Edge computing is an emerging paradigm for the increasing computing and networking demands from end devices to smart things. Edge computing allows the computation to be offloaded from the cloud data centers to the net...
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Edge computing is an emerging paradigm for the increasing computing and networking demands from end devices to smart things. Edge computing allows the computation to be offloaded from the cloud data centers to the network edge and edge nodes for lower latency, security and privacy preservation. Although energy efficiency in cloud data centers has been broadly investigated, energy efficiency in edge computing is largely left uninvestigated due to the complicated interactions between edge devices, edge servers, and cloud data centers. In order to achieve energy efficiency in edge computing, a systematic review on energy efficiency of edge devices, edge servers, and cloud data centers is required. In this paper, we survey the state-of-the-art research work on energy-aware edge computing, and identify related research challenges and directions, including architecture, operating system, middleware, applications services, and computation offloading.
Data processing on convolutional neural networks (CNNs) places a heavy burden on energy-constrained mobile platforms. This article optimizes energy on a mobile client by partitioning CNN computations between in situ p...
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Data processing on convolutional neural networks (CNNs) places a heavy burden on energy-constrained mobile platforms. This article optimizes energy on a mobile client by partitioning CNN computations between in situ processing on the client and offloaded computations in the cloud. A new analytical CNN energy model is formulated, capturing all major components of the in situ computation, for ASIC-based deep learning accelerators. The model is benchmarked against measured silicon data. The analytical framework is used to determine the optimal energy partition point between the client and the cloud at runtime. On standard CNN topologies, partitioned computation is demonstrated to provide significant energy savings on the client over a fully cloud-based computation or fully in situ computation. For example, at 80 Mbps effective bit rate and 0.78 W transmission power, the optimal partition for AlexNet [SqueezeNet] saves up to 52.4% [73.4%] energy over a fully cloud-based computation and 27.3% [28.8%] energy over a fully in situ computation.
Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge *** this paper,we consider a serial task model and desig...
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Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge *** this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual *** we construct the system utility measuring QoS based on application latency and user devices’energy *** also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O *** results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.
High-Performance Fortran (HPF) was envisioned as a vehicle for modernizing legacy Fortran codes to achieve scalable parallel performance. To a large extent, today's commercially available HPF compilers have failed...
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High-Performance Fortran (HPF) was envisioned as a vehicle for modernizing legacy Fortran codes to achieve scalable parallel performance. To a large extent, today's commercially available HPF compilers have failed to deliver scalable parallel performance for a broad spectrum of applications because of insufficiently powerful compiler analysis and optimization. Substantial restructuring and hand-optimization can be required to achieve acceptable performance with an HPF port of an existing Fortran application, even for regular data-parallel applications. A key goal of the Rice dHPF compiler project has been to develop optimization techniques that enable a wide range of existing scientific applications to be ported easily to efficient HPF with minimal restructuring. This paper describes the challenges to effective parallelization presented by complex (but regular) data-parallel applications, and then describes how the novel analysis and optimization technologies in the dHPF compiler address these challenges effectively, without major rewriting of the applications. We illustrate the techniques by describing their use for parallelizing the NAS SP and BT benchmarks. The dHPF compiler generates multipartitioned parallelizations of these codes that are approaching the scalability and efficiency of sophisticated hand-coded parallelizations. Copyright (C) 2002 John Wiley Sons, Ltd.
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