Abstract
Cloud containers represent a new, light-weight alternative to virtual machines in cloud computing. A user job may be described by a container graph that specifies the resource profile of each container and container dependence relations. This work is the first in the cloud computing literature that designs efficient market mechanisms for container based cloud jobs. Our design targets simultaneously incentive compatibility, computational efficiency, and economic efficiency. It further adapts the idea of batch online optimization into the paradigm of mechanism design, leveraging agile creation of cloud containers and exploiting delay tolerance of elastic cloud jobs. The new and classic techniques we employ include: (i) compact exponential optimization for expressing and handling non-traditional constraints that arise from container dependence and job deadlines; (ii) the primal-dual schema for designing efficient approximation algorithms for social welfare maximization; and (iii) posted price mechanisms for batch decision making and truthful payment design. Theoretical analysis and trace-driven empirical evaluation verify the efficacy of our container auction algorithms.
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References
Aliyun Container Engine, http://cn.aliyun.com/product/contain-erservice
Amazon ECS, https://aws.amazon.com/cn/ecs/
Azure Container, https://azure.microsoft.com/en-us/services/container-service/
Batch Applications, https://github.com/Azure/azure-content/blob/master/articles/batch/batch-hpc-solutions.md
Google Cluster Data, https://code.google.com/p/googlecl-usterdata
Google Container Engine, http://cloud.google.com/container-engine/
Awerbuch B, Azar Y, Meyerson A (2003) Reducing truth-telling online mechanisms to online optimization. In: Proceedings of ACM STOC
Bitton S, Emek Y, Kutten S (2018) Efficient dispatching of job batches in emerging clouds. In: Proceedings of IEEE INFOCOM
Chen B, Deng X, Zang W (2004) On-Line Scheduling a batch processing system to minimize total weighted job completion time. J Comb Optim 8(1):85–95
Cheng K, Slien Y, Zhang Y, Zhu X, Wang L, Zhong H (2019) Towards efficient privacy-preserving auction mechanism for two-sided cloud markets. In: Proceedings of IEEE ICC
Dove A (2013) Life Science Technologies: Biology Watches the cloud. Science 340(6138):1350–1352
Etzion H, Moor S (2008) Simulation of Online Selling with Posted-price and Auctions: Comparison of Dual Channel’s Performance under Different Auction Mechanisms. In: Proceedings of HICSS
Golin MJ, Rote G (1998) A Dynamic Programming Algorithm for Constructing Optimal Prefix-free Codes with Unequal Letter Costs. IEEE Trans Inf Theory 44(5):1770–1781
Gopinathan A, Li Z (2011) Strategyproof auctions for balancing social welfare and fairness in secondary spectrum markets. In: Proceedings of the IEEE INFOCOM
Gu S, Li Z, Wu C, Huang C (2016) An efficient auction mechanism for service chains in the NFV market. In: Proceedings of IEEE INFOCOM
He S, Guo L, Guo Y, Wu C (2012) Elastic application container: a lightweight approach for cloud resource provisioning. In: Proceedings of IEEE international conference on advanced information NETWORKING and applications
Huang J, Wu J, Chen L, Yan J (2019) Utility-aware batch-processing algorithms for dynamic carpooling based on double auction. In: Proceedings of IEEE ISPA/BDCloud/socialcom/ sustaincom
Huang Z, Kim A (2015) Welfare maximization with production costs: a primal dual approach. In: Proceedings of ACM-SIAM SODA
Jiao Y, Wang P, Niyato D, Suankaewmanee K (2019) Auction mechanisms in cloud/fog computing resource allocation for public blockchain networks. IEEE Trans Parallel Distrib Syst 30(9):1975–1989
Kumar D, Shae Z, Jamjoom H (2012) Scheduling batch and heterogeneous jobs with runtime elasticity in a parallel processing environment. In: Proceedings of IEEE IPDPSW
Lu Y, Zheng X, Li L, Xu LD (2020) Pricing the cloud: a qos-based auction approach. Enterpr Inf Syst 14(3):334–351
Mohamed NM, Lin H, Feng W (2013) Accelerating Data-intensive Genome Analysis in the Cloud. In: Proceedings of BICob
Myerson RB (1981) Optimal auction design. Math Oper Res 6(1):58–73
RightScale (2013) Social Gaming in the Cloud: A Technical White Paper
Shi W, Wu C, Li Z (2014) RSMOA: A revenue and social welfare maximizing online for dynamic cloud resource provisioning. In: Proceedings of IEEE IWQos
Shi W, Zhang L, Wu C, Li Z, Lau F (2014) An online auction framework for dynamic resource provisioning in cloud computing. In: Proceedings of ACM SIGMETRICS
Tosatto A, Ruiu P, Attanasio A (2015) Container-Based Orchestration in cloud: State of the art and challenges. In: Proceedings of ninth international conference on complex, intelligent, and software intensive systems
Waibel P, Yeshchenko A, Schulte S, Mendling J (2018) Optimized container-based process execution in the cloud. In: Proceedings of OTM. Springer, Berlin
Williamson DP (2002) The Primal-Dual method for approximation algorithms. Math Program 91(3):447–478
Xu X, Yu H, Pei X (2014) A novel resource scheduling approach in container based clouds. In: Proceedings of IEEE ICCS
Zhang E, Zhuo YQ (2011) Online Advertising Channel Choice — Posted Price vs Auction
Zhang H, Jiang H, Li B, Liu F, Vasilakos AV, Liu J (2016) A framework for truthful online auctions in cloud computing with heterogeneous user demands. IEEE Trans Comput 65(3):805–818
Zhang L, Li Z, Wu C (2014) Dynamic resource provisioning in cloud computing: a randomized auction approach. In: Proceedings of IEEE INFOCOM
Zhang X, Huang Z, Wu C, Li Z, Lau F (2015) Online Auctions in IaaS Clouds: Welfare and Profit Maximization with Server Costs. In: Proceedings of ACM SIGMETRICS
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He, Y., Ma, L., Zhou, R. et al. Batch Auction Design for Cloud Container Services. Mobile Netw Appl 27, 222–235 (2022). https://doi.org/10.1007/s11036-020-01626-z
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DOI: https://doi.org/10.1007/s11036-020-01626-z