An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing

Rafieyan, Elham; Khorsand, Reihaneh; Ramezanpour, Mohammadreza

doi:10.1016/j.cie.2020.106272

Cited by 61 publications

(30 citation statements)

References 33 publications

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“…Since the Gini coefficient can reflects the data difference between different evaluation objects, Li et al [31] proposed a method to calculate the weights of objectives based on the Gini coefficient: Suppose that G k denotes the Gini coefficient of the k-th criterion (k = 1, 2, · · · , n), m denotes the total data of a specific criterion, y ki denotes the i-th alternative' performance value under the k-th criterion, E k denotes the expectation value of all alternatives' performance values under the k-th criterion. Then the Gini coefficient value G k can be calculated by Equations (8) and (9) [31].…”

Section: The Gini-coefficient-based Weighting Methodsmentioning

confidence: 99%

“…In particular, when the mean value of all alternatives' performance values under a specific criterion that is not equal to 0, the Gini coefficient of the criterion is calculated by Equation (8); otherwise, the Gini coefficient of the criterion is calculated by Equation (9).…”

Section: The Gini-coefficient-based Weighting Methodsmentioning

confidence: 99%

“…a 1 (5,7,9) (0.7,0.9,1) (5,7,9) (0.7,0.9,1) (1,3,5) (0.3,0.5,0.7) a 2 (1,3,5) (0.9,1,1) (5,7,9) (0.7,0.9,1) (7,9,10) (0.1,0.3,0.5) a 3 (0,1,3) (0.3,0.5,0.7) (7,9,10) (0.7,0.9,1) (7,9,10) (0.3,0.5,0.7) a 4 (9,10,10) (0.5,0.7,0.9) (5,7,9) (0.5,0.7,0.9) (1,3,5) (0.7,0.9,1) DM2 a 1 (3,5,7) (0.7,0.9,1) (7,9,10) (0.9,1,1) (1,3,5) (0.3,0.5,0.7) a 2 (1,3,5) (0.7,0.9,1) (7,9,10) (0.7,0.9,1) (7,9,10) (0.3,0.5,0.7) a 3 (1,3,5) (0.3,0.5,0.7) (5,7,9) (0.7,0.9,1) (7,9,10) (0.5,0.7,0.9) a 4 (9,10,10) (0.5,0.7,0.9) (5,7,9) (0.5,0.7,0.9) (1,3,5) (0.7,0.9,1) DM3 a 1 (5,7,9) (0.7,0.9,1) (7,9,10) (0.9,1,1) (3,5,7) (0.1,0.3,0.5) a 2 (1,3,5) (0.7,0.9,1) (5,7,9) (0.7,0.9,1) (7,9,10) (0.5,0.7,0.9) a 3 (0,1,3) (0.3,0.5,0.7) (7,9,10) (0.7,0.9,1) (7,9,10) (0.5,0.7,0.9) a 4 (9,10,10) (0.3,0.5,0.7) (3,5,7) (0.7,0.9,1) (1,3,5) (0.7,0.9,1) ((1, 3, 5), (0.767, 0.933, 1)) ((5.667, 7.667, 9.333), (0.7, 0.9, 1)) ((7, 9, 10), (0.3, 0.5, 0.7)) a 3 ((0.333, 1.667, 3.667), (0.3, 0.5, 0.7)) ((6.333, 8.333, 9.667), (0.7, 0.9, 1)) ((7, 9, 10), (0.433, 0.633, 0.833)) a 4 ((9, 10, 10), (0.433, 0.633, 0.833)) ((4.333, 6.333, 8.333), (0.567, 0.767, 0.933)) ((1, 3, 5), (0.7, 0.9, 1))…”

