In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources\nin the form of virtual machines are provided over the Internet. A user can rent an arbitrary number\nof computing resources to meet their requirements, making cloud computing an attractive choice\nfor executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual\nmachines is an important problem in the cloud computing environment. This paper proposes a greedy\nand a genetic algorithm with an adaptive selection of suitable crossover and mutation operations\n(named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous\nvirtual machines. A comprehensive simulation study has been done to evaluate the performance\nof the proposed algorithms in terms of their solution quality and efficiency. The simulation results\nshow that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of\nsolution quality.
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