Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones

By Yonggang Wen, Weiwen Zhang, Haiyun Luo. 2012 Proceedings IEEE INFOCOM, 2012

376 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/abc4f486e1c76c3d1680751ea4af0dc1056b31c3

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. Xu Chen
2. F. Yu
3. Chen-meng Wang
4. Chengchao Liang
5. Lun Tang
6. Qianbin Chen
7. Liekang Zeng
8. Sergio Barbarossa

In Electrical and Electronic Engineering

1. Xu Chen
2. Liekang Zeng
3. Zhi Zhou
4. Yonggang Wen
5. Haiyun Luo
6. Song Chong
7. F. Yu
8. Stefania Sardellitti

In Information Systems

1. Xu Chen
2. F. Yu
3. Lun Tang
4. Chengchao Liang
5. Chen-meng Wang
6. Qianbin Chen
7. Stefania Sardellitti
8. Sergio Barbarossa

Citations in the corpus

• CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020, positive): “As demonstrated in previous empirical studies [26], [28], [29], for many data processing tasks as exemplified by data encoding and decoding, the required computing cycles are proportional to their input data sizes.”
• Cloud robotics: architecture, challenges and applications (2012, neutral): “IV.”
• Mobile Cloud Computing: A Survey, State of Art and Future Directions (2013, background): “More recently, [19] determines when to upload the mobile application to the cloud to save energy on mobile device while meeting the application time constraint.”
• Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing (2015, methodology): “in [15] presented an efficient offloading policy by jointly configuring the clock frequency in the mobile device and scheduling the data transmission to minimize the energy consumption.”
• DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (2015, methodology): “A recent work [13] suggested an optimal mobile cloud computing algorithm of which main objective is to minimize smartphone energy consumption with given delay constraints.”
• Deep reinforcement learning based computation offloading and resource allocation for MEC (2018, methodology): “We set zn = 10−27 ( f l n )2 according to the practical measurement in [13].”
• Decentralized Computation Offloading Game for Mobile Cloud Computing (2014, background): “Most previous work has investigated the efficient computation offloading mechanism design from the perspective of a single mobile device user [6], [7], [8], [9], [10], [11], [12], [13], [14], [15].”
• Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks (2014, background): “Offloading typically requires code partitioning [7], [23], [24], aimed to decide which parts of the code should run locally and which parts should be offloaded, depending on contextual parameters, such as computational intensity of each module, size of the program state to be exchanged to transfer…”
• Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, result): “Compared to our previous work [10], this paper has several contributions.”
• Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing (2017, background): “According to the realistic measurements in [25], we set vn = 10−11(F (l) n )2.”