Recently, intelligent scheduling approaches using surrogate models have been proposed to efficiently allocate volatile tasks in heterogeneous fog environments. Advances like deterministic surrogate models, deep neural networks (DNN) and gradient-based optimization allow low energy consumption and response times to be reached. However, deterministic surrogate models, which estimate objective values for optimization, do not consider the uncertainties in the distribution of the Quality of Service (QoS) objective function that can lead to high Service Level Agreement (SLA) violation rates. Moreover, the brittle nature of DNN training and prevent such models from reaching minimal energy or response times. To overcome these difficulties, we present a novel scheduler: GOSH i.e. Gradient Based Optimization using Second Order derivatives and Heteroscedastic Deep Surrogate Models. GOSH uses a second-order gradient based optimization approach to obtain better QoS and reduce the number of iterations to converge to a scheduling decision, subsequently lowering the scheduling time. Instead of a vanilla DNN, GOSH uses a Natural Parameter Network to approximate objective scores. Further, a Lower Confidence Bound optimization approach allows GOSH to find an optimal trade-off between greedy minimization of the mean latency and uncertainty reduction by employing error-based exploration. Thus, GOSH and its co-simulation based extension GOSH*, can adapt quickly and reach better objective scores than baseline methods. We show that GOSH* reaches better objective scores than GOSH, but it is suitable only for high resource availability settings, whereas GOSH is apt for limited resource settings. Real system experiments for both GOSH and GOSH* show significant improvements against the state-of-the-art in terms of energy consumption, response time and SLA violations by up to 18, 27 and 82 percent, respectively.
翻译:最近,提出了使用替代模型的明智时间安排方法,以有效分配不同雾环境中的不稳定任务; 诸如确定性替代模型、深神经网络(DNN)和基于梯度的优化等进步,使得能达到低能源消耗和响应时间。然而,估计优化目标值的确定性替代模型,没有考虑到服务质量(QOS)目标功能分布上的不确定性,从而导致高服务级协议违约率。此外,DNN培训的易碎性质,防止这类模型达到最低能源或响应时间。为了克服这些困难,我们提出了一个新的时间表:GOSH i.e. 使用第二顺序衍生物和高渗透性深表面表面模型的渐进优化基础优化模型;GOS 使用基于二级梯度的加速度优化优化方法,以获得更好的QOS 优化效果,减少服务级协议违反率的频率,通过时间来适应时间安排决定,随后降低时间。 与Vanilla DNNE相比,GOH 使用一个自然定位时间网络,以接近目标分级的快速计算。