Journal Article10.1109/TCC.2015.2511727
Energy-Efficient Task Execution for Application as a General Topology in Mobile Cloud Computing
Weiwen Zhang,Yonggang Wen +1 more
102
TL;DR: It is shown by simulation that the collaborative task execution is more energy-efficient than local execution and remote execution and Lagrange Relaxation based Aggregated Cost (LARAC) algorithm.
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Abstract: Mobile cloud computing has been proposed as an effective solution to augment the capabilities of resource-poor mobile devices. In this paper, we investigate energy-efficient collaborative task execution to reduce the energy consumption on mobile devices. We model a mobile application as a general topology, consisting of a set of fine-grained tasks. Each task within the application can be either executed on the mobile device or on the cloud. We aim to find out the execution decision for each task to minimize the energy consumption on the mobile device while meeting a delay deadline. We formulate the collaborative task execution as a delay-constrained workflow scheduling problem. We leverage the partial critical path analysis for the workflow scheduling; for each path, we schedule the tasks using two algorithms based on different cases. For the special case without execution restriction, we adopt one-climb policy to obtain the solution. For the general case where there are some tasks that must be executed either on the mobile device or on the cloud, we adopt Lagrange Relaxation based Aggregated Cost (LARAC) algorithm to obtain the solution. We show by simulation that the collaborative task execution is more energy-efficient than local execution and remote execution.
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Citations
Optimal Task Offloading and Resource Allocation in Mobile-Edge Computing With Inter-User Task Dependency
TL;DR: This paper considers a two-user MEC network, where each WD has a sequence of tasks to execute and proves that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimalOffloading decisions.
276
Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things
TL;DR: A distributed deep learning-driven task offloading (DDTO) algorithm is proposed to generate near-optimal offloading decisions over the MDs, edge cloud server, and central cloud server and achieves high performance and greatly reduces the computational complexity when compared with other offloading schemes that neglect the collaboration of heterogeneous clouds.
258
DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing
TL;DR: A Deep Meta Reinforcement Learning-based offloading (DMRO) algorithm, which combines multiple parallel DNNs with Q-learning to make fine-grained offloading decisions, is proposed, which achieves obvious improvement over the Deep Q-Learning algorithm and has strong portability in making real-time offload decisions even in time-varying IoT environments.
Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach
TL;DR: A deep reinforcement learning (DRL) framework based on the actor-critic learning structure is proposed, which achieves up to 99.1% of the optimal performance while significantly reducing the computational complexity compared to the existing optimization methods.
201
An Efficient Application Partitioning Algorithm in Mobile Environments
TL;DR: Simulation results show that the MCOP algorithm provides a stable method with low time complexity which significantly reduces execution time and energy consumption by optimally distributing tasks between mobile devices and servers, besides it adapts well to mobile environmental changes.
130
References
The Case for VM-Based Cloudlets in Mobile Computing
TL;DR: The results from a proof-of-concept prototype suggest that VM technology can indeed help meet the need for rapid customization of infrastructure for diverse applications, and this article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them.
MAUI: making smartphones last longer with code offload
Eduardo Cuervo,Aruna Balasubramanian,Dae-Ki Cho,Alec Wolman,Stefan Saroiu,Ranveer Chandra,Paramvir Bahl +6 more
- 15 Jun 2010
TL;DR: MAUI supports fine-grained code offload to maximize energy savings with minimal burden on the programmer, and decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains.
A survey of mobile cloud computing: architecture, applications, and approaches
TL;DR: A survey of MCC is given, which helps general readers have an overview of the MCC including the definition, architecture, and applications and the issues, existing solutions, and approaches are presented.
2.6K
CloneCloud: elastic execution between mobile device and cloud
Byung-Gon Chun,Sunghwan Ihm,Petros Maniatis,Mayur Naik,Ashwin Patti +4 more
- 10 Apr 2011
TL;DR: The design and implementation of CloneCloud is presented, a system that automatically transforms mobile applications to benefit from the cloud that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud.
Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?
Kumar Karthik,Yung-Hsiang Lu +1 more
TL;DR: The cloud heralds a new era of computing where application services are provided through the Internet, but is it the ultimate solution for extending such systems' battery lifetimes?
1.7K