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Marco Netto is a researcher working with resource management for distributed systems since 2000. His projects explore aspects of SLA management, virtualization, HPC, performance evaluation, job scheduling, quality-of-service, optimization issues, simulation, analytics, big data, scientific computing, among others. Marco is currently the research manager of the Industrial Cloud Technologies group at IBM Research Brazil, and an IBM Master Inventor. He is also an IEEE Senior member and contributes to article revision for several scientific journals and conferences in distributed systems.
Study elasticity and auto-scaling aspects of cloud and big data applications. the work contains: (a) auto-scaling/elasticity policies based on user patience and Prospect Theory (this is a novel way that performs resource management based on information of user expectation from cloud performance), (b) deep analysis on proactive vs reactive auto-scaling/elasticity triggers for resource management in cloud, (c) methods for determining the step size of resource scaling operations under bounded and unbounded maximum capacity, (d) the introduction of a new metric called Auto-scaling Demand Index, which represents the difference between the target utilization interval and the actual values, (e) auto-scaling/elasticity in cloud based on user application data which is able to speed the trigger of resource provisioning faster than monitoring system resource usage (e.g. CPU, memory, network), which is the classical state of the art solution.