A Dynamic Offloading Algorithm for Mobile Computing

By Donghong Huang, Ping Wang, Dusist Niyato. IEEE Transactions on Wireless Communications, 2012

500 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Information Systems

View Paper: https://www.semanticscholar.org/paper/b01672c370a5b8c6264cc38e1ec0efed55bd3ebe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Khaled Ben Letaief
2. Kaibin Huang
3. Jun Zhang
4. Changsheng You
5. Jie Xu
6. Yuyi Mao
7. Shuguang Cui
8. Feng Wang

In Electrical and Electronic Engineering

1. Khaled Ben Letaief
2. Jun Zhang
3. Changsheng You
4. Kaibin Huang
5. Yuyi Mao
6. Jie Xu
7. Shuguang Cui
8. Feng Wang

In Information Systems

1. Liang Wang
2. Shuguang Cui
3. Min Sheng
4. Khaled Ben Letaief
5. Yonggang Wen
6. Haiyun Luo
7. Gesualdo Scutari
8. F. Yu

Citations in the corpus

• Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, methodology): “By utilizing the computation, cache and energy resources of edge devices, computation offloading can reduce the computation latency, conserve battery resources, and even enhance security for the computation intensive IoT applications [4], [5].”
• Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (2015, methodology): “A stochastic control algorithm was proposed in [10] for adapting the offloaded components of an application to a time-varying wireless channel.”
• Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks (2018, background): “Various works have studied different facets of this problem, considering e.g. stochastic task arrivals [7], [8], energy efficiency [9], [10], [11], collaboration [12], [13], [14] etc.”
• Online job dispatching and scheduling in edge-clouds (2017, background): “With the development of cloud computing, more and more mobile applications can leverage the rich computing resources in cloud data centers [1]–[4].”
• Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, background): “In order to minimize the long-term average energy consumption, a stochastic control algorithm was proposed in [22], which determines the offloaded software components in an application.”
• A Survey on Mobile Edge Computing: The Communication Perspective (2017, methodology): “In [101], in order to minimize the mobile-energy consumption while keeping the proportion of executions violating the deadline requirement below a threshold, a dynamic offloading algorithm was proposed to determine the offloaded software components from an application running at a mobile user based ”
• Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, methodology): “Index Terms—Energy-optimal execution, mobile application, cloud computing, lagrangian multiplier method, stochastic wireless channel.”
• Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing (2016, background): “Compared to our work, however, they did not consider the task precedence and resource scheduling in computation offloading policy.”
• Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling (2017, background): “Earlier works either did not take into account this aspect as they only considered a fixed CPU frequency at the MD [10], [16], [17], or assumed a simplified task model [18]–[20] with either no or infinite granularity in offloadable data.”
• Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing (2017, background): “Like the studies in [13], [14] and [27], the time and energy consumption of computation outcome transmission from the MEC server to UE n is neglected in this work, due to the fact that the size of computation outcome data in general is much smaller than that of the computation input data including t”
• Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing (2014, methodology): “A dynamic formulation of computation offloading was proposed in [15].”
• Joint offloading and computing optimization in wireless powered mobile-edge computing systems (2017, methodology): “[26] and [27] investigated the dynamic offloading for MEC systems based on the techniques of Markov decision process and Lyapunov optimization, respectively.”
• Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling (2016, methodology): “A dynamic offloading algorithm based on Lyapunov optimization is proposed in [14] to achieve energy saving.”