GB-PANDAS：相似性调度的吞吐量和大流量优化分析
GB-PANDAS: Throughput and heavy-traffic optimality analysis for affinity scheduling
摘要：
近三十年来，动态亲和调度一直是一个开放的问题。该问题是动态调度多类型的任务，多技能的服务器，使得所得到的调度系统是稳定的容量区域（吞吐量最优）和任务的平均延迟最小化在高负载附近的边界的容量区域（重流量最优）。至于应用程序，MapReduce、Hadoop和Dryad等数据密集型分析适合这种设置，其中服务器集对于不同的任务类型是异构的，因此任务类型和服务器对决定了任务的处理速率。在这种框架中使用的负载均衡算法是亲和调度的一个例子，当热点出现时，亲和调度在高负载下被期望是鲁棒的和延迟最优的。流体模型规划、MaxWeight算法和广义$cμ$规则是针对亲和调度提出的第一批算法，它们在不同意义上具有最优的理论保证，这将在相关工作部分中讨论。由于其不切实际的假设，所有这些算法对于数据中心应用程序的使用都是不实用的。加入最短队列最大权重（JSQ-MaxWeight）、JSQ-Priority和加权工作负载算法是用于具有机架结构的两级和三级数据局部性的系统的负载均衡策略的示例。在这项工作中，我们提出了广义平衡熊猫算法（GB-PANDAS）的系统与多层次的数据局部性，并证明其吞吐量最优。我们证明了这一结果下的任意分布的服务时间，而以前的理论工作假设几何分布的服务时间。大量的仿真结果表明，GB-PANDAS算法在平均时延上比JSQ-MaxWeight算法有明显的改善，性能提高了一倍
英文摘要：
Dynamic affinity scheduling has been an open problem for nearly three decades. The problem is to dynamically schedule multi-type tasks to multi-skilled servers such that the resulting queueing system is both stable in the capacity region (throughput optimality) and the mean delay of tasks is minimized at high loads near the boundary of the capacity region (heavy-traffic optimality). As for applications, data-intensive analytics like MapReduce, Hadoop, and Dryad fit into this setting, where the set of servers is heterogeneous for different task types, so the pair of task type and server determines the processing rate of the task. The load balancing algorithm used in such frameworks is an example of affinity scheduling which is desired to be both robust and delay optimal at high loads when hot-spots occur. Fluid model planning, the MaxWeight algorithm, and the generalized $cμ$-rule are among the first algorithms proposed for affinity scheduling that have theoretical guarantees on being optimal in different senses, which will be discussed in the related work section. All these algorithms are not practical for use in data center applications because of their non-realistic assumptions. The join-the-shortest-queue-MaxWeight (JSQ-MaxWeight), JSQ-Priority, and weighted-workload algorithms are examples of load balancing policies for systems with two and three levels of data locality with a rack structure. In this work, we propose the Generalized-Balanced-Pandas algorithm (GB-PANDAS) for a system with multiple levels of data locality and prove its throughput optimality. We prove this result under an arbitrary distribution for service times, whereas most previous theoretical work assumes geometric distribution for service times. The extensive simulation results show that the GB-PANDAS algorithm alleviates the mean delay and has a better performance than the JSQ-MaxWeight algorithm by twofold
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