Interval-valued Fuzzy Logic approach for overloaded hosts in consolidation of virtual machines in cloud computing

Moura, Bruno; Schneider, Guilherme Bayer; Yamin, Adenauer; Santos, Hélida; Reiser, Renata; Bedregal, Benjamín R. C.

doi:10.1016/j.fss.2021.03.001

Cited by 14 publications

(9 citation statements)

References 48 publications

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“…According to the last k days of observed utilization are known, we need to estimate the utilization in k + 1 day. We try to resolve it by using linear regression, as shown in formula (12), where the 𝛽 i (i ∈ {0, 1, … , k}) are the regression coefficient which should be estimated by samples.…”

Section: Cyclic Utilization Predictionmentioning

confidence: 99%

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Loads prediction and consolidation of virtual machines in cloud

Chen

Zhang

et al. 2023

Concurrency and Computation

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SummaryVirtual machine (VM) consolidation is to assign a set of VMs requested by operators to the physical machines (PMs) in the data centers so that certain cost, profit or performance objective is optimized, subject to the PMs resource capacity constraints. It is an important mean to decrease the total power consumption by reducing the number of active physical machines (PM) in a cloud. In this context, most of the existing solutions rely on highly frequent live migration to reduce the number of active physical machines. However, live migration is a high resource consumption and time‐consuming operation, thus, frequent use of live migration not only increases energy consumption but also affects the stability of the physical machine, which in turn affects the services on the virtual machine. Reducing the number of active physical machines while reducing the number of live migrations is a major challenge in the face of massive fluctuating virtual machine loads. In order to solve this problem, in this paper, we present a VM consolidation algorithm for predictable loads (VCPL) to reduce the live migration operations. First, we present a cyclic usage prediction (CUP) method to predict the load in a whole cycle (a day) of a VM. Then, we separate the VMs with stable and cyclic load out from others and consolidate them to PMs by using VCPL to make sure each PM has a stable load. Thus, energy can be reduced by avoiding most of live migration operations, and the stability of the data center can be observably improved. We evaluate our solution through simulations on real‐world workloads, the results show that, 66% of long‐term VMs have stable and cyclic loads and are predictable, by using VCPL, the live migration operations occurring on the PMs which accommodate those VMs can be reduced significantly than other solutions.

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Section: Cyclic Utilization Predictionmentioning

confidence: 99%

“…Many VM consolidation methods are proposed in literature, for example, References 5‐17. The utilizations of the PMs improve significantly by highly frequent live migration operation.…”

Section: Introductionmentioning

confidence: 99%

Loads prediction and consolidation of virtual machines in cloud

Chen

Zhang

et al. 2023

Concurrency and Computation

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“…They build up a model for virtual machines relocation from one physical host to the other with an aim to lower energy consumption. Moura et al [ 37 ] used the internal value fuzzy logic approach to overcome the problems of resources using vagueness and inaccuracies to save energy with the lowest performance deprivation. They increased energy effectiveness by 2.3% in cloud computing simulation environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Cloud Servers: Resource Optimization Using Different Energy Saving Techniques

Hijji

Ahmad

Alam

et al. 2022

Sensors

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Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.

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“…Intervalued fuzzy logic approach has been used by Moura et al [14] in cumulation of virtual machines for overloaded hosts in cloud computing. The approach aims to optimize efficient energy consumption and address uncertainties in determining the usage of resources.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Evaluating the Reusability of Software-As-A-Service Using Fuzzy Logic Approach

Deepika

Sangwan

2022

Preprint

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Cloud computing provides software and hardware resources in the form of cloud services with the help of internet. Cloud Computing is also coming forward as an efficient criterion for reuse. Software-as-a Service is one of the three types of cloud services which provides on demand software services on pay-as-you-go basis. In order to make SaaS more advantageous and to assist end users efficiently, there is a need to build a reusability model for SaaS. Reusability plays a very vital role in developing a product easily and efficiently. In this paper, we have proposed a software reusability estimation model for SaaS. We have been used Fuzzy Logic Methodology for evaluating the SaaS reusability. For this model, we used the SaaS reusability attributes i.e., Commonality, Accessibility, Availability, Customizability and Efficiency. Centroid Method is used for validating the results obtained from the proposed approach. This model evaluates the reusability of SaaS services with the required accuracy level. On the basis of experiment results, it has been proved out that machine learning techniques gives enhanced accuracy of estimation as compared to other reusability estimation techniques, found helpful and more useful for SaaS service providers as well as users.

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Interval-valued Fuzzy Logic approach for overloaded hosts in consolidation of virtual machines in cloud computing

Cited by 14 publications

References 48 publications

Loads prediction and consolidation of virtual machines in cloud

Loads prediction and consolidation of virtual machines in cloud

Cloud Servers: Resource Optimization Using Different Energy Saving Techniques

A Framework for Evaluating the Reusability of Software-As-A-Service Using Fuzzy Logic Approach

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