Strategic Management of Advance Reservations in Cloud and Network Services

This project is supported by the National Science Foundation under grant CNS-1717858. This support is graciously acknowledged.


Advance reservation (AR) services form a pillar of the economy. For instance, they are widely deployed in the industries of transportation, lodging, and health care. They are also increasingly being adopted for the management of resources in communication networks and in cloud computing. For users they offer assurance that resources will be available when needed, while for providers they offer a new stream of revenue and the ability to forecast demand. Yet, the behavior of systems supporting AR is complex, since the decision of a customer about making a reservation affects the performance of other customers. Furthermore, pricing schemes employed by service providers have a significant impact on customer behavior. The main goals of this project are to develop mathematical models for characterizing the strategic behavior of users and providers in cloud and networking systems that support AR. The project also entails the design of efficient resource allocation and pricing mechanisms to incentivize widespread adoption of AR services in a way that benefits both customers and the service provider.

The main goals of this project are: i) to develop models for characterizing the strategic behavior of all participants in advance reservation schemes employed in cloud computing and network systems; ii) to analyze the equilibrium outcomes of these models using game theory; iii) to design efficient resource allocation and pricing mechanisms that incentivize the deployment of AR services to the benefit of all participants; and iv) to transition theory into practice. This project incorporates key features of advance reservation in cloud and network services along several dimensions, including the randomness of the demand, the heterogeneity of the workload, the dynamic nature of the network traffic and topology, and the ability for users to learn and adapt their strategies based on historical data. The investigations entail characterizing the equilibria outcomes and their efficiency, using both established and newer concepts, such as the Price of Anarchy and the Price of Conservatism in non-cooperative contexts, and the Core and Bargaining Schemes in cooperative contexts. The project further includes measurements on an open cloud marketplace (Massachusetts Open Cloud) to inform the analysis and guide both service providers and customers in their strategic decisions.



  • Zhenpeng Shi (PhD)
  • Jonathan Chamberlain (PhD)
  • Ghazanfar Yezdan (Undergraduate)
  • Arturo Garcia (Undergraduate)



  1. Jonathan Chamberlain, Eran Simhon, and David Starobinski, “Preemptible Queues with Advance Reservations: Strategic Behavior and Revenue Management,” European Journal of Operational Research (EJOR), Vol. 30, No. 2, pp. 561-578, September 2021. pdf
  2. Jonathan Chamberlain and David Starobinski, “Strategic Revenue management of Preemptive versus Non-preemptive Queues,” Operations Research Letters, Vol. 49, pp. 184-187, March 2021. pdf
  3. Jonathan Chamberlain and David Starobinski, “Social Welfare and Price of Anarchy in Preemptive Priority Queues,” Operations Research Letters, Vol. 48, pp. 530-533, July 2020. pdf
  4. Zhenpeng Shi, Azer Bestavros, Ariel Orda, and David Starobinski, “A Game-Theoretic Analysis of Shared/Buy-in Computing Systems,” IEEE Open Journal of the Communications Society, Vol. 1, pp. 190-204, 2020. pdf
  5. Christopher Liao, Yonatan Klausner, David Starobinski, Eran Simhon, and Azer Bestavros, “A Case Study of a Shared/Buy-In Computing Ecosystem,” Cluster Computing (Springer), Vol. 21, No. 3, pp. 1595-1606, September 2018. pdf
  6. Eran Simhon and David Starobinski, “On the Impact of Information Disclosures on Advance Reservations: A Game-Theoretic View,” European Journal of Operations Research (EJOR), Vol. 267, No. 3, pp. 1075-1088, June 2018. pdf