Cost-Aware Performance Modeling of Multi-tier Web Applications in the Cloud

Abstract—In this article I have deployed a web-based applica- tion to Docker using Amazon web service (EC2 Instance). Elastic Cloud Compute service is used to the run the docker applications on scalable clusters. Amazon EC2 is fully managed and secured Container orchestration service that makes to deploy, manage and scale Dockerized web application. Docker containers on an EC2 instance to run a university management system website on the Amazon Web Services (AWS) cloud. For educational institutions, a university management system is a crucial tool that enables administrators to manage student records, course information, and other crucial data. The introduction to Docker, a containerization technology that makes it simple to build and manage web applications, follows a discussion of the advantages of hosting a university management system website on the cloud. The next section of the paper focuses on the AWS EC2 service and its capabilities, such as its capability for flexible computing and scalability. The paper also offers a step-by-step tutorial on how to use Docker to deploy a university administration system website on an EC2 instance, including instructions on how to set up the EC2 instance, install and configure Docker, and deploy the website code in a Docker container. Elastic Load Balancer and Auto Scaling, two AWS services, are used in the process of setting up security and scalability for the website. The benefits of using Docker to host a university administration system website on AWS EC2 are outlined in the paper’s conclusion, along with predictions for the development of cloud-based web application hosting. This research paper offers a thorough overview of the steps involved in hosting a university management system website using Docker on AWS EC2, making it a useful tool for educational institutions looking to improve their information management procedures and take advantage of cloud computing and Docker- Containerization.

Significant effort is currently invested in application integration, enabling business processes of different companies to interact and form complex multiparty processes. Web service standards, based on WSDL (Web Service Definition Language), have been adopted as process-to-process communication paradigms. However, the conceptual modeling of applications using Web services has not yet been addressed. Interaction with Web services is often specified at the level of the source code; thus, Web service interfaces are buried within a programmatic specification.In this article, we argue that Web services should be considered first-class citizens in the specification of Web applications. Thus, service-enabled Web applications should benefit from the high-level modeling and automatic code generation techniques that have long been advocated for Web application design and implementation. To this end, we extend a declarative model for specifying data-intensive Web applications in two directions: (i) high-level modeling of Web services and their interactions with the Web applications which use them, and (ii) modeling and specification of Web applications implementing new, complex Web services.Our approach is fully implemented within a CASE tool allowing the high-level modeling and automatic deployment of service-enabled Web applications.

Analytical (predictive) application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult because they must be both accurate and concise. To ease the burden of performance modeling, we developed Palm (Performance and Architecture Lab Modeling tool), a modeling tool that combines top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. First, Palm provides a source code modeling annotation language for abstracting or expressing complexity. Second, Palm generates hierarchical models according to well-defined rules. Since a model's hierarchy is defined by static and dynamic source code structure, there is a link between a program's organization and its model. By coordinating models and source code, Palm's models are 'first-class' and reproducible. Third, Palm incorporates measurements to focus attention, represent constant behavior, and validate models. We discuss generating models for three different applications.

Data Security is a worldwide concern mostly for small medium enterprise (SMEs) and frameworks, approaches, methods are constantly evolving that has a connection with cloud computing, information systems, artificial intelligence, blockchain. Many developers, administrators or product teams running blind. Those are not knowing of problems with their application or do not have the information to fix the problems. The things which can go wrong with web and mobile applications or services is unlimited like dependency failures, resources, and crashes. Main argument is an evaluation of benefits by using Cloud as infrastructure and application on proactive monitoring called Azure Application Insights (AppInsight) towards target like web application, web API, PKI etc. The findings, demonstration of the study should reveal and support our main hypothesis that there is direct link between the proactive monitoring and the main factors that affects utilizing the cloud services. To address this need, in this paper, we introduce AppInsight, the best practice and a model of proactive approach to monitor different targets using Microsoft technology on Azure Cloud services. AppInsight – a model of proactive monitoring includes several functionalities: (1) identifying availability, (2) failures dependencies, (3) performance and (4) using telemetry data generates ad-hoc solution to fix potential failure of web application, web API etc. AppInsight a feature of Azure Monitor used to monitor live applications. AppInsight will automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues. You will get a range of telemetry data of analytics of your target which is monitored by AppInsight. To evaluate this tool, we conduct an empirical evaluation by comparing data from actual live monitoring of Y target. Demo Video: https://www.youtube.com/watch?v=q7R8-c0ge7M

Cloud computing is a computational model in which resource providers can offer on-demand services to clients in a transparent way. However, to be able to guarantee quality of service without limiting the number of accepted requests, providers must be able to dynamically manage the available resources so that they can be optimized. This dynamic resource management is not a trivial task, since it involves meeting several challenges related to workload modeling, virtualization, performance modeling, deployment and monitoring of applications on virtualized resources. This paper carries out a performance evaluation of a module for resource management in a cloud environment that includes handling available resources during execution time and ensuring the quality of service defined in the service level agreement. An analysis was conducted of different resource configurations to define which dimension of resource scaling has a real influence on client requests. The results were used to model and implement a simulated cloud system, in which the allocated resource can be changed on-the-fly, with a corresponding change in price. In this way, the proposed module seeks to satisfy both the client by ensuring quality of service, and the provider by ensuring the best use of resources at a fair price.

With the advent of gaining popularity in cloud computing, there is an increase in demand of resources among the heterogeneous workload types on cloud. Resource management is a key challenging problem that is faced by the cloud service providers, by achieving business goals and agreed level of service with the subscribers. This paper focuses on dynamic resource allocation with risk analysis by meeting service level agreements. Further proposed framework handles heterogeneous workload types by dynamic capacity planning with risk assessment to maximise the profit and resource utilisation on clouds. In addition to advanced resource reservation, SLA-based scheduling/rescheduling with risks involved in resource allocation is considered in the proposed model. The experimental results demonstrate that proposed framework maximises the resource utilisation and profit gain of the cloud service provider when evaluated against widely used static configuration strategy.

This paper presents WebRatio BPM, an Eclipse-based tool that supports the design and deployment of business processes as Web applications. The tool applies Model Driven Engineering techniques to complex, multi-actor business processes, mixing tasks executed by humans and by machines, and produces a Web application running prototype that implements the specified process. Business processes are described through the standard BPMN notation, extended with information on task assignment, escalation policies, activity semantics, and typed dataflows, to enable a two-step generative approach: first the Process Model is automatically transformed into a Web Application Model in the WebML notation, which seamlessly expresses both human- and machine-executable tasks; secondly, the Application Model is fed to an automatic transformation capable of producing the running code. The tool provides various features that increase the productivity and the quality of the resulting application: one-click generation of a running prototype of the process from the BPMN model; fine-grained refinement of the resulting application; support of continuous evolution of the application design after requirements changes (both at business process and at application levels).KeywordsBusiness ProcessModel TransformationApplication ModelSite ViewBPMN ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The testing of Ajax (Asynchronous JavaScript and XML) web applications poses novel challenges for testers because Ajax constructs dynamic web applications by using Asynchronous communication and run time Document Object Model (DOM) manipulation. Ajax involves extreme dynamism, which induces novel kind of issues like state explosion, triggering state changes and unreachable states etc. that require more demanding web-testing methods. Model based testing is amongst the effective approaches to detect faults in web applications. However, the state model generated for an Ajax application can be enormous and may be hit by state explosion problem for large number of user action based changes and for immense dynamism. Recent research has not addressed this issue comprehensively because existing techniques either apply partial reduction or compromise the effectiveness of testing. This research uses soft computing based Fuzzy C Means (FCM) clustering algorithm to generate state machine model of an Ajax web application. The focus is on devising a framework to avoid the state explosion problem. The framework prioritizes the requirements and use cases based on requirements weightage, stakeholder weightage and user session based use case frequency. FCM uses this data to reduce the state space by identifying the most pivotal usage areas. The resultant DOM mutations for only these usage areas are considered to induce the finite state machine thus avoiding the state explosion. Experimental results show that the framework controls the size of generated state machine without compromising the effectiveness of testing.

It will lead to the failure of entire development if the software developed cannot satisfy the performance needs. Software performance engineering is an evaluating process which analyzes the performance before completion of software. With the development of internet technology, web application has become the mainstream of modern software development. Currently, the evaluation before development is generally through modeling and simulation, however, which needs parameters to support. The key to performance modeling and evaluation is to collect parameters. Through analysis, the biggest difference between web applications and traditional desktop software is that web applications have a variable number of users. The key factors to influence the performance of web application are the access time of database in multiple users and the response time of various Medias and files. Therefore, in the context, the access time of database in different concurrent number of users need to be collected, then analysis the relationship between parameters and multivariate attributes such as concurrent numbers, keyword numbers and record numbers, sum up and come to the similar function and obtain the performance parameters library which provide a basis for performance modeling and evaluation.

Cloud Computing has received much attention by the IT-Business world. As compared to the common computing platforms, cloud computing is more flexible in supporting real-time computation and is considered a more powerful model for hosting and delivering services over the Internet. However, since cloud computing is still at its infancy, it faces many challenges that stand against its growth and spread. This article discusses some challenges facing cloud computing growth and conducts a comparison study between Amazon EC2 and Windows Azure in dealing with such challenges. It concludes that Amazon EC2 generally offers better solutions than Windows Azure. Nevertheless, the selection between them depends on the needs of customers.

In recent years, the number of users and service providers are increasing in using cloud services so the accessibility and the effective management of the required resources, irrespective of the time and place, seem to be of great importance for both sides.Improving the performance and utilization of the cloud systems are gained by the auto-scaling of the applications; this is because of the fact that, some approaches have been proposed for auto scaling.This paper seeks to checking some value, based on the learning automata, for the scalability of the web applications, which combines virtual machine clusters and the learning automata in order to provide the best possible way for the scaling up and scaling down of the virtual machines.The results of this study indicate how an increased capacity of virtual machine which have been done by the value of thresholds could effect on SLA and overhead of responding.

A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances

The World Wide Web has evolved gradually from a document delivery platform to an architecture for distributed programming. This largely unplanned evolution is apparent in the set of interconnected languages and protocols that any Web application must manage. This paper presents Ur/Web, a domain-specific, statically typed functional programming language with a much simpler model for programming modern Web applications. Ur/Web's model is unified, where programs in a single programming language are compiled to other "Web standards" languages as needed; supports novel kinds of encapsulation of Web-specific state; and exposes simple concurrency, where programmers can reason about distributed, multithreaded applications via a mix of transactions and cooