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In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge...
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... are in total 40 video files with 5 bitrate levels, which are 360p, 480p, 720p, 1080p and 1440p. The required bandwidth and transcoding delay for a video file with different bitrate levels are shown in Table 3 according to [6] and [45]. The video access pattern is assumed to follow the Zipf distribution according to [5], and each video file f i (1 ≤ i ≤ 40) with bitrate level j (1 ≤ j ≤ 5) has an accessing probability: ...Citations
... • Tasks: a unit work in an application in which there are different types of tasks in edge computing such as LiDAR [37] or camera [38] of autonomous vehicles, wireless body area networks (WBAN) of healthcare IoT applications [17]. • Methodology: different methods can be utilized to schedule tasks, including centralized and distributed [39]. ...
The edge computing paradigm enables mobile devices with limited memory and processing power to execute delay-sensitive, compute-intensive, and bandwidth-intensive applications on the network by bringing the computational power and storage capacity closer to end users. Edge computing comprises heterogeneous computing platforms with resource constraints that are geographically distributed all over the network. As users are mobile and applications change over time, identifying an optimal task scheduling method is a complex multi-objective optimization problem that is NP-hard, meaning the exhaustive search with a time complexity that grows exponentially can solve the problem. Therefore, various approaches are utilized to discover a good solution for scheduling the tasks within a reasonable time complexity, while achieving the most optimal solution takes exponential time. This study reviews task scheduling algorithms based on centralized and distributed methods in a three-layer computing architecture to identify their strengths and limitations in scheduling tasks to edge service nodes.
... where f mix denotes the Mixing network [48], [49], which is a non-negative non-linear monotonic mapping between r t 1 and r t d−s , s ∈ {1, 2, 3}. The convergence of this centralized and distributed algorithm to a locally optimal policy has been substantiated in [49], [50]. ...
As an attractive enabling technology for next-generation wireless communications, network slicing supports diverse customized services in the global space-air-ground integrated network (SAGIN) with diverse resource constraints. In this paper, we dynamically consider three typical classes of radio access network (RAN) slices, namely high-throughput slices, low-delay slices and wide-coverage slices, under the same underlying physical SAGIN. The throughput, the service delay and the coverage area of these three classes of RAN slices are jointly optimized in a non-scalar form by considering the distinct channel features and service advantages of the terrestrial, aerial and satellite components of SAGINs. A joint central and distributed multi-agent deep deterministic policy gradient (CDMADDPG) algorithm is proposed for solving the above problem to obtain the Pareto optimal solutions. The algorithm first determines the optimal virtual unmanned aerial vehicle (vUAV) positions and the inter-slice sub-channel and power sharing by relying on a centralized unit. Then it optimizes the intra-slice sub-channel and power allocation, and the virtual base station (vBS)/vUAV/virtual low earth orbit (vLEO) satellite deployment in support of three classes of slices by three separate distributed units. Simulation results verify that the proposed method approaches the Pareto-optimal exploitation of multiple RAN slices, and outperforms the benchmarkers.
