Physical Sciences and Mathematics Commons^™

Scheduling In Mapreduce Clusters, Chen He

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing.

As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied.

The objective of this dissertation is to …

Dynamic Load Balancing For I/O-Intensive Applications On Clusters, Xiao Qin, Hong Jiang, Adam Manzanares, Xiaojun Ruan, Shu Yin

School of Computing: Faculty Publications

Load balancing for clusters has been investigated extensively, mainly focusing on the effective usage of global CPU and memory resources. However, previous CPU- or memory-centric load balancing schemes suffer significant performance drop under I/O-intensive workloads due to the imbalance of I/O load. To solve this problem, we propose two simple yet effective I/O-aware load-balancing schemes for two types of clusters: (1) homogeneous clusters where nodes are identical and (2) heterogeneous clusters, which are comprised of a variety of nodes with different performance characteristics in computing power, memory capacity, and disk speed. In addition to assigning I/O-intensive sequential and parallel jobs …

Allocating Non-Real-Time And Soft Real-Time Jobs In Multiclusters, Ligang He, Stephen A. Jarvis, Daniel P. Spooner, Hong Jiang, Donna N. Dillenberger, Graham R. Nudd

School of Computing: Faculty Publications

This paper addresses workload allocation techniques for two types of sequential jobs that might be found in multicluster systems, namely, non-real-time jobs and soft real-time jobs. Two workload allocation strategies, the Optimized mean Response Time (ORT) and the Optimized mean Miss Rate (OMR), are developed by establishing and numerically solving two optimization equation sets. The ORT strategy achieves an optimized mean response time for non-real-time jobs, while the OMR strategy obtains an optimized mean miss rate for soft real-time jobs over multiple clusters. Both strategies take into account average system behaviors (such as the mean arrival rate of jobs) in …

Irrigation Scheduling Of Soybeans, Corn, Wheat, And Potatoes (Camac Progress Report 87-8), Blaine L. Blad

School of Natural Resources: Faculty Publications

No abstract provided.

A Demonstration And Evaluation Of The Use Of Climate Information To Support Irrigation Scheduling And Other Agricultural Operations (Camac Progress Report 87-4), K. G. Hubbard, D. A. Wilhite, S. J. Meyer, J. Booysen, R. Sagar, J. J. Schmidt, J. R. Hines

School of Natural Resources: Faculty Publications

No abstract provided.