Energy Efficiency of Mobile Clients in Cloud Computing

By A. P. Miettinen, Jukka Kalevi Nurminen. 2010

855 citations recorded by the source corpus.

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Citations in the corpus

Federated Multi-Task Learning (2017, background): “Communication can also exacerbate poor performance, as it can be slower than computation by many orders of magnitude in typical cellular or wireless networks [52, 20, 48, 9, 38].”
Decentralized Computation Offloading Game for Mobile Cloud Computing (2014, background): “According to the realistic measurements in [6], [27], we can set n n ¼ 10 (cid”
A Distributed Truthful Auction Mechanism for Task Allocation in Mobile Cloud Computing (2018, background): “To alleviate resource scarcity of mobile devices, one effective way is to offload their complex or resourcedemanded tasks to remote cloud [5], [6], [7], [8] through pay as you go.”
A Survey of Green Mobile Networks: Opportunities and Challenges (2011, background): “As learned from a prototype of cloud-based mobile service, the Stratus [117], a bundle of energy-efficient techniques are strongly required but still in suspense [118, 119], such as, data aggregation to bunch up sporadic transmissions, asymmetric dictionary-based compression, and efficient algorithm”
CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020, methodology): “…as the computing intensity (in processing cycles per 1KB input) of the given DNN model, which is measured by application-driven ofﬂine proﬁling [26] in the setup phase. f is the device’s CPU frequency, reﬂecting its computing capability in a coarse granularity. m is the available maximum…”
Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges (2014, background): “• Long WAN Latency: Latency adversely impacts on the energy efficiency [48] and interactive response [49] of cloud-mobile applications by consuming excessive mobile resources and raising transmission delays.”
Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, methodology): “In order to process one bit of computation task input from the i th mobile device, Li CPU cycles will be needed, which depends on the types of applications and can be obtained by off-line measurements [30].”
Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing (2017, methodology): “Table I SIMULATION PARAMETERS [5], [7], [12], [13].”
Cloud-Vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture (2012, background): “Energy efﬁciency of smart mobile devices is studied in conjunction with cloud computing in [23].”
Edge Computing: Vision and Challenges (2016, unknown): “For the endpoint layer, ofﬂoad-ing workload to the edge can be treated as an energy free method [40], [41].”
Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing (2016, methodology): “It is clear that we can adjust the clock frequency of the CPU chip to achieve the optimum computation time and energy consumption on a mobile device by employing DVFS technique.”
Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices (2016, background): “Besides, X = 5900 cycles per byte, which corresponds to the workload of processing the English main page of Wikipedia [28].”
Performance and energy modeling for live migration of virtual machines (2011, background): “Mobile cloud computing [27] has emerged as a new paradigm in which mobile applications are executed in a cloud-based virtualized execution environment controlled by mobile applications and users, the computing resources such as processing, memory, and storage are not physically present on the user’s”
Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges (2013, background): “Latency adversely impacts on the energy efficiency [74] and interactive response [168] of CMA-based mobile applications due to excessive consumption of mobile resources and raising transmission delays.”
Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, methodology): “D. Kilper is with the Center for Integrated Access Networks, College of Optical Sciences, University of Arizona 1630 E. University Blvd. Tucson, AZ 85721 (e-mail: dkilper@optics.arizona.edu).”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, background): “Table I S IMULATION P ARAMETERS [7], [10], [17], [40], [41] Parameter After computing and ofﬂoading the tasks in time slot t , each UE updates its physical and virtual queue lengths in (3), (4), (22)–(25), (31), and (32) when the MEC servers update (7), (26), (27), and (33) for the next slot t + 1 .”
Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading (2014, methodology): “As in [3], [6], [7], [12], and [13], energy consumption and total execution time are the key performance indicators considered for the optimization.”
Federated Learning over Wireless Networks: Optimization Model Design and Analysis (2019, methodology): “We denote the number of CPU cycles for UE n to execute one sample of data by cn, which can be measured offline [11] and is known as a priori.”