Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment

Agarwal, Mohit; Srivastava, Gur Mauj Saran

doi:10.1142/s0219622018500244

Cited by 30 publications

(9 citation statements)

References 50 publications

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“…Each search compares the optimal solution with the historical record. If the historical optimal value is exceeded, then the historical optimal position and optimal solution are updated [21][22][23]. Dong L (2018) proposed a new particle swarm optimization algorithm (co-pso) to extract nonlinear IP information from MT (magnetotelluric) sounding data [24].…”

Section: Polarization Parameter Extraction Based On Particle Swarm Opmentioning

confidence: 99%

“…The burial depth was h = 200 m below the ground. The polarization parameters of basalt were given by Reference [23] as follows: σ ∞ = 0.01 S/m, c = 0.82, η = 0.28, and τ = 0.00005 s. The polarization parameters of graphite ore were given by Reference [22] as follows: σ ∞ = 0.01 S/m, c = 0.82, η = 0.28, and τ = 0.00005 s. To show that the skin depth for polarized porous media is universal, we added 50 Hz power frequency noise to the response and extracted the polarization parameters in the case of power frequency noise, as shown in Table 3. The traditional skin depth formula and generalized skin depth formula were used to image the graphite ore, as shown in Figure 19.…”

Section: Depth Interpretation Of a Three-dimensional Chargeable Bodymentioning

confidence: 99%

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The Generalized Skin Depth for Polarized Porous Media Based on the Cole–Cole Model

Meng

Shao

et al. 2020

Applied Sciences

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In the field of frequency-domain electromagnetic detection, skin depth is an important parameter for electromagnetic data interpretation and imaging. The classic skin depth formula is calculated based only on conductivity; the induced-polarization effect in real earth is not considered, so the imaging results have obvious errors. To solve these problems, based on plane wave theory and the Cole–Cole conductivity model, a generalized skin depth formula of polarized media is derived in the frequency domain. The accuracy of the generalized skin depth is verified through comparison with the classical skin depth. To show the practicability of this study, the theoretical data with induced polarization (IP) effects are used to explain the generalized skin depth for polarized porous media. The generalized skin depth calculation for a typical porous polarization model is related not only to conductivity, but also to polarization parameters, such as chargeability, characteristic time constant, and frequency dependence. At low-frequency excitation, the generalized skin depth formula can be used to calculate the propagation depth of electromagnetic waves relatively accurately for porous polarized media. This method can be applied to the calculation of electromagnetic wave propagation depths in complex dispersive media. Compared with non-polarized media, in porous polarized media, under low-frequency excitation, the electromagnetic wave propagates deeper, allowing the detection of deeper objects. The data interpretation and imaging of polarized porous media by the generalized skin depth formula have higher accuracy.

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Section: Polarization Parameter Extraction Based On Particle Swarm Opmentioning

confidence: 99%

Section: Depth Interpretation Of a Three-dimensional Chargeable Bodymentioning

confidence: 99%

The Generalized Skin Depth for Polarized Porous Media Based on the Cole–Cole Model

Meng

Shao

et al. 2020

Applied Sciences

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“…Hu et al [7] devised a scientific workflow multi-objective scheduling algorithm for the reliability of workflow scheduling in a multi-cloud environment, with a goal to minimize the completion time and cost of workflow under reliability constraints. A number of hybrid algorithms [8,9,10] combine the excellent characteristics of multiple heuristic algorithms to achieve good performance.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation

et al. 2020

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With the development of artificial intelligence and the Internet of things, the prospects of cloud computing applications have become broader, and the number of users and cloud data centers (CDCs) has exploded. It is a challenge to realize efficient job scheduling and resource allocation of multiple users and data centers. However, the traditional scheduling model based on heuristic algorithm has some limitations in the complex and changeable cloud environment. In addition, many existing single-agent models rarely consider the multi-objective global optimization of the system. Therefore, this paper proposes a two-stage job scheduling and resource allocation framework that adopts multiple intelligent schedulers to solve the cooperative scheduling problem between job scheduling and resource allocation. A heterogeneous distributed deep learning (HDDL) model is used in the job scheduling stage to schedule multiple jobs to multiple cloud data centers. The deep Q-network (DQN) model is a resource scheduler to deploy virtual machine to physical servers for execution. Extensive numerical results show that both HDDL-baesd job scheduler and DQN-based resource allocator outperform the benchmark algorithm in terms of optimizing energy consumption and job delay. Furthermore, the proposed framework not only can achieve a global near-optimum by achieving local optimization at each stage but also has good scalability and low computation delay.

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“…Thus, traditional algorithms like SPG2 are inappropriate for this case. The particle swarm optimization (PSO) and genetic algorithm (GA) hybrid algorithm (PSO-GA) (Kumar and Vidyarthi, 2016;Agarwal and Srivastava, 2018), which is combined with the principal component analysis (PCA) strategy (PGAPSO), is selected to identify OPRs of the NAO. NAOI is selected as the objective function (North Atlantic sector), while the perturbations are superimposed on the Arctic region.…”

Section: Introductionmentioning

confidence: 99%

Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method

Yuan

et al. 2020

Preprint

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Abstract. The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in the North Hemisphere. It has a profound influence on the westerly wind strength and storm tracks in North Atlantic, which further affect the winter climate in Northern Hemisphere. Therefore, the identification for optimal precursor (OPR) of the NAO event is of research value and practical significance. In this paper, the conditional nonlinear optimal perturbation (CNOP) method, which has been widely used in research on the OPR of climatic event, is adopted to explore which kind of initial perturbation is most likely to trigger the NAO anomaly pattern in the Community Earth System Model (CESM). Since the adjoint model of CESM has yet to be developed, this kind of problem cannot be solved using traditional strategies based on gradient information provided by the adjoint model. We utilize an adjoint-free algorithm to solve CNOP in such a high dimensional numerical model, and OPRs of the NAO can be successfully identified. The results reveal that OPRs obtained by CNOP can cause the basic state to develop into typical dipole mode, and the nonlinear process plays an important role in the last stage of the prediction period. The algorithm adopted in this work can avoid falling into a local optimum and is accelerated with multiple parallel frameworks to enhance performance. The solution scheme can also be generalized to the OPR research of other climate events or other complex numerical models.

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Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment

Cited by 30 publications

References 50 publications

The Generalized Skin Depth for Polarized Porous Media Based on the Cole–Cole Model

The Generalized Skin Depth for Polarized Porous Media Based on the Cole–Cole Model

A Two-Stage Framework for the Multi-User Multi-Data Center Job Scheduling and Resource Allocation

Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method

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