Section: Dm1mentioning

confidence: 99%

“…Moreover, the CSP evaluation data often involves uncertain, unreliability, multi-scale and imprecise weights of quality of service (QoS) parameters, such as the availability, response time, and price [7]. The existing MCDM methods for CSP selection problems can be divided into three categories: the utility value-based methods such as the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) [8], VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [9,10], and Multiplicative Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) [11]; outranking methods such as the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) [12] and ELimination and Choice Expressing the Reality (ELECTRE) [13]; and preference ordering methods such as the Analytic Hierarchical Process (AHP) [14] and the Best Worst Method (BWM) [9,15,16]. The utility value-based methods are easy to understand with a ranking set as its output, and as a result are widely applied in practice [17].…”

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confidence: 99%

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Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

et al. 2020

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The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.Sustainability 2020, 12, 3410 2 of 17 future generations to meet their own needs" [2]. Sustainability requires sustainable business practices. For doing so, information systems play an important role in the organization transition towards sustainability initiatives. The sustainable information systems have been considered as an opportunity for organizations to improve productivity, reduce costs, and increase profitability [3]. The flexible, elastic and agile nature of cloud computing provides an opportunity for governmental, academic and business organizations to consider migrating their existing applications to cloud environments to make their business processes agile with minimal cost and management effort [4]. Due to the increase in the number of cloud service providers (CSPs) offering functionally similar cloud services with varying cost, features and quality, it becomes extremely complex and burdensome to select a dependable, scalable, sustainable and high cost-effective cloud service for consumers to fulfil and satisfy their requirements and business strategies. There is a pressing need of comprehensive techniques to help them for the sustainable CSP development [5,6].Sustainable CSP development can be formulated as a multicriteria decision-making (MCDM) problem [5]. It involves the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and experts for selecting the trustworthy CSPs. Moreover, the CSP evaluation data often involves uncertain, unreliability, multi-scale and imprecise weights of quality of service (QoS)...

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Section: The Gini-coefficient-based Weighting Methodsmentioning

confidence: 99%

Section: The Gini-coefficient-based Weighting Methodsmentioning

confidence: 99%

Section: Dm1mentioning

confidence: 99%

mentioning

confidence: 99%

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Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

et al. 2020

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“…Statistical analyzes on the different randomly generated task graphs are applied to estimate the verification results. Rafieyan et al 23 studied the problem of cloud scheduling based on a combination of the concept of the multi‐criteria decision‐making method and compromise ranking method (VIKOR) in virtualized heterogeneous systems, while considering three levels of priorities, including scheduling level, resources level, and job level. The authors have investigated the effect of priority of each job in compared with other jobs separately using comparison matrixes of jobs.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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Adam‐Ladybug Beetle Optimization enabled multi‐objective service placement strategy in fog computing

Sharma,

Sharma

2024

Concurrency and Computation

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SummaryThe Internet of Things (IoT) has transformed every aspect of our lives and has become universal in multiple fields from personnel to government and military applications. However, IoT suffers from the inherent limitation of latency and high computational costs, which can be effectively overcome by using a fog computing framework. However, the key challenge in fog computing is to address the problem of service placement among the nodes, thereby providing optimal utilization of resources and minimizing service time. This research work presents a novel service placement technique, by considering the service placement issue as a multi‐objective optimization problem. Here, a two‐level fog computing network comprising a fog master node and fog cells is considered. The master node is responsible for the service placement of the fog nodes, and the service placement is carried out using the Adam‐Ladybug Beetle Optimization (ALBO) algorithm. Further, multiple objectives, like resource utilization, makespan, response time, service time, cost, and energy consumption are considered to enhance service placement. Moreover, the efficiency of the ALBO for service placement (ALBO_SP) is examined considering service cost, energy consumption, and service time and is found to attain values of 19.009, 73.581 J, and 4.854 s, respectively.

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An adaptive scheduling approach based on integrated best-worst and VIKOR for cloud computing

Cited by 61 publications

References 33 publications

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Adam‐Ladybug Beetle Optimization enabled multi‐objective service placement strategy in fog computing

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