Optimal Scheduling and Management on Correlating Reliability, Performance, and Energy Consumption for Multiagent Cloud Systems

In large-scale cloud computing environments, a multiagent cloud system (MACS) can be developed for effectively managing cloud infrastructure. In principle, a MACS has two essential components, a global agent (GA) and multiple local agents (LAs). The GA dispatches user requests to multiple LAs, while the LAs make optimal resource management strategies for serving user requests received. To develop optimal request scheduling strategies and resource management strategies for the MACS, multiple correlated metrics, particularly reliability, performance, and energy consumption need to be comprehensively considered. In this paper, a reliability-performance-energy correlation model is first proposed. The new model captures significant effects of random resource failures and recovery on MACS performance and energy consumption to ensure high fidelity and precise evaluation. A net profit optimization model depicting the tradeoff between performance and energy consumption is then developed based on the proposed theoretical model. Furthermore, an approach for optimization is proposed, where LAs can perform dynamic resource management for reaching optimum in the net profit metric. A genetic algorithm is also designed and implemented for searching the solution of global request scheduling for the GA. Numerical examples illustrate that the MACS can achieve efficient and comprehensive scheduling and management capability based on cooperation between the GA and LAs.