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Machine learning (ML) models are increasingly trained with distributed workers possessing heterogeneous resources. In such scenarios, model training efficiency may be negatively affected by \emph{stragglers}---workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy, called \emph{semi-dynamic load balancing}, to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at \emph{iteration boundaries}, being non-intrusive to intra-iteration execution. Based on it we further develop LB-BSP, an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities---by right-sizing the sample batches at the synchronization barriers. We have designed distinct load tuning algorithms for ML in CPU clusters, in GPU clusters as well as in federated learning setups, based on their respective characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to $54\%$ .

Online cloud services are increasingly deployed as long-running applications (LRAs) in containers. Placing LRA containers is known to be difficult as they often have sophisticated resource interferences and I/O dependencies. Existing schedulers rely on operators to manually express the container scheduling requirements as placement constraints and strive to satisfy as many constraints as possible. Such schedulers, however, fall short in performance as placement constraints only provide qualitative scheduling guidelines and minimizing constraint violations does not necessarily result in the optimal performance.In this work, we present Metis, a general-purpose scheduler that learns to optimally place LRA containers using deep reinforcement learning (RL) techniques. This eliminates the complex manual specification of placement constraints and offers, for the first time, concrete quantitative scheduling criteria. As directly training an RL agent does not scale, we develop a novel hierarchical learning technique that decomposes a complex container placement problem into a hierarchy of subproblems with significantly reduced state and action space. We show that many subproblems have similar structures and can hence be solved by training a unified RL agent offline. Large-scale EC2 deployment shows that compared with the traditional constraint-based schedulers, Metis improves the throughput by up to 61%, optimizes various performance metrics, and easily scales to a large cluster where 3K containers run on over 700 machines.

Machine learning (ML) models are increasingly trained in clusters with non-dedicated workers possessing heterogeneous resources. In such scenarios, model training efficiency can be negatively affected by stragglers -- workers that run much slower than others. Efficient model training requires eliminating such stragglers, yet for modern ML workloads, existing load balancing strategies are inefficient and even infeasible. In this paper, we propose a novel strategy called semi-dynamic load balancing to eliminate stragglers of distributed ML workloads. The key insight is that ML workers shall be load-balanced at iteration boundaries, being non-intrusive to intra-iteration execution. We develop LB-BSP based on such an insight, which is an integrated worker coordination mechanism that adapts workers' load to their instantaneous processing capabilities by right-sizing the sample batches at the synchronization barriers. We have custom-designed the batch sizing algorithm respectively for CPU and GPU clusters based on their own characteristics. LB-BSP has been implemented as a Python module for ML frameworks like TensorFlow and PyTorch. Our EC2 deployment confirms that LB-BSP is practical, effective and light-weight, and is able to accelerating distributed training by up to $54\%$.