Subspace analysis (SA) is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal-processing tasks. However, traditional SA often requires...
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Subspace analysis (SA) is a widely used technique for coping with high-dimensional data and is becoming a fundamental step in the early treatment of many signal-processing tasks. However, traditional SA often requires a large amount of memory and computational resources, as it is equivalent to eigenspace determination. To address this issue, specialized streaming algorithms have been developed, allowing SA to be run on low-power devices, such as sensors or edge devices. Here, we present a classification and a comparison of these methods by providing a consistent description and highlighting their features and similarities. We also evaluate their performance in the task of subspace identification with a focus on computational complexity and memory footprint for different signal dimensions. Additionally, we test the implementation of these algorithms on common hardware platforms typically employed for sensors and edge devices.
Vehicular edgecomputing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC ...
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Vehicular edgecomputing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC scenario, the uneven geographical distribution of the vehicles naturally causes the load imbalance among the edge servers and leads to the overload and performance degradation problems of the edge servers in hot areas. To this end, in this paper, we propose a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further offloaded to the other low-load edge servers. Our objective is to minimize the total task processing delay of all the vehicles while guaranteeing the task processing delay tolerance and the holding time of each vehicle. An M/M/1 queue is used to model the task queuing and task computing processes on each RSU. An exact potential game is adopted to model the competition process for the task offloading among the RSUs. A two-stage iterative algorithm is designed to decompose the optimization problem into two stages and solve them iteratively. We analyze the computational complexity of the algorithm and conduct extensive simulations by varying different crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other reference schemes.
Live-streaming video requires a lot of CPU-intensive transcoding so that viewers can receive video at bitrates appropriate to their devices and network conditions, which is necessary for a good quality of experience (...
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Live-streaming video requires a lot of CPU-intensive transcoding so that viewers can receive video at bitrates appropriate to their devices and network conditions, which is necessary for a good quality of experience (QoE). We allocate transcoding tasks to edge-servers in a multiple-access edge-computing (MEC) architecture, taking into account server capacity, wireless network coverage, and the cost budget of broadcasters, as well as QoE. Our algorithm first chooses candidate transcoding tasks by giving higher priority to the tasks that make the most cost-effective contribution to popularity-weighted video quality (PWQ). It assigns these tasks to edge-servers in a greedy manner, taking network coverage and computational load into account. Subsequently, it meets a cost budget by reassigning some tasks and removing other assignments altogether, while trying to minimize the effect of these alterations on total PWQ. Simulation results show that our scheme achieves 0.06% to 94.62% (average 25.3%) more PWQ than alternative schemes under the same cost budget.
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