This paper presents the HybriD-GM model conception, from modeling to consolidation. The D-GM environment is also extended, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering the CPU and GPU integration. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables granularity control, optimizing hardware resources in distributed computations organized as tree data structures. In the HybriD-GM evaluation, simulations of Shor’s and Grover’s algorithms achieve significant performance improvements in comparison to the previous D-GM version, and also with other related works, for example, LIQUi|⟩ and ProjectQ simulators.

This paper has as its primary objective to introduce the the HybriD-GM model conception, as well as extend the D-GM environment, providing efficient parallel executions for quantum computing simulations, targeted to hybrid architectures considering both CPU and GPU. By managing projection operators over quantum structures, and exploring coalescing memory access patterns, the HybriD-GM model enables the granularity control, optimizing hardware resources in distributed computations organized as tree data-structures. In the HybriD-GM evaluation, simulations of Shor’s and Grover’s algorithms achieve significant performance improvements in comparison to the D-GM previous version and to the LIQUi|⟩ and ProjectQ simulators.

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY and 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLA), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1.