@InProceedings{8081436,
  author = {S. Sthapit and J. R. Hopgood and J. Thompson},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Distributed computational load balancing for real-time applications},
  year = {2017},
  pages = {1385-1189},
  abstract = {Mobile Cloud Computing or Fog computing refer to offloading computationally intensive algorithms from a mobile device to a cloud or a intermediate cloud in order to save resources (time and energy) in the mobile device. In this paper, we look at alternative solution when the cloud or fog is not available. We modelled sensors using network of queues and use linear programming to make scheduling decisions. We then propose novel algorithms which can improve efficiency of the overall system. Results show significant performance improvement at the cost of using some extra energy. Particularly, when incoming job rate is higher, we found our Proactive Centralised gives the best compromise between performance and energy whereas Reactive Distributed is more effective when job rate is lower.},
  keywords = {cloud computing;mobile computing;resource allocation;scheduling;real-time applications;mobile device;intermediate cloud;fog computing;distributed computational load;computationally intensive algorithms;mobile cloud computing;proactive centralised;reactive distributed;Cloud computing;Wireless fidelity;Signal processing algorithms;Cameras;Central Processing Unit;Sensors;Drones;Offloading;Mobile Cloud Computing;Energy;IOT;Fog Computing;Edge Computing},
  doi = {10.23919/EUSIPCO.2017.8081436},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346774.pdf},
}

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In this paper, we look at alternative solution when the cloud or fog is not available. We modelled sensors using network of queues and use linear programming to make scheduling decisions. We then propose novel algorithms which can improve efficiency of the overall system. Results show significant performance improvement at the cost of using some extra energy. 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