HySARC2: Hybrid Scheduling Algorithm Based on Resource Clustering in Cloud Environments

Vasile, Mihaela-Andreea; Pop, Florin; Tutueanu, Radu-Ioan; Cristea, Valentin

doi:10.1007/978-3-319-03859-9_36

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…As a result, most good genes hidden inside of the current best solution can be inherited by the solutions generated by the mapping operator, benefiting the high qualities of the obtained solutions and improving NFA's effectiveness. According to this conclusion, we propose a new movement scheme, in which a firefly's position is updated by Equation (14) when this firefly moves toward a brighter firefly j.…”

Section: Novel Movement Schemementioning

confidence: 99%

“…Equation (14) indicates that the value domain of the randomization is [− 1 2 r * i , 1 2 r * i ], relying on the value of r * i . Thus, when a firefly is far away from the brightest one (i.e., a great r * i value), a wide searching range can be obtained.…”

Section: Novel Movement Schemementioning

confidence: 99%

“…Lines 14-27 represent the evolving procedure. If firefly j is brighter than firefly i (i.e., I j > I i in Line 17), firefly i is required to move toward the firefly j and its location X i will be updated by Equation (14). Note that I j > I i indicates that the solution corresponding to firefly j is better than that corresponding to firefly i, i.e., f (π j ) < f (π i ).…”

Section: E Description Of Nfamentioning

confidence: 99%

“…In the proposed NFA, there are two parameters β 0 and γ , both of which are used to update fireflies' positions in Equation (14). As indicated in Section IV-D, β 0 ∈ [0, 1] in general.…”

Section: B Parameter Determinationmentioning

confidence: 99%

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A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

et al. 2019

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This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence); (2) calculating the solution's objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distancebased mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more ''good genes'' hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications.

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Section: Novel Movement Schemementioning

confidence: 99%

Section: Novel Movement Schemementioning

confidence: 99%

Section: E Description Of Nfamentioning

confidence: 99%

Section: B Parameter Determinationmentioning

confidence: 99%

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A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

et al. 2019

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“…The termination criterion of FILS is set as the maximal number of iterations 100. Relative Error (RE) defined by (14) is adopted to evaluate the performance.…”

Section: Parameter Determinationmentioning

confidence: 99%

Scheduling Parallel Intrusion Detecting Applications on Hybrid Clouds

Zhang

Sun

et al. 2018

Security and Communication Networks

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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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Novel efficient particle swarm optimization algorithms for solving QoS‐demanded bag‐of‐tasks scheduling problems with profit maximization on hybrid clouds

Zhang

Sun

2017

Concurrency and Computation

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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.

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HySARC2: Hybrid Scheduling Algorithm Based on Resource Clustering in Cloud Environments

Cited by 9 publications

References 11 publications

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

Scheduling Parallel Intrusion Detecting Applications on Hybrid Clouds

Novel efficient particle swarm optimization algorithms for solving QoS‐demanded bag‐of‐tasks scheduling problems with profit maximization on hybrid clouds

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