Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource deman...
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Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.
Despite the remarkable coding gains exhibited by the recently released new-generation video coding standards, their serious computational complexity will pose a significant challenge in coding latency to practical app...
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ISBN:
(纸本)9798400701085
Despite the remarkable coding gains exhibited by the recently released new-generation video coding standards, their serious computational complexity will pose a significant challenge in coding latency to practical applications. Therefore, the corresponding low-complexity optimizations assume paramount importance. To facilitate the research in this field, the first open source software library for video coding fast algorithm implementation, namely OpenFastVC, is proposed in this paper. Specifically, OpenFastVC offers the outputting and processing of the intermediate coding information, e.g., the CU partitioning results, which is indispensable to fast algorithm design. To facilitate the integration of the designed algorithms, OpenFastVC also provides application programming interfaces (APIs) for direct control over the encoding process. Moreover, the existing typical fast algorithms are further implemented in OpenFastVC, enabling researchers to evaluate the performance of their algorithm effortlessly. The release of this library is highly favorable for the design, implementation, and evaluation of video coding fast algorithms, thereby fostering the widespread adoption of the new coding standards. The open source library for OpenFastVC is available at https://***/OpenCompression/OpenFastV C.
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