Toward an Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks
Toward an Optimal Latency-Energy Dynamic Offloading Scheme for Collaborative Cloud Networks
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DOI：
10.1109/access.2023.3280415
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Jui Mhatre;Ahyoung Lee;Tu N. Nguyen
中科院分区：
文献类型：
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作者：
Jui Mhatre;Ahyoung Lee;Tu N. Nguyen
如今，虚拟化和人工智能等不断发展的技术变得越来越流行，因为它们在移动设备上更加方便，可以访问。但是，缺乏在用户端处理这些应用程序的资源，而移动设备的能量有限仍然是重大障碍。协作边缘和云计算是解决此问题的解决方案之一。需要一种最佳的卸载策略来平衡云的传输延迟和边缘服务器的有限资源。我们已经提出了一个多周期的深层确定性策略梯度（MP-DDPG）算法，以找到对协作云网络的最佳卸载策略，包括中央云服务器，Edge Cloud Server和Mobile Devices，以及受到最小化计算，变速箱延迟，传输延迟，限制的移动设备和能耗。该算法的新颖性在于将任务划分在多个时间段中的卸载，并在每个插槽中重复使用云和边缘资源，而不是通过单个大型任务来卸载单个卸载决策并用尽远程资源。我们的结果表明，MP-DDPG达到了协作云网络中的最小延迟和能耗。
Growing technologies like virtualization and artificial intelligence have become more popular nowadays because they are more handy and accessible on mobile devices. But lack of resources for processing these applications at the user end and the limited energy of mobile devices are still significant hurdles. Collaborative edge and cloud computing are one of the solutions to this problem. An optimal offloading strategy is required to balance transmission latency for the cloud and limited resources at edge servers. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy to the collaborative cloud network including the central cloud server, edge cloud servers, and mobile devices constrained by minimization of computation, transmission delay, and energy consumption. The novelty of this algorithm lies in partitioning the task to offload in multiple time slots and reusing cloud and edge resources in every slot, rather than taking a single offloading decision and running out of remote resources by offloading a single large task. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network.