Purdue University Graduate School
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Latency-Aware Pricing in the Cloud Market

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posted on 2019-06-10, 20:41 authored by Yang ZhangYang Zhang
Latency is regarded as the Achilles heel of cloud computing. Pricing is an essential component in the cloud market since it not only directly affects a cloud service provider's (CSP's) revenue but also a user's budget. This dissertation investigates the latency-aware pricing schemes that provide rigorous performance guarantees for the cloud market. The research is conducted along the following major problems as summarized below:

First, we will address a major challenge confronting the CSPs utilizing a tiered storage (with cold storage and hot storage) architecture - how to maximize their overall profit over a variety of storage tiers that offer distinct characteristics, as well as file placement and access request scheduling policies. To this end, we propose a scheme where the CSP offers a two-stage auction process for (a) requesting storage capacity, and (b) requesting accesses with latency requirements. Our two-stage bidding scheme provides a hybrid storage and access optimization framework with the objective of maximizing the CSP's total net profit over four dimensions: file acceptance decision, placement of accepted files, file access decision and access request scheduling policy. The proposed optimization is a mixed-integer nonlinear program that is hard to solve. We propose an efficient heuristic to relax the integer optimization and to solve the resulting nonlinear stochastic programs. The algorithm is evaluated under different scenarios and with different storage system parameters, and insightful numerical results are reported by comparing the proposed approach with other profit-maximization models. We see a profit increase of over 60% of our proposed method compared to other schemes in certain simulation scenarios.

Second, we will resolve one of the challenges when using Amazon Web Services (AWS). Amazon Elastic Compute Cloud (EC2) provides two most popular pricing schemes--i) the costly on-demand instance where the job is guaranteed to be completed, and ii) the cheap spot instance where a job may be interrupted. We consider a user can select a combination of on-demand and spot instances to finish a task. Thus he needs to find the optimal bidding price for the spot-instance, and the portion of the job to be run on the on-demand instance. We formulate the problem as an optimization problem and seek to find the optimal solution. We consider three bidding strategies: one-time requests with expected guarantee, one-time requests with penalty for incomplete job and violating the deadline, and persistent requests. Even without a penalty on incomplete jobs, the optimization problem turns out to be non-convex. Nevertheless, we show that the portion of the job to be run on the on-demand instance is at most half. If the job has a higher execution time or smaller deadline, the bidding price is higher and vice versa. Additionally, the user never selects the on-demand instance if the execution time is smaller than the deadline. The numerical results illustrate the sensitivity of the effective portfolio to several of the parameters involved in the model. Our empirical analysis on the Amazon EC2 data shows that our strategies can be employed on the real instances, where the expected total cost of the proposed scheme decreases over 45% compared to the baseline strategy.


Degree Type

  • Doctor of Philosophy


  • Industrial Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Seokcheon Lee

Additional Committee Member 2

Dr. Christopher J. Quinn

Additional Committee Member 3

Dr. Mario Ventresca

Additional Committee Member 4

Dr. Yu Xiang