Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results.

A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments

A comprehensive survey for scheduling techniques in cloud computing

The personnel selection is a vital activity for companies, and multi-valued neutrosophic sets (MVNSs) can denote the fuzziness and hesitancy in the processes of the personnel selection. The extant fuzzy TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) methods take advantage of distance to denote the difference between two fuzzy sets (FSs). Nevertheless, the distance measurement, which ignores the included angle between two FSs, cannot comprehensively reflect the difference between two FSs. To cover this defect, a projection-based TODIM method with MVNSs for personnel selection is established to consider the risk preference of decision-makers and overcome the defect of the extant fuzzy TODIM methods. The proposed TODIM method makes use of an improved comparison method which overcomes the deficiency of extant comparison method. Furthermore, a projection-based difference measurement is defined and utilized in the projection-based TODIM method. We conduct a numerical example of the personnel selection to explain the application of the projection-based TODIM method and discuss the influence of the parameter. Finally, the proposed method is compared with several extant methods to verify its feasibility.

A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection

The presence of code and design smells can have a severe impact on the quality of a program. Consequently, their detection and correction have drawn the attention of both researchers and practitioners who have proposed various approaches to detect code and design smells in programs. However, none of these approaches handle the inherent uncertainty of the detection process. We propose a Bayesian approach to manage this uncertainty. First, we present a systematic process to convert existing state-of-the-art detection rules into a probabilistic model. We illustrate this process by generating a model to detect occurrences of the Blob antipattern. Second, we present results of the validation of the model: we built this model on two open-source programs, GanttProject v1.10.2 and Xerces v2.7.0, and measured its accuracy. Third, we compare our model with another approach to show that it returns the same candidate classes while ordering them to minimise the quality analysts' effort. Finally, we show that when past detection results are available, our model can be calibrated using machine learning techniques to offer an improved, context-specific detection.

A GQM-based Method and a Bayesian Approach for the Detection of Code and Design Smells

A novel fuzzy TOPSIS method using emerging interval-valued spherical fuzzy sets

Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms

The problem of high energy consumption is becoming more and more serious due to the construction of large-scale cloud data centers. In order to reduce the energy consumption and SLA violation, a new virtual machine VM placement algorithm named ATEA adaptive three-threshold energy-aware algorithm, which takes good use of the historical data from resource usage by VMs, is presented. In ATEA, according to the load handled, data center hosts are divided into four classes: hosts with little load, hosts with light load, hosts with moderate load, and hosts with heavy load. ATEA migrates VMs on heavily loaded or little-loaded hosts to lightly loaded hosts, while the VMs on lightly loaded and moderately loaded hosts remain unchanged. Then, on the basis of ATEA, two kinds of adaptive three-threshold algorithm and three kinds of VMs selection policies are proposed. Finally, we verify the effectiveness of the proposed algorithms by CloudSim toolkit utilizing real-world workload. The experimental results show that the proposed algorithms efficiently reduce energy consumption and SLA violation.

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Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

Toward trustworthy cloud service selection

In order to improve the energy efficiency of large-scale data centers, a virtual machine (VM) deployment algorithm called three-threshold energy saving algorithm (TESA), which is based on the linear relation between the energy consumption and (processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is, host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies (minimization of migrations policy based on TESA (MIMT), maximization of migrations policy based on TESA (MAMT), highest potential growth policy based on TESA (HPGT), lowest potential growth policy based on TESA (LPGT) and random choice policy based on TESA (RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold (ST) algorithm and minimization of migrations (MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

A novel virtual machine deployment algorithm with energy efficiency in cloud computing

The imprecise quality of service (QoS) evaluations from consumers may lead to the inappropriate prediction for the trustworthiness of cloud services in an uncertain cloud environment. The service ranking prediction is a promising idea to overcome this deficiency of values prediction approaches by probing the ordering relations between cloud services concealed in the imprecise evaluations. To address the challenges for trustworthy service selection resulting from fluctuating QoS, flexible service pricing and complicated potential risks, this paper proposes a time-aware approach to predict the trustworthiness ranking of cloud services, with the tradeoffs between performance-cost and potential risks in multiple periods. In this approach, the interval neutrosophic set (INS) theory is utilized to describe and assess the performance-costs and potential risks of cloud services: (1) the original evaluation data about cloud services are preprocessed into the trustworthiness interval neutrosophic numbers (INNs); (2) the new INS operators are proposed with the theoretical proofs to calculate the possibility degree and the ranking values of trustworthiness INNs, contributing to the identification of the neighboring users based on the Kendall rank correlation coefficient (KRCC). The problem of time-aware trustworthiness ranking prediction is formulated as a multi-criterion decision-making (MCDM) problem of creating a ranked services list using INS, and an improved ELECTRE method is developed to solve it. The proposed approach is verified by experiments based on an appropriate baseline for comparative analysis. The experimental results demonstrate that the proposed approach can achieve a better prediction quality than the existing approach. The results also show that our approach works effectively in the risk-sensitive and performance-cost-sensitive application scenarios and prevent the malignant price competition launched by low-quality services.

Zhigang Hu

Papers

Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms

Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers

Toward trustworthy cloud service selection

A novel virtual machine deployment algorithm with energy efficiency in cloud computing

Time-aware trustworthiness ranking prediction for cloud services using interval neutrosophic set and ELECTRE