理论上有保证的多服务器限期作业在线调度
Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs
摘要：
多服务器作业在现代计算集群中是必不可少的。多服务器作业具有多个任务组件，每个任务组件负责处理特定大小的工作负载。有效的在线工作负载调度是至关重要的，但具有挑战性的同位异构多服务器作业。分派策略应该决定$(i)$在哪里启动到达作业的每个任务组件实例，以及$(ii)$每个任务组件处理的工作负载的大小。现有的策略是明确的和有效的，当面对服务的本地性和资源争用在离线和在线设置。然而，当加入期限感知约束时，这些策略的理论优越性不能得到保证。为了填补这一理论空白，本文在时空资源网格模型的基础上，设计了一种基于$α$竞争机制的多服务器任务调度策略.我们将问题表示为一个社会福利最大化规划，并使用几个精心设计的伪函数在线求解。将社会福利表示为作业效用和计算集群效用之和。严格证明了所提出的策略对于某些$α\geq 2$是$α$ -竞争的.我们还通过仿真验证了它的理论优越性，结果表明，它明显优于两个手工制定的基线政策的社会福利。
英文摘要：
Multi-server jobs are imperative in modern computing clusters. A multi-server job has multiple task components and each of the task components is responsible for processing a specific size of workloads. Efficient online workload dispatching is crucial but challenging to co-located heterogeneous multi-server jobs. The dispatching policy should decide $(i)$ where to launch each task component instance of the arrived jobs and $(ii)$ the size of workloads that each task component processes. Existing policies are explicit and effective when facing service locality and resource contention in both offline and online settings. However, when adding the deadline-aware constraint, the theoretical superiority of these policies could not be guaranteed. To fill the theoretical gap, in this paper, we design an $α$-competitive online workload dispatching policy for deadline-aware multi-server jobs based on the spatio-temporal resource mesh model. We formulate the problem as a social welfare maximization program and solve it online with several well designed pseudo functions. The social welfare is formulated as the sum of the utilities of jobs and the utility of the computing cluster. The proposed policy is rigorously proved to be $α$-competitive for some $α\geq 2$. We also validate the theoretical superiority of it with simulations and the results show that it distinctly outperforms two handcrafted baseline policies on the social welfare.
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