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It makes it possible to scale up\/down the allocated computing resources on demand in response to fluctuations of the workload. On clouds, this represents a necessary feature to keep the operating cost at affordable levels while accommodating user-defined QoS requirements. In this article, we study this problem from a game-theoretic perspective. The control logic driving elasticity is distributed among local control agents capable of choosing the right amount of resources to use by each module. In a first step, we model the problem as a noncooperative game in which agents pursue their self-interest. We identify the Nash equilibria and we design a distributed procedure to reach the best equilibrium in the Pareto sense. As a second step, we extend the noncooperative formulation with a decentralized incentive-based mechanism in order to promote cooperation by moving the agreement point closer to the system optimum. Simulations confirm the results of our theoretical analysis and the quality of our strategies.<\/jats:p>","DOI":"10.1145\/2903146","type":"journal-article","created":{"date-parts":[[2016,6,10]],"date-time":"2016-06-10T13:00:33Z","timestamp":1465563633000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["A Game-Theoretic Approach for Elastic Distributed Data Stream Processing"],"prefix":"10.1145","volume":"11","author":[{"given":"Gabriele","family":"Mencagli","sequence":"first","affiliation":[{"name":"University of Pisa, Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,6,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Joao Gama and Mohamed Medhat Gaber. 2007. Learning from Data Streams: Processing Techniques in Sensor Networks (1 ed.).   Joao Gama and Mohamed Medhat Gaber. 2007. 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