To Sell or Not To Sell: Trading Your Reserved Instances in Amazon EC2 Marketplace

Recently, Amazon EC2 offers a reserved instance marketplace, where cloud users can sell their idle reserved instances varying in contract lengths and pricing options for avoiding the waste of their unused reservations. However, without knowing the future demands, it is hard for users to determine how to sell instances optimally, for it would incur more cost if new demands arrive after selling their reserved instances. For dealing with this problem, in this paper we first propose three online selling algorithms to guide cloud users in making decisions whether or not to sell their reservations in Amazon EC2 marketplace while guaranteeing competitive ratios. We prove theoretically that the three proposed online algorithms can guarantee bounded competitive ratios, whose values are specific to the type of reserved instances under consideration. Specifically, for all standard instances (Linux, US East) for 1-year terms in Amazon EC2, compared with a benchmark optimal offline algorithm, our algorithm A3T/4 can achieve a ratio of 2-α-a/4 in managing instance purchasing cost, where α is the entitled discount due to reservation and a is the selling discount specified by the user who sells its reservations. Finally, through extensive experiments based on workload data collected from real-world applications, we validate the effectiveness of our online instance selling algorithms by showing that it can bring significant cost savings to cloud users compared with always keeping their reservations in Amazon EC2 reserved instance marketplace.

[1]  Minghua Chen,et al.  Online algorithms for uploading deferrable big data to the cloud , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[2]  Tao Qin,et al.  Randomized Mechanisms for Selling Reserved Instances in Cloud Computing , 2017, AAAI.

[3]  Yossi Azar,et al.  Prompt Mechanism for Ad Placement over Time , 2011, SAGT.

[4]  Gerhard J. Woeginger,et al.  Online Algorithms , 1998, Lecture Notes in Computer Science.

[5]  Anna R. Karlin,et al.  Competitive randomized algorithms for non-uniform problems , 1990, SODA '90.

[6]  Edith Cohen,et al.  Competitive Analysis of the LRFU Paging Algorithm , 2001, WADS.

[7]  Shijun Liu,et al.  Selling Reserved Instances through Pay-as-You-Go Model in Cloud Computing , 2017, 2017 IEEE International Conference on Web Services (ICWS).

[8]  Tao Qin,et al.  New Mechanism for Reservation in Cloud Computing , 2015, AAMAS.

[9]  Baochun Li,et al.  Dynamic Cloud Instance Acquisition via IaaS Cloud Brokerage , 2015, IEEE Transactions on Parallel and Distributed Systems.

[10]  Allan Borodin,et al.  Online computation and competitive analysis , 1998 .

[11]  Prashant J. Shenoy,et al.  Dynamic Provisioning of Multi-tier Internet Applications , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[12]  Jerome A. Rolia,et al.  Capacity Management and Demand Prediction for Next Generation Data Centers , 2007, IEEE International Conference on Web Services (ICWS 2007).

[13]  Yu-Ju Hong,et al.  Dynamic server provisioning to minimize cost in an IaaS cloud , 2011, PERV.

[14]  Wei Wang,et al.  To Reserve or Not to Reserve: Optimal Online Multi-Instance Acquisition in IaaS Clouds , 2013, ICAC.