TY - GEN
T1 - Resolution Matters
T2 - 15th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2022
AU - Kur, Justin
AU - Chen, Jingshu
AU - Xue, Ji
AU - Huang, Jun
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Overall resource utilization in public cloud data centers remains very low. To increase the efficiency of these data centers, low priority batch jobs are often co-located on the same machines as latency-sensitive jobs. Existing methodologies have used machine learning to predict the amount of resources that should be reserved for these jobs to maintain acceptable latency. However, these methodologies overlook the impact of measurement granularity on usage prediction and scheduling performance. When batch jobs have long durations, coarsegrained data can be used to make the prediction problem less challenging, but the resulting predictions may degrade scheduling performance. In this paper, we investigate the impact of measurement granularity on scheduler performance using extensive trace-driven simulation and job data generated from the Alibaba cluster trace. © 2022 IEEE.
AB - Overall resource utilization in public cloud data centers remains very low. To increase the efficiency of these data centers, low priority batch jobs are often co-located on the same machines as latency-sensitive jobs. Existing methodologies have used machine learning to predict the amount of resources that should be reserved for these jobs to maintain acceptable latency. However, these methodologies overlook the impact of measurement granularity on usage prediction and scheduling performance. When batch jobs have long durations, coarsegrained data can be used to make the prediction problem less challenging, but the resulting predictions may degrade scheduling performance. In this paper, we investigate the impact of measurement granularity on scheduler performance using extensive trace-driven simulation and job data generated from the Alibaba cluster trace. © 2022 IEEE.
KW - Cloud computing
KW - Job scheduling
KW - Resource usage prediction
UR - http://www.scopus.com/inward/record.url?scp=85150684856&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85150684856&origin=recordpage
U2 - 10.1109/UCC56403.2022.00029
DO - 10.1109/UCC56403.2022.00029
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022
SP - 163
EP - 166
BT - Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC 2022)
PB - IEEE
Y2 - 6 December 2022 through 9 December 2022
ER -