scholarly journals Efficient VM Selection Strategies in Cloud Datacenter Using Fuzzy Soft Set

2021 ◽  
Vol 33 (5) ◽  
pp. 153-179
Author(s):  
Nithiya Baskaran ◽  
Eswari R.
Keyword(s):  
Power Consumption ◽  
Time Complexity ◽  
Performance Comparison ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Vm Placement ◽  
Soft Sets ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Use Of Resources

A cloud data center is established to meet the storage demand due to the rate of growth of data. The inefficient use of resources causes an enormous amount of power consumption in data centers. In this paper, a fuzzy soft set-based virtual machine (FSS_VM) consolidation algorithm is proposed to address this problem. The algorithm uses four thresholds to detect overloaded hosts and applies fuzzy soft set approach to select appropriate VM for migration. It considers all factors: CPU utilization, memory usage, RAM usage, and correlation values. The algorithm is experimentally tested for 11 different combinations of choice parameters where each combination is considered as fuzzy soft set and compared with existing algorithms for various metrics. The experimental results show that proposed FSS_VM algorithm achieves significant improvement in optimizing the objectives such as power consumption, service level agreement violation rate, and VM migrations compared to all existing algorithms. Moreover, performance comparison among the fuzzy soft set-based VM selection methods are made, and Pareto-optimal fuzzy soft sets are identified. The results show that the Pareto-based VM selection improves the QoS. The time complexity of the proposed algorithm increases when it finds best VM for migration. The future work will reduce the time complexity and will concentrate on developing an efficient VM placement strategy for VM migration since it has the greater impact on improving QoS in VM placement.

Virtual Machine Placement Strategy for Cloud Data Center

2015 ◽  
pp. 261-287
Author(s):  
Sourav Kanti Addya ◽  
Bibhudutta Sahoo ◽  
Ashok Kumar Turuk
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Bin Packing ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Placement Problem ◽  
Cloud Data ◽  
Minimum Power Consumption

The data center is the physical infrastructure layer in cloud architecture. To run a large data center requires a huge amount of power. A proper strategy can minimize the number of servers used. Minimization of active servers caused minimization of power consumption. But the maximum number of virtual machine placement will be a monetary benefit for cloud service providers. To earn maximum revenue, the CSP is to maximize resource utilization. VM placement is one of the major issues to achieve minimum power consumption as well as to earn maximum revenue by CSP. In this research chapter, we have formulated an optimization problem for initial VM placement in the data center. An iterative heuristic using simulated annealing has been used for VM placement problem. The proposed heuristic has been analysis to be scalable and the coding scheme shows that the proposed technique is outperforming traditional FFD on bin packing technique.

Virtual Machine Placement Strategy for Cloud Data Center

2016 ◽  
pp. 783-808
Author(s):  
Sourav Kanti Addya ◽  
Bibhudatta Sahoo ◽  
Ashok Kumar Turuk
Keyword(s):  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Bin Packing ◽  
Virtual Machine Placement ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Placement Problem ◽  
Cloud Data ◽  
Minimum Power Consumption

The data center is the physical infrastructure layer in cloud architecture. To run a large data center requires a huge amount of power. A proper strategy can minimize the number of servers used. Minimization of active servers caused minimization of power consumption. But the maximum number of virtual machine placement will be a monetary benefit for cloud service providers. To earn maximum revenue, the CSP is to maximize resource utilization. VM placement is one of the major issues to achieve minimum power consumption as well as to earn maximum revenue by CSP. In this research chapter, we have formulated an optimization problem for initial VM placement in the data center. An iterative heuristic using simulated annealing has been used for VM placement problem. The proposed heuristic has been analysis to be scalable and the coding scheme shows that the proposed technique is outperforming traditional FFD on bin packing technique.

scholarly journals Generalised Interval-Valued Fuzzy Soft Set

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Shawkat Alkhazaleh ◽  
Abdul Razak Salleh
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Decision Making Problem ◽  
Fuzzy Soft Sets ◽  
Interval Valued

We introduce the concept of generalised interval-valued fuzzy soft set and its operations and study some of their properties. We give applications of this theory in solving a decision making problem. We also introduce a similarity measure of two generalised interval-valued fuzzy soft sets and discuss its application in a medical diagnosis problem: fuzzy set; soft set; fuzzy soft set; generalised fuzzy soft set; generalised interval-valued fuzzy soft set; interval-valued fuzzy set; interval-valued fuzzy soft set.

