Instance-Based Learning Algorithms
Machine Learning
Adaptive Offloading Inference for Delivering Applications in Pervasive Computing Environments
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Energy-Aware Task Scheduling: Towards Enabling Mobile Computing over MANETs
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 12 - Volume 13
The TCP/IP Guide: A Comprehensive, Illustrated Internet Protocols Reference
The TCP/IP Guide: A Comprehensive, Illustrated Internet Protocols Reference
Finding Prototypes For Nearest Neighbor Classifiers
IEEE Transactions on Computers
Mapping parallelism to multi-cores: a machine learning based approach
Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Towards energy-aware scheduling in data centers using machine learning
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
MAUI: making smartphones last longer with code offload
Proceedings of the 8th international conference on Mobile systems, applications, and services
CODES/ISSS '10 Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
Light-weight adaptive task offloading from smartphones to nearby computational resources
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
SNARF: a social networking-inspired accelerator remoting framework
Proceedings of the first edition of the MCC workshop on Mobile cloud computing
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Remote offloading techniques have been proposed to overcome the limited resources of mobile platforms by leveraging external powerful resources such as personal work-stations or cloud servers. Prior studies have primarily focused on core mechanisms for offloading. Yet, adaptive scheduling in such systems is important because offloading effectiveness can be influenced by varying network conditions, workload requirements, and load at the target device. In this paper, we present a study on the feasibility of applying machine learning techniques to address the adaptive scheduling problem in mobile offloading framework. The study considers 19 different machine learning algorithms and four workloads, with a dataset obtained through the deployment of an Android-based remote offloading framework prototype on actual mobile and cloud resources. From this set, a subset of machine learning algorithms, which have relatively high scheduling accuracy, is selected to implement an offline offloading scheduler. Finally, by taking computational cost and the scheduling performance into account, we use Instance-Based Learning to evaluate an online adaptive scheduler for mobile offloading. In our evaluation, we observe that an Instance Learning-based online offloading scheduler selects the best scheduling decision in 87.5% instances, in an experiment setup in which an image processing workload is offloaded while subject to varying network bandwidth conditions and the amount of data transfer.