Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: interdata center network bandwidth to improve performance. Unfortunately, these systems may encounter a cost-bottleneck ($) because they have not considered data transfer cost ($), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this paper, we present Kimchi, a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kim-chi is also mindful of data transfer cost in the presence of dynamics. A Kimchi prototype has been implemented on Spark and experiments show that it reduces cost by 14% ∼ 24% without impacting performance and reduces query execution time by 45% ∼ 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g. Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.
|Original language||English (US)|
|Title of host publication||Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020|
|Editors||Laurent Lefevre, Carlos A. Varela, George Pallis, Adel N. Toosi, Omer Rana, Rajkumar Buyya|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - May 2020|
|Event||20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020 - Melbourne, Australia|
Duration: May 11 2020 → May 14 2020
|Name||Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020|
|Conference||20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020|
|Period||5/11/20 → 5/14/20|
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Copyright 2021 Elsevier B.V., All rights reserved.
- Data Analytics
- Network Cost