Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.
翻译:移动边缘计算有助于用户卸载计算任务到边缘服务器,以满足严格的延迟要求。 先前的工作主要是在提供系统信息(例如服务器处理速度、蜂窝数据率)或系统不确定性下集中卸载时,探索任务卸载任务(例如,服务器处理速度、蜂窝数据率),或者在系统不确定性下集中卸载。 但两者通常都无法处理涉及许多在动态和不确定环境中同时共存的用户的任务安排。 在本文件中,我们开发了一个多用户卸载框架,考虑到未知的、但随机的系统边端信息,以便能够进行分散的用户启动的服务安排。 具体地说,我们把动态任务安排设计成一个多用户多武装在线的多武装盗匪过程,并提出基于分散式卸载(DEBO)的卸载(DEBO),以优化用户在网络延迟情况下获得的回报。 我们显示,DEBO可以推算出最佳的用户- 用户- 服务器任务安排, 从而实现接近最佳的服务性业绩和紧紧的 O(log T) 背负遗憾。 此外,我们把DEBO 推广到各种共同的情景, 如未知的奖励差距、 客户动态进入或离开客户的多功能分配, 同时进一步探索用户卸载时, 需要基于真实的优化的优化的优化的计算。