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Recently, Parallel Intrusion Detection (PID) becomes very popular and its procedure of the parallel processing is called a PID application (PIDA). This PIDA can be regarded as a Bag-of-Tasks (BoT) application, consisting of multiple tasks that can be processed in parallel. Given multiple PIDAs (i.e., BoT applications) to be handled, when the private cloud has insufficiently available resources to afford all tasks, some tasks have to be outsourced to public clouds with resource-used costs. The key challenge here is how to schedule tasks on hybrid clouds to minimize makespan given a limited budget. This problem can be formulated as an Integer Programming model, which is generally NP-Hard. Accordingly, in this paper, we construct an Iterated Local Search (ILS) algorithm, which employs an effective heuristic to obtain the initial task sequence and utilizes an insertion-neighbourhood-based local search method to explore better task sequences with lower makespans. A swap-based perturbation operator is adopted to avoid local optimum. With the objective of improving the proposal’s efficiency without loss of any effectiveness, to calculate task sequences’ objectives, we construct a Fast Task Assignment (FTA) method by integrating an existing Task Assignment (TA) method with an acceleration mechanism designed through theoretical analysis. Accordingly, the proposed ILS is named FILS. Experimental results show that FILS outperforms the existing best algorithm for the considered problem, considerably and significantly. More importantly, compared with TA, FTA achieves a 2.42x speedup, which verifies that the acceleration mechanism employed by FTA is able to remarkably improve the efficiency. Finally, impacts of key factors are also evaluated and analyzed, exhaustively.

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SummaryUsers are willing to execute bag‐of‐task applications consisting of multiple tasks on clouds, since cloud resources are delivered in a pay‐as‐you‐go manner. Given multiple bag‐of‐task applications to be executed with user‐specified quality‐of‐service demands, a cloud provider has to outsource some tasks to public clouds when its private cloud has insufficient resources to afford all applications' tasks. The key issue is how to schedule tasks on hybrid clouds (environments consisting of a private cloud and multiple public clouds) for maximizing the cloud provider's profit while meeting the quality‐of‐service demands. To solve this problem, we propose an efficient particle swarm optimization algorithm (EPSO) and three hybrid ones (HEPSO1‐HEPSO3), in which task sequences are considered as solutions. A mapping operator (BBMO) is developed to map particles to solutions and a quick task dispatching method containing an acceleration method is designed to calculate solutions' objectives. Experimental results show that EPSO not only outperforms an existing PSO (the best algorithm for solving a problem that is a special case of ours) significantly but also achieves a 11.48x speedup. The HEPSO1 to HEPSO3 outperform EPSO. The BBMO outperforms the well‐known ranked‐order value rule and achieves a 5.47x speedup. The acceleration method in quick task dispatching brings a 2.69x speedup.