Hit Ratio Driven Mobile Edge Caching Scheme for Video on Demand Services

Chen, Xing; He, Lijun; Xu, Shoufang; Hu, Shibo; Li, Qingzhou; Liu, Guizhong

doi:10.1109/icme.2019.00293

Cited by 19 publications

(14 citation statements)

References 11 publications

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“…Fundamentally, a higher hit ratio is essential to obtain a better system eiciency. Several studies to achieve a signiicantly increased hit ratio have recently been proposed from the LVS perspective [26,34,117,119,139,183,187]. In particular, the work [117] investigated the ield of view (FoV) aware algorithm for ABS-based edge caching served LVS services, where a common-FoV probabilistic model was analyzed based on the viewing histories of previous users to improve the caching hit ratio.…”

Section: Hit Ratiomentioning

confidence: 99%

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Dao

Tran

et al. 2022

ACM Comput. Surv.

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Live video streaming services have experienced significant growth since the emergence of social networking paradigms in recent years. In this scenario, adaptive bitrate streaming communications transmitted on web protocols provide a convenient and cost-efficient facility to serve various multimedia platforms over the Internet. In these communication models, video content is delivered optimally, possibly transcoded, edited automatically, and cached temporarily by network elements along the path. To this end, the computational capabilities of various network elements are considered as major resources to be optimized for service quality improvements. This paper provides a contemporary survey of cutting-edge live video streaming studies from a computation-driven perspective. First, an overview of the global standards, system architectures, and streaming protocols is presented. Next, hierarchical computation-driven models of live video streaming are anatomized, including cloud-, edge-, and peer-to-peer-based solutions. Cutting-edge studies are then reviewed to discover the advances they have made in improving system performance in multiple aspects. Finally, open challenges are presented to direct future research in this field.

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Section: Hit Ratiomentioning

confidence: 99%

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Dao

Tran

et al. 2022

ACM Comput. Surv.

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“…Reference [16] studied how to minimize the traffic between content delivery network (CDN) and network edge, and support transparently and dynamically switch between multiple CDNs to keep QoS rates. Reference [17] solved a joint optimization problem of caching and transcoding for video on demand (VoD) services in MEC systems. MEC is also applied to adaptive video streaming in [18], and authors explored how to jointly maximize quality of experience (QoE) and minimize backhaul traffic for device-to-device communication.…”

Section: Related Workmentioning

confidence: 99%

“…Attracted by the advantage of MEC, more and more researchers pay attention to MEC applications in recent years. MEC architecture is introduced and applied in various scenarios, such as edge caching [15]- [19], service deployment [20], [21], low latency data transmission [20]- [22], video on demand (VoD) [17], [23], high bitrate multimedia streaming [24], [25], security management [19], [26], and other multimedia services. In addition, to figure out complex issues in MEC systems, machine learning (ML) techniques are adopted [19], [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

et al. 2019

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By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario. INDEX TERMS Deep-Q-network, deep reinforcement learning, mobile edge computing, multimedia, quality of service.

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“…Compared with the edgecloud architecture, ESS overcomes the single-point failure problem and performance bottleneck problem encountered [6], [10]. New challenges raised by ESS are starting to attract researchers' attention in recent years, who attempt to achieve various optimization objectives by caching data replicas on edge servers, e.g., minimum data retrieval latency [11], maximum cache hit ratio [12], [13], maximum caching benefits [14], [15], maximum caching capacity [16], [17]. A common assumption made by these replica-based approaches is that storage resources on each edge server in an ESS can always be hired on-demand or reserved in advance for caching data replicas.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

Jin

Luo

et al. 2023

IEEE Trans. Serv. Comput.

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Edge computing, as a new computing paradigm, brings cloud computing's computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constitute edge storage systems (ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this paper, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novel Erasure Coding based Edge Data Placement (EC-EDP) problem as an integer linear programming problem and prove its N P-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

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Hit Ratio Driven Mobile Edge Caching Scheme for Video on Demand Services

Cited by 19 publications

References 11 publications

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

A Contemporary Survey on Live Video Streaming from a Computation-Driven Perspective

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

Cost-Effective Data Placement in Edge Storage Systems With Erasure Code

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