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Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed algorithm can obtain a better solution concerning the energy cost and profit.

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To well support the hard real-time requirement for embedded applications, we enhance the real-time ability in Linux kernel through two new kernel mechanisms. Firstly, we focus on one of the major sources of unresponsiveness in the kernel, namely the presence of non-preemptible code sections, a new technique called normal task delayed locking technique can be used to reduce the OS latency. Secondly, because of the coarse-grained timer mechanism can not satisfy the microsecond-level timer resolution required by real-time tasks, we present a new microsecond-level timer mechanism, which is based on UTIME technique. The test result shows that the design can improve the real-time performance of the Linux system efficiently, which could be used to most of the embedded hard real-time systems.