We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating node) or be atomically executed at the designate server. IoT nodes and servers harvest ambient energy and jobs have a completion deadline . For this setup, we are concerned with the temporal allocation of jobs that maximizes the minimum level among all energy buffers in the network while meeting all the deadlines, i.e., that makes the network as much as possible energy neutral . Jobs continuously and asynchronously arrive at the IoT nodes, and computing resources are allocated dynamically at runtime, automatically adapting the processing load across nodes and servers. To achieve this, we present a Model Predictive Control based algorithm, where the job scheduler solves a sequence of low complexity convex problems and exploits future job and energy arrival estimates. The proposed technique is numerically evaluated, showing excellent adaptation capabilities, and performance close to that of an offline optimal scheduler with perfect information of all processes.

中文翻译：

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能源收集物联网边缘网络中计算任务的弹性和预测分配

我们考虑一个分布式物联网边缘网络，其端节点生成的计算作业可以在本地处理，也可以全部或部分卸载到其他具有必要计算和能源资源的物联网节点和/或边缘服务器。也就是说，作业既可以在多个节点（包括起始节点）上进行分区和执行，也可以在指定服务器上原子地执行。物联网节点和服务器收集环境能量，工作完成最后期限 . 对于这种设置，我们关注的是在满足所有截止日期的同时使网络中所有能量缓冲区中的最低水平最大化的作业的时间分配，即，使网络尽可能多能量中性 . 作业持续异步地到达物联网节点，计算资源在运行时动态分配，自动适应节点和服务器之间的处理负载。为了实现这一点，我们提出了一种基于模型预测控制的算法，其中作业调度程序解决了一系列低复杂度的凸问题，并利用未来的作业和能量到达估计。所提出的技术经过数值评估，显示出出色的适应能力，其性能接近具有所有进程完美信息的离线最优调度器的性能。