A hybrid auto-scaling technique for clouds processing applications with service level agreements

Biswas, Anshuman; Majumdar, Shikharesh; Nandy, Biswajit; El-Haraki, Ali

doi:10.1186/s13677-017-0100-5

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…During periods of high demand, the cloud can rapidly provision additional resources, and during low-demand periods, it can release unneeded resources. Biswas et al [42] state that dynamic scaling ensures efficient resource utilization and costeffectiveness. Cloud computing and machine learning are two powerful technologies that can be effectively combined to leverage their respective strengths.…”

Section: Information Securitymentioning

confidence: 99%

Prospects of Cybersecurity in Smart Cities

Almeida

2023

Future Internet

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The complex and interconnected infrastructure of smart cities offers several opportunities for attackers to exploit vulnerabilities and carry out cyberattacks that can have serious consequences for the functioning of cities’ critical infrastructures. This study aims to address this phenomenon and characterize the dimensions of security risks in smart cities and present mitigation proposals to address these risks. The study adopts a qualitative methodology through the identification of 62 European research projects in the field of cybersecurity in smart cities, which are underway during the period from 2022 to 2027. Compared to previous studies, this work provides a comprehensive view of security risks from the perspective of multiple universities, research centers, and companies participating in European projects. The findings of this study offer relevant scientific contributions by identifying 7 dimensions and 31 sub-dimensions of cybersecurity risks in smart cities and proposing 24 mitigation strategies to face these security challenges. Furthermore, this study explores emerging cybersecurity issues to which smart cities are exposed by the increasing proliferation of new technologies and standards.

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Section: Information Securitymentioning

confidence: 99%

Prospects of Cybersecurity in Smart Cities

Almeida

2023

Future Internet

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“…Te objective is to estimate the needed resources for horizontal scaling depending on the incoming workloads. Moreover, [51] presents a machine learning (ML)-based proactive algorithm combined with a reactive algorithm for scaling resources according to user's demands. Te strategy, based on a price model, aims at both maximizing broker's proft and minimizing the user's costs.…”

Section: Web Applicationsmentioning

confidence: 99%

“…Besides, MLscale can accurately model the response time while minimizing the cost of resources for web applications. All the approaches presented in [45,[49][50][51][52] were focused on Web applications, where the requirements of individual tasks are much lighter than the PSEs considered in our article.…”

Section: Web Applicationsmentioning

confidence: 99%

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

Yannibelli

Pacini

Monge

et al. 2023

Scientific Programming

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Many important computational applications in science, engineering, industry, and technology are represented by PSE (parameter sweep experiment) applications. These applications involve a large number of resource-intensive and independent computational tasks. Because of this, cloud autoscaling approaches have been proposed to execute PSE applications on public cloud environments that offer instances of different VM (virtual machine) types, under a pay-per-use scheme, to execute diverse applications. One of the most recent approaches is the autoscaler MOEA (multiobjective evolutive algorithm), which is based on the multiobjective evolutionary algorithm NSGA-II (nondominated sorting genetic algorithm II). MOEA considers on-demand and spot VM instances and three optimization objectives relevant for users: minimizing the computing time, monetary cost, and spot instance interruptions of the application’s execution. However, MOEA’s performance regarding these optimization objectives depends significantly on the optimization algorithm used. It has been shown recently that MOEA’s performance improves considerably when NSGA-II is replaced by a more recent algorithm named NSGA-III. In this paper, we analyze the incorporation of other multiobjective optimization algorithms into MOEA to enhance the performance of this autoscaler. First, we consider three multiobjective optimization algorithms named E-NSGA-III (extreme NSGA-III), SMS-EMOA (S-metric selection evolutionary multiobjective optimization algorithm), and SMPSO (speed-constrained multiobjective particle swarm optimization), which have behavioral differences with NSGA-III. Then, we evaluate the performance of MOEA with each of these algorithms, considering the three optimization objectives, on four real-world PSE applications from the meteorology and molecular dynamics areas, considering different application sizes. To do that, we use the well-known CloudSim simulator and consider different VM types available in Amazon EC2. Finally, we analyze the obtained performance results, which show that MOEA with E-NSGA-III arises as the best alternative, reaching better and significant savings in terms of computing time (10%–17%), monetary cost (10%–40%), and spot instance interruptions (33%–100%).

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“…This is the solution that can be implemented by cloud providers only. Also related to low-level measurements in [2], the authors propose a hybrid method between reactive and proactive applied to an intermediary enterprise managing a virtual private cloud that provides resources to subscribers. The authors also mentioned the necessity of selecting the metric and determining the exact thresholds for auto scaling from which to provide the GoS quantity and the standard for auto scaling based on GoS.…”

Section: Related Workmentioning

confidence: 99%

The Effect of the Resource Consumption Characteristics of Cloud Applications on the Efficiency of Low-Metric Auto Scaling Solutions

Cu¹,

Sơn²

2018

IJCCSA

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Auto scaling is a service provided by the cloud service provider that allows provision of temporary resources to the subscriber's systems to prevent overloading. So far, many methods of auto scaling have been proposed and applied. Among them, solutions based on low-level metrics are commonly used in industry systems. Resource statistics are the basis for detecting overloading situation and making additional resources in a timely manner. However, the effectiveness of these methods depends very much on the accuracy of the overload calculation from low-level metrics. Overloading is mentioned in solutions that usually favor a shortage of CPU resources. However, the demand for resources comes from the application running on that each application has the characteristics of demanding different resource types, with different CPU, memory, I/O ratios so it can not just be statistically on CPU consumption. The point of view here is that even though based on low level resources, the source for calculation and forecasting is the characteristic of the resource needs of the application. In this paper, we will develop an empirical model to assess the effect of the application's resource consumption characteristics on the efficiency of the lowmetricauto scaling solutions and propose an auto scaling solution that is calculated based on statistics of different types of resources. The results of the simulations show that the proposed solution based on multiple resources is more positive.

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A hybrid auto-scaling technique for clouds processing applications with service level agreements

Cited by 14 publications

References 29 publications

Prospects of Cybersecurity in Smart Cities

Prospects of Cybersecurity in Smart Cities

An In-depth Benchmarking of Evolutionary and Swarm Intelligence Algorithms for Autoscaling Parameter Sweep Applications on Public Clouds

The Effect of the Resource Consumption Characteristics of Cloud Applications on the Efficiency of Low-Metric Auto Scaling Solutions

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