A Game Theoretic Approach to Estimate Fair Cost of VM Placement in Cloud Data Center

2018 ◽  
Vol 12 (4) ◽  
pp. 3509-3518
Author(s):  
Sourav Kanti Addya ◽  
Ashok Kumar Turuk ◽  
Bibhudatta Sahoo ◽  
Anurag Satpathy ◽  
Mahasweta Sarkar
Keyword(s):  
Data Center ◽  
Theoretic Approach ◽  
Vm Placement ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Game Theoretic ◽  
Game Theoretic Approach

scholarly journals An algorithm for fuzzy soft set based decision making approach

2020 ◽  
Vol 30 (1) ◽  
pp. 59-70
Author(s):  
Shehu Mohammed ◽  
Akbar Azam
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Group Decision Making ◽  
Group Decision ◽  
Soft Set ◽  
Mathematical Tool ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Fuzzy Soft Sets ◽  
Priority Table

The notion of soft set theory was initiated as a general mathematical tool for handling ambiguities. Decision making is viewed as a cognitive-based human activity for selecting the best alternative. In the present time, decision making techniques based on fuzzy soft sets have gained enormous attentions. On this development, this paper proposes a new algorithm for decision making in fuzzy soft set environment by hybridizing some existing techniques. The first novelty is the idea of absolute scores. The second concerns the concept of priority table in group decision making problems. The advantages of our approach herein are stronger power of objects discrimination and a well-determined inference.

scholarly journals On Fuzzy Soft Sets

2009 ◽  
Vol 2009 ◽  
pp. 1-6 ◽  
Author(s):  
B. Ahmad ◽  
Athar Kharal
Keyword(s):  
Set Theory ◽  
Soft Set ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
Fuzzy Soft Sets

We further contribute to the properties of fuzzy soft sets as defined and studied in the work of Maji et al. ( 2001), Roy and Maji (2007), and Yang et al. (2007) and support them with examples and counterexamples. We improve Proposition 3.3 by Maji et al., (2001). Finally we define arbitrary fuzzy soft union and fuzzy soft intersection and prove DeMorgan Inclusions and DeMorgan Laws in Fuzzy Soft Set Theory.

Minimization of VM Placement Change in Energy-Aware Resource Provisioning for Cloud Data Center

2013 ◽  
Vol 325-326 ◽  
pp. 1730-1733 ◽  
Author(s):  
Si Yuan Jing ◽  
Shahzad Ali ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Network Flow ◽  
Resource Provisioning ◽  
Approximate Algorithm ◽  
Vm Placement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Placement Change

Numerous part of the energy-aware resource provision research for cloud data center just considers how to maximize the resource utilization, i.e. minimize the required servers, without considering the overhead of a virtual machine (abbreviated as a VM) placement change. In this work, we propose a new method to minimize the energy consumption and VM placement change at the same time, moreover we also design a network-flow-theory based approximate algorithm to solve it. The simulation results show that, compared to existing work, the proposed method can slightly decrease the energy consumption but greatly decrease the number of VM placement change

An Interval Valued Fuzzy Soft Set Based Optimization Algorithm for High Yielding Seed Selection

2018 ◽  
Vol 7 (2) ◽  
pp. 44-61 ◽  
Author(s):  
T. R. Sooraj ◽  
B. K. Tripathy
Keyword(s):  
Decision Making ◽  
Experimental Verification ◽  
Hybrid Models ◽  
Soft Set ◽  
Seed Selection ◽  
Fuzzy Soft Set ◽  
Soft Sets ◽  
New Varieties ◽  
Interval Valued ◽  
Selection Of

As seed selection is a challenging task due to the presence of hundreds of varieties of seeds of each kind, some homework is necessary for selecting suitable seeds as new varieties and kinds of seeds are introduced in the market every year having their own strengths and weaknesses. The complexities involved in the characteristics in the form of parameters results in uncertainties and as a result some uncertainty based model or hybrid models of more than is required to model the scenario and come out with a decision. Soft sets have enough of parameterization tools to support and hence is the most suitable one for such a study. However, as hybrid models are more efficient, the authors select a model called the interval valued fuzzy soft set (IVFSS) and propose a decision-making algorithm for the selection of seeds. A real database of seeds is used for experimental verification of the efficiency of the algorithm. This is the first attempt for such a study. The use of signed priorities and intervals for the membership of values for entities makes the study more efficient and realistic.

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