QoE-aware Data Caching Optimization with Budget in Edge Computing

Liu, Ying; Han, Yuzheng; Zhang, Ao; Xia, Xiaoyu; Chen, Feifei; Zhang, Mingwei; He, Qiang

doi:10.1109/icws53863.2021.00050

Cited by 8 publications

(9 citation statements)

References 23 publications

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“…Although the study in this paper also considers the factor of users' QoE. First, our final goal is different from that in [10], the optimization objective in [10] is an evaluative indicator in our study but not an optimization objective. Secondly, during the transformation of QoS and QoE, both QoS and QoE in our study are continuous variables, while are discrete variables in [10].…”

Section: Related Workmentioning

confidence: 92%

“…The authors of [7], [22] and [23] study how to maximize the profit of the app vendor, while the author of [24] proposes a method to minimize the total cost, but their processing of the revenue are too single, simply thinking that each user's request causes the same revenue, which is obviously more diverse and complex in actual condition, and the work in [7] is not comprehensive enough in considering the components of profit. The ultimate goal of the study in [10] is to maximize the overall QoE values of all users. Its' author classifies the QoE required by users and the quality of data into several levels, each level corresponds to certain quantitative resources.…”

Section: Related Workmentioning

confidence: 99%

“…The researches in [8] and [9] aim to minimize overall users' latency and maximize caching revenue from app vendors' perspective, respectively, when considering edge servers' limited cache space. The author in [10] takes into account both the budget and servers' cache space constraints. High cache hit rate of the data means more data requests are satisfied by the data cached on edge servers, and more effective the data caching solution is.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cost-effective data caching optimization with distance-aware in edge computing

Wang

Han

et al. 2023

2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022)

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In recent years, edge data caching has been concerned by more and more research scholars. Caching data, especially popular data, on edge servers can significantly reduce users' latency, while also greatly reducing direct data transmission between users and remote clouds, and slowing down the congestion of the backbone network. Most of the existing research on edge data caching is mainly aim at minimizing users' latency, saving devices' energy, and improving the hit rate of data caching, etc. From the perspective of an app vendor, how to formulate an edge data caching solution that can bring him the most profit while complying with a given budget constraint and considering the experience requirements of different users is a very realistic and challenging problem. In this paper, we formulate the Profit-Maximizing Edge Data Caching(PMEDC) as a constrained optimization problem. For PMEDC in small scale scenarios, we propose an optimization model named PMEDC-IP that can derive the optimal caching strategy. For PMEDC in large scale scenarios, we propose an approximation algorithm named MPF(Maximum Profit First) that can find an approximate optimal caching scheme in a reasonable time. We conduct extensive experiments on a real-world dataset, and based on the final experimental results, PMEDC-IP and MPF proposed by us significantly outperform the other three representative methods.

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Section: Related Workmentioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cost-effective data caching optimization with distance-aware in edge computing

Wang

Han

et al. 2023

2022 2nd Conference on High Performance Computing and Communication Engineering (HPCCE 2022)

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“…For instance, QoE-aware caching policies were presented in [134], [55], [85], [133], [49], [87], [86] and [154]. According to [134], [55], [85], [155], the authors used common optimization techniques for solving caching problems. However, most recent works, e.g., [133], [49], [87], [86] and [154], attempted to deal with these problem by using emerging ML-based algorithms such as Deep Learning.…”

Section: Qos/qoe and Data Cachingmentioning

confidence: 99%

“…The proposed algorithm was used to maximize the weighted sum of this objective QoE in a joint caching placement, video quality decision and user association problem. A heuristic data caching method was proposed in [155] to maximize the overall QoE which was modeled from QoS metrics, data storage capacity and caching cost. The algorithm was designed from Genetic algorithm.…”

Section: Qos/qoe and Data Cachingmentioning

confidence: 99%

QoE-Driven IoT Architecture: A Comprehensive Review on System and Resource Management

et al. 2022

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Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: (1) QoEdriven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies, (2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture, and (3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed. INDEX TERMSInternet of Things, Quality of Service, Quality of Experience, IoT services, IoT applications, QoS for IoT services, QoS metrics, QoE metrics, IoT architecture Recently, emerging IoT architectures with multi-layers, e.g., Mobile Edge Computing (MEC), Fog Computing and Cloud Computing, have been proposed to improve user experiences.

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A Resource-Constrained Multi-level SLA Customization Approach Based on QoE Analysis of Large-Scale Customers

Wang

2023

Lecture Notes in Computer Science

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QoE-aware Data Caching Optimization with Budget in Edge Computing

Cited by 8 publications

References 23 publications

Cost-effective data caching optimization with distance-aware in edge computing

Cost-effective data caching optimization with distance-aware in edge computing

QoE-Driven IoT Architecture: A Comprehensive Review on System and Resource Management

A Resource-Constrained Multi-level SLA Customization Approach Based on QoE Analysis of Large-Scale Customers

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