infinite queue capacity in all stations and their efficiency coefficient is less than one. Accordingly, the sign of an entry is the same as the sign of customer entry [3]. Figure 1…”

Section: Statement Of the Problem And Modelingmentioning

confidence: 99%

“…𝑋 ! "* ; The objective function (3) summates the customer and the waiting time in the system, whereas objective function (4) minimizes the maximum possibility of unemployment at various levels of all facilities. Equations (5-8) are related to the linearization of objective functions.…”

Section: Stmentioning

confidence: 99%

Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Syah

Elveny

Soerjati

et al. 2022

Foundations of Computing and Decision Sciences

View full text Add to dashboard Cite

Using hubs in distribution networks is an efficient approach. In this paper, a model for the location-allocation problem is designed within the framework of the queuing network in which services have several levels, and customers must go through these levels to complete the service. The purpose of the model is to locate an appropriate number of facilities among potential locations and allocate customers. The model is presented as a multi-objective nonlinear mixed-integer programming model. The objective functions include the summation of the customer and the waiting time in the system and the waiting time in the system and minimizing the maximum possibility of unemployment in the facility. To solve the model, the technique of accurate solution of the epsilon constraint method is used for multi-objective optimization, and Pareto solutions of the problem will be calculated. Moreover, the sensitivity analysis of the problem is performed, and the results demonstrate sensitivity to customer demand rate. Based on the results obtained, it can be concluded that the proposed model is able to greatly summate the customer and the waiting time in the system and reduce the maximum probability of unemployment at several levels of all facilities. The model can also be further developed by choosing vehicles for each customer.

show abstract

“…K. Gao et al proposed a novel freshness-aware age optimization solution to satisfy the real-time demands of users [17]. In order to deploy cost-effective transcoding operations by distributing the computation-intensive workload among cloud, edge, and crowd, X. Chen et al proposed a novel stochastic approach that jointly optimizes the usage of transmission resources and transcoding resources [18]. In [19], the authors proposed a TrPF framework to protect users' trajectory privacy by TTP.…”

Section: Trajectory Privacy Preservingmentioning

confidence: 99%

Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

et al. 2022

View full text Add to dashboard Cite

Location privacy-preserving methods for location-based services in mobile communication networks have received great attention. Traditional location privacy-preserving methods mostly focus on the researches of location data analysis in geographical space. However, there is a lack of studies on location privacy preservation by considering the personalized features of users. In this paper, we present a Knowledge-Driven Location Privacy Preserving (KD-LPP) scheme, in order to mine user preferences and provide customized location privacy protection for users. Firstly, the UBPG algorithm is proposed to mine the basic portrait. User familiarity and user curiosity are modelled to generate psychological portrait. Then, the location transfer matrix based on the user portrait is built to transfer the real location to an anonymous location. In order to achieve customized privacy protection, the amount of privacy is modelled to quantize the demand of privacy protection of target user. Finally, experimental evaluation on two real datasets illustrates that our KD-LPP scheme can not only protect user privacy, but also achieve better accuracy of privacy protection.

show abstract

Augmented Queue-Based Transmission and Transcoding Optimization for Livecast Services Based on Cloud-Edge-Crowd Integration

Cited by 19 publications

References 34 publications

Computing Offloading With Fairness Guarantee: A Deep Reinforcement Learning Method

Computing Offloading With Fairness Guarantee: A Deep Reinforcement Learning Method

Optimizing the Multi-Level Location-Assignment Problem in Queue Networks Using a Multi-Objective Optimization Approach

Knowledge-Driven Location Privacy Preserving Scheme for Location-Based Social Networks

Contact Info

Product

Resources

About