Abstract. We address parallel jobs scheduling problem for computational GRID systems. We concentrate on two-level hierarchy scheduling: at the first level broker allocates computational jobs to parallel computers. At the second level each computer generates schedules of the parallel jobs assigned to it by its own local scheduler. Selection, allocation strategies, and efficiency of proposed hierarchical scheduling algorithms are discussed.

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Аннотация. Рассмотрена проблема балансировки нагрузки для множества параллельных задач на группе географически распределенных кластеров уменьшающая количество энергии при вычислениях. Предложены несколько алгоритмов распределения задач и проведена экспериментальная проверка их эффективности.

At present, big companies such as Amazon, Google, Facebook, Microsoft, Yahoo! own huge datacenters with thousands of nodes. These clusters are used simultaneously by many users. The users submit jobs containing one or more tasks. Task flow is usually a mix of short, long, interactive, batch, and tasks with different priorities. Cluster scheduler decides on which server to run the task, where the task is then run as a process, container or a virtual machine. Scheduler optimizations are important as they provide higher server utilization, lower latency, improved load balancing, and fault tolerance. Achieving good task placement is hard. The problem has multiple dimensions and requires algorithmically complex optimizations. This Grushin D.A., Kuzyurin N.N. On an effective scheduling problem in computation clusters.