TY - GEN
T1 - Heterogeneous MacroTasking (HEMT) for Parallel Processing in the Cloud
AU - Shan, Yuquan
AU - Kesidis, George
AU - Jain, Aman
AU - Urgaonkar, Bhurvan
AU - Khamse-Ashari, Jalal
AU - Lambadaris, Ioannis
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/12/7
Y1 - 2020/12/7
N2 - Using tiny tasks (microtasks) has long been regarded an effective way of load balancing in parallel computing systems. When combined with containerized execution nodes pulling in work upon becoming idle, microtasking has the desirable property of automatically adapting its load distribution to the processing capacities of participating nodes-more powerful nodes finish their work sooner and, therefore, pull in additional work faster. As a result, microtasking is deemed especially desirable in settings with heterogeneous processing capacities and poorly characterized workloads. However, microtasking does have additional scheduling and I/O overheads that may make it costly in some scenarios. Moreover, the optimal task size generally needs to be learned. We herein study an alternative load balancing scheme-Heterogeneous MacroTasking (HEMT)-wherein workload is intentionally skewed according to the nodes' processing capacity. We implemented and open-sourced a prototype of HEMT within the Apache Spark application framework and conducted experiments using the Apache Mesos cluster manager. It's shown experimentally that when workload-specific estimates of nodes' processing capacities are learned, Spark with HEMT offers up to 10% shorter average completion times for realistic, multistage data-processing workloads over the baseline Homogeneous microTasking (HomT) system.
AB - Using tiny tasks (microtasks) has long been regarded an effective way of load balancing in parallel computing systems. When combined with containerized execution nodes pulling in work upon becoming idle, microtasking has the desirable property of automatically adapting its load distribution to the processing capacities of participating nodes-more powerful nodes finish their work sooner and, therefore, pull in additional work faster. As a result, microtasking is deemed especially desirable in settings with heterogeneous processing capacities and poorly characterized workloads. However, microtasking does have additional scheduling and I/O overheads that may make it costly in some scenarios. Moreover, the optimal task size generally needs to be learned. We herein study an alternative load balancing scheme-Heterogeneous MacroTasking (HEMT)-wherein workload is intentionally skewed according to the nodes' processing capacity. We implemented and open-sourced a prototype of HEMT within the Apache Spark application framework and conducted experiments using the Apache Mesos cluster manager. It's shown experimentally that when workload-specific estimates of nodes' processing capacities are learned, Spark with HEMT offers up to 10% shorter average completion times for realistic, multistage data-processing workloads over the baseline Homogeneous microTasking (HomT) system.
UR - http://www.scopus.com/inward/record.url?scp=85100487237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100487237&partnerID=8YFLogxK
U2 - 10.1145/3429885.3429962
DO - 10.1145/3429885.3429962
M3 - Conference contribution
AN - SCOPUS:85100487237
T3 - WOC 2020 - Proceedings of the 2020 6th International Workshop on Container Technologies and Container Clouds, Part of Middleware 2020
SP - 7
EP - 12
BT - WOC 2020 - Proceedings of the 2020 6th International Workshop on Container Technologies and Container Clouds, Part of Middleware 2020
PB - Association for Computing Machinery, Inc
T2 - 6th International Workshop on Container Technologies and Container Clouds, WOC 2020 - Part of Middleware 2020
Y2 - 7 December 2020 through 11 December 2020
ER -