Journal
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 37, Issue 3, Pages 540-555Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2019.2895473
Keywords
Container communication; multi-resource load balance; large-scale data centers; container placement; container reassignment
Funding
- National Key Research and Development Program of China [2018YFB0803405]
- China Postdoctoral Science Foundation [2018M630117]
- National Natural Science Foundation of China [61802024]
- Huawei Autonomous and Service 2.0 Project [A2018185]
- Baidu Songguo Plan
- NSF of China [61602266]
- NSF of Tianjin [16JCYBJC41900]
- Beijing Outstanding Young Scientist Project
- PolyU G-YBAG
- China National Funds for Distinguished Young Scientists [61825204]
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Containerization has been used in many applications for isolation purposes due to its lightweight, scalable, and highly portable properties. However, to apply containerization in large-scale Internet data centers faces a big challenge. Services in data centers are always instantiated as a group of containers, which often generate heavy communication workloads and therefore resulting in inefficient communications and downgraded service performance. Although assigning the containers of the same service to the same server can reduce the communication overhead, this may cause heavily imbalanced resource utilization since containers of the same service are usually intensive to the same resource. To reduce communication cost as well as balance the resource utilization in large-scale data centers, we further explore the container distribution issues in a real industrial environment and find that such conflict lies in two phases-container placement and container reassignment. The objective of this paper is to address the container distribution problem in these two phases. For the container placement problem, we propose an efficient communication aware worst fit decreasing algorithm to place a set of new containers into data centers. For the container reassignment problem, we propose a two-stage algorithm called Sweep&Search to optimize a given initial distribution of containers by migrating containers among servers. We implement the proposed algorithms in Baidu's data centers and conduct extensive evaluations. Compared with the state-of-the- art strategies, the evaluation results show that our algorithms perform better up to 70% and increase the overall service throughput up to 90% simultaneously.
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