scholarly journals Severity: a QoS-aware approach to cloud application elasticity

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Andreas Tsagkaropoulos ◽  
Yiannis Verginadis ◽  
Nikos Papageorgiou ◽  
Fotis Paraskevopoulos ◽  
Dimitris Apostolou ◽  
...  
Keyword(s):  
Source Code ◽  
Dynamic Adaptation ◽  
Application Performance ◽  
Algorithmic Approach ◽  
Cloud Application ◽  
Application Hosting ◽  
Application Adaptation ◽  
Adaptation Action ◽  
Flexible Application ◽  
Cloud Applications

AbstractWhile a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The feasibility of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

Severity: A QoS-aware approach to cloud application elasticity

2021 ◽  
Author(s):  
Andreas Tsagkaropoulos ◽  
Yiannis Verginadis ◽  
Nikos Papageorgiou ◽  
Fotis Paraskevopoulos ◽  
Dimitris Apostolou ◽  
...  
Keyword(s):  
Source Code ◽  
Dynamic Adaptation ◽  
Application Performance ◽  
Algorithmic Approach ◽  
Cloud Application ◽  
Application Hosting ◽  
Application Adaptation ◽  
Adaptation Action ◽  
Flexible Application ◽  
Cloud Applications

Abstract While a multitude of cloud vendors exist today offering flexible application hosting services, the application adaptation capabilities provided in terms of autoscaling are rather limited. In most cases, a static adaptation action is used having a fixed scaling response. In the cases that a dynamic adaptation action is provided, this is based on a single scaling variable. We propose Severity, a novel algorithmic approach aiding the adaptation of cloud applications. Based on the input of the DevOps, our approach detects situations, calculates their Severity and proposes adaptations which can lead to better application performance. Severity can be calculated for any number of application QoS attributes and any type of such attributes, whether bounded or unbounded. Evaluation with four distinct workload types and a variety of monitoring attributes shows that QoS for particular application categories is improved. The efficacy of our approach is demonstrated with a prototype implementation of an application adaptation manager, for which the source code is provided.

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1590
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Performance Monitoring ◽  
Nonlinear Problems ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

scholarly journals Measuring Security for Applications Hosted in Cloud

2019 ◽  
Vol 8 (10) ◽  
pp. 2957-2963
Keyword(s):  
Cloud Service ◽  
Cloud Adoption ◽  
Security Controls ◽  
Detailed Method ◽  
Cloud Application ◽  
Security Control ◽  
Security Levels ◽  
Cloud Applications ◽  
Holding Back ◽  
Control Implementation

Despite the numerous benefits of cloud computing, concerns around security, trust and privacy are holding back the cloud adoption. Lack of visibility and tangible measurement of the security posture of any cloud hosted application is a disadvantage to cloud service customers. Decision to migrate workloads on the Cloud requires thoughtful analysis about security implications and ability to measure the security controls after hosting. In this paper, we propose a framework to quantitatively measure different aspects of information security for Cloud applications. This framework has a system through which we can define applications specific controls, gather information on control implementation, calculate the security levels for applications and present them to stakeholders through dashboards. Framework also includes detailed method to quantify the security of a Cloud application considering different aspects of security, control criticalities, stakeholder responsibilities and cloud service models. System and method provide visibility to Cloud customer on the security posture of their cloud hosted applications.

scholarly journals Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

2021 ◽  
Author(s):  
Ivana Stupar ◽  
Darko Huljenić
Keyword(s):  
Quality Of Service ◽  
Cloud Service ◽  
Operational Cost ◽  
Cloud Infrastructure ◽  
Application Service ◽  
Cloud Application ◽  
Wide Range ◽  
Service Deployment ◽  
Cloud Applications

Abstract Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

scholarly journals Utilization of information technology with the zoom cloud application at the Farabi Bali Music Education Institute

2020 ◽  
Vol 6 (Extra-C) ◽  
pp. 114-127
Author(s):  
Ni Luh Gede Dian Aprista Dewi ◽  
Anak Agung Putu Agung ◽  
I Wayan Sujana
Keyword(s):  
Information Technology ◽  
Music Education ◽  
Student Satisfaction ◽  
Test Data ◽  
Performance Indicators ◽  
Corporate Image ◽  
Purposeful Sampling ◽  
Cloud Application ◽  
Research Population ◽  
Cloud Applications

This study aims to test data to analyze the effect of using information technology with zoom cloud applications, corporate image, student satisfaction and loyalty. This research was conducted with the research population composed of all students at Farabi Bali Music Education Institute totalizing 1,338 students, the sample used was 93 students with the method of purposeful sampling, that is, the method of determining the sample according to predetermined characteristics. The implication of this research is that the use of information technology with zoom cloud applications can be improved, paying attention to the performance indicators of the work in progress, so that the use of information technology with zoom cloud applications can increase student satisfaction and loyalty. A company's image can be enhanced with a set of impressions. Student satisfaction can be increased by general satisfaction and loyalty can be increased by demonstrating immunity to competitors' attractiveness.    

