A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing
By Lei Yang, Jiannong Cao, Shaojie Tang, Tao Li, Alvin T. S. Chan. 2012 IEEE Fifth International Conference on Cloud Computing, 2012
455 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/6cd9594d365c3914b7d0cda37b2b90088eb38ce9
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In Computer Networks and Communications
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Citations in the corpus
- Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks (2019, methodology): “Di and Fi can be obtained by using the approaches provided in [43].”
- Decentralized Computation Offloading Game for Mobile Cloud Computing (2014, methodology): “Similar to many previous studies in mobile cloud computing [6], [17], [19] and mobile networking [20], [21], [22], to enable tractable analysis and get useful insights, we consider a quasi-static scenario where the set of mobile device users N remains unchanged during a computation offloading period…”
- Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling (2016, methodology): “Therefore, many works are devoted to partial offloading [13]–[17].”
- EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications (2019, background): “The focus of [26] is on the design of a system architecture (i.”
- Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee (2018, methodology): “Similar to the existing works [6], [9], [10], [13], [20], [22], [23], to enable tractable analysis, we consider a quasistatic scenario where all UEs and the wireless network remain stationary during an offloading period (usually within several seconds [10]).”
- Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing (2016, unknown): “Compared to our work, however, they did not consider the task precedence and resource scheduling in computation offloading policy.”
- A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “Mobile Big Data Analytics Big data processing is a hot topic for big data architecture in the cloud and mobile cloud [55, 38].”
- Privacy Protection for Preventing Data Over-Collection in Smart City (2016, background): “This framework not only allows single user partitioning but also supports sharing computation instances among multiple users [22].”
- Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing (2020, background): “Both Dn,t and Fn,t can be characterized as in [36].”
- Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds (2017, background): “Other works focus on optimizing only computation resources, but may not take into account the effect of communication resources on offloading [13], [14].”
- Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks (2017, background): “Previously, this problem has been partially addressed by optimizing either the offloading decision [7], [8], communication resources [9], [10], or computing resources [11], [12].”
- Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing (2017, methodology): “Similar to many previous works in mobile cloud computing [29] and mobile networking [30]–[34], to enable tractable analysis and get useful insights, we employ a quasistatic scenario where the set of mobile device users Kn,∀n remains unchanged during a computation offloading period (e.”