Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption

By Ruilong Deng, Rongxing Lu, Chengzhe Lai, Tom H. Luan, Hao Liang. IEEE Internet of Things Journal, 2016

758 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/6fbbb3b44ffde1a65371eceefbeb40194e3afc91

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In Computer Networks and Communications

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Citations in the corpus

Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, background): “[103] presented a framework for workload allocation in the cloud and Fog environment to examine power consumption-delay trade-off issues.”
Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges (2020, methodology): “The task scheduling schemes that balance latency and energy consumption include HyFog [68], OWAS [69], DOTS [70], DEBTS [71], etc.”
Multiobjective Optimization for Computation Offloading in Fog Computing (2018, background): “In [18], the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated.”
Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay (2018, background): “[8] proposed an optimal workload allocation scheme in edge-cloud computing with respect to trade-off between delay The majority of IT”
On Reducing IoT Service Delay via Fog Offloading (2018, background): “Another effort is the work in [11] that addresses power-delay tradeoff in cloud-fog computing by”
Incentive Mechanism Design for Computation Offloading in Heterogeneous Fog Computing: A Contract-Based Approach (2018, background): “[7] studied the interplay and cooperation between the fog and cloud, and investigated the tradeoff between power consumption and transmission delay in the fog-cloud computing system.”
Resource Management Approaches in Fog Computing: a Comprehensive Review (2019, methodology): “[64] have introduced an approximate solution in form of three subproblems including the energy consumption-delay trade-off into fog computing, the energy consumption-delay trade-off into cloud computing, and the minimization of communication delay for dispatch for workload allocation problem in the ”
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing (2018, background): “The studied problem was cast as a network-wide power minimization subject to an average delay requirement [16].”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “4 points out that the main attributes of the Fog paradigm are the following ones [1], [6], [37], [38]:”
Fog Computing: A Taxonomy, Survey and Future Directions (2016, background): “The energy consumption of all devices in home-based Fog computing environment [34] and the energy-latency tradeoff in different phases of Fog-Cloud interaction [41] have been highlightedwidely in theseworks.”
A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing (2017, background): “The concept of edge computing [14]–[16] is to perform data processing at the edge end, near the source of data.”
Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “A tradeoff between power consumption and transmission delay is investigated in [130].”
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey (2018, background): “focused on investigating power consumption and network delay tradeoff in cloud-fog services by developing a computation-based framework and workload scheduling [124].”
An Overview of Service Placement Problem in Fog and Edge Computing (2020, background): “Mono-objective [10, 12, 14, 16, 17, 19, 20, 43, 44] • Latency Low latency for delay-sensitive applications.”