Algorithm for Secure Hybrid Cloud Design Against DDoS Attacks

2018 ◽  
Vol 13 (4) ◽  
pp. 61-77 ◽  
Author(s):  
Akashdeep Bhardwaj ◽  
Sam Goundar
Keyword(s):  
Service Industry ◽  
Denial Of Service ◽  
Hybrid Cloud ◽  
Ddos Attacks ◽  
Cloud Infrastructure ◽  
Dos Attacks ◽  
Cloud Application ◽  
Government Education ◽  
Cloud Environments ◽  
Cloud Applications

This article describes how cloud computing has become a significant IT infrastructure in business, government, education, research, and service industry domains. Security of cloud-based applications, especially for those applications with constant inbound and outbound user traffic is important. It becomes of the utmost importance to secure the data flowing between the cloud application and user systems against cyber criminals who launch Denial of Service (DoS) attacks. Existing research related to cloud security focuses on securing the flow of information on servers or between networks but there is a lack of research to mitigate Distributed Denial of Service attacks on cloud environments as presented by Buyya et al. and Fachkha, et al. In this article, the authors propose an algorithm and a Hybrid Cloud-based Secure Architecture to mitigate DDoS attacks. By proposing a three-tier cloud infrastructure with a two-tier defense system for separate Network and Application layers, the authors show that DDoS attacks can be detected and blocked before reaching the infrastructure hosting the Cloud applications.

scholarly journals A Multi-Objective Differential Evolutionary Optimization Method for Performance Optimization of Cloud Application

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Performance Optimization ◽  
Search Space ◽  
Optimization Method ◽  
Performance Ratio ◽  
Cost Performance ◽  
Cloud Resource ◽  
Cloud Application ◽  
Problem Instances ◽  
Evolutionary Operators ◽  
Cloud Applications

Due to the limited search space in the existing performance optimization ap-proaches at software architectures of cloud applications (SAoCA) level, it is difficult for these methods to obtain the cloud resource usage scheme with optimal cost-performance ratio. Aiming at this problem, this paper firstly de-fines a performance optimization model called CAPOM that can enlarge the search space effectively. Secondly, an efficient differential evolutionary op-timization algorithm named MODE4CA is proposed to solve the CAPOM model by defining evolutionary operators with strategy pool and repair mechanism. Further, a method for optimizing performance at SAoCA level, called POM4CA is derived. Finally, two problem instances with different sizes are taken to conduct the experiments for comparing POM4CA with the current representative method under the light and heavy workload. The ex-perimental results show that POM4CA method can obtain better response time and spend less cost of cloud resources.

scholarly journals An Approach to Modeling and Analyzing Reliability for Microservice-Oriented Cloud Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zheng Liu ◽  
Guisheng Fan ◽  
Huiqun Yu ◽  
Liqiong Chen
Keyword(s):  
High Performance ◽  
Fault Tolerant ◽  
Service Reliability ◽  
Architectural Style ◽  
Modeling And Analysis ◽  
User Request ◽  
Cloud Application ◽  
Cloud Applications ◽  
Microservice Architecture

Microservice architecture is a cloud-native architectural style, which has attracted extensive attention from the scientific research and industry communities to benefit independent development and deployment. However, due to the complexity of cloud-based platforms, the design of fault-tolerant strategies for microservice-oriented cloud applications becomes challenging. In order to improve the quality of service, it is essential to focus on the microservice with more criticality and maximize the reliability of the entire cloud application. This paper studies the modeling and analysis of service reliability in the cloud environment. Firstly, a formal description language is defined to model microservice, user request, and container accurately. Secondly, the reliability analysis is conducted to measure a critical microservice’s fluctuation and vibration attributes within a period, and the related properties of the constructed model are analyzed. Thirdly, a fault-tolerant strategy with redundancy operation has been proposed to optimize cloud application reliability. Finally, the effectiveness of the method is verified by experiments. The simulation results show that the algorithm obtains the maximum benefits and has high performance through several experiments.

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