Multi-resource packing for cluster schedulers

Abstract

Tasks in modern data-parallel clusters have highly diverse resource requirements along CPU, memory, disk and network. We present Tetris, a multi-resource cluster scheduler that packs tasks to machines based on their requirements of all resource types. Doing so avoids resource fragmentation as well as over-allocation of the resources that are not explicitly allocated, both of which are drawbacks of current schedulers. Tetris adapts heuristics for the multidimensional bin packing problem to the context of cluster schedulers wherein task arrivals and machine availability change in an online manner and wherein task's resource needs change with time and with the machine that the task is placed at. In addition, Tetris improves average job completion time by preferentially serving jobs that have less remaining work. We observe that fair allocations do not o er the best performance and the above heuristics are compatible with a large class of fairness policies; hence, we show how to simultaneously achieve good performance and fairness. Tracedriven simulations and deployment of our Apache YARN prototype on a node cluster show gains of over in makespan and job completion time while achieving nearly perfect fairness.

Productive Efficiency of Energy-Aware Data Centers

“…This scheduling approach may be the perfect choice when real-time responses are not required [45,46], since the omniscient algorithm performs high-quality task assignations by considering all restrictions and features of the data-center [47][48][49][50] at the cost of longer latency [46]. The scheduling process of a monolithic scheduler, such as that given by Google Borg [51], is illustrated in Figure 1.…”

Optimal Transcoding and Caching for Adaptive Streaming in Media Cloud: an Analytical Approach

“…Qazi et al [14] introduced resource management module to balance the computing resource consumption at each middlebox, in the context of software defined networking. Grandl et al [15] orchestrated multiple resources via a heuristic packing approach for cluster scheduler, to optimize the job completion time. Zhou et al [16] developed a blind resource scheduling algorithm for mobile media cloud, by formulating a finite time horizon optimization problem and statically routing user requests to appropriate media service nodes.…”

Improving Energy Efficiency for Mobile Media Cloud via Virtual Machine Consolidation

“…In [19], heuristics for virtual machines consolidation are validated. Grandl et al [11] considered multiple resource requirements (e.g., CPU, memory and networking) of multimedia services, formulating a multi-dimensional bin packing problem to simultaneously achieve satisfactory system performance and fairness. In [21], the authors consider the problem of fair load balancing different resource demands and address the problem on how to best match each application tier on each resource.…”