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Balancing performance, resource efficiency and energy efficiency for virtual machine deployment in DVFS-enabled clouds: an evolutionary game theoretic approach., , , , , and . GECCO (Companion), page 1205-1212. ACM, (2014)Evolutionarily reconfigurable cloud-integrated body sensor networks., , , and . HealthCom, page 633-639. IEEE, (2015)Cielo: An Evolutionary Game Theoretic Framework for Virtual Machine Placement in Clouds., , , , and . FiCloud, page 1-8. IEEE Computer Society, (2014)Multiobjective Communication Optimization for Cloud-Integrated Body Sensor Networks., , , , and . CCGRID, page 685-693. IEEE Computer Society, (2014)Leveraging evolutionary multiobjective games for configuring cloud-integrated body sensor networks., , , , and . SCIS&ISIS, page 630-636. IEEE, (2014)Configuring Cloud-integrated Body Sensor Networks with Evolutionary Algorithms., , , , and . BODYNETS, ICST, (2014)Evolutionary Game Theoretic Power Capping for Virtual Machine Placement in Clouds., , , , and . BICT, ICST, (2014)An Evolutionary Game Theoretic Approach for Configuring Cloud-Integrated Body Sensor Networks., , , , and . NCA, page 277-281. IEEE Computer Society, (2014)Adaptability and Stability in Dynamic Integration of Body Sensor Networks with Clouds., , , and . NCA, page 98-105. IEEE Computer Society, (2015)Low-latency Job Scheduling with Preemption for the Development of Deep Learning., , and . OpML, page 27-30. USENIX Association, (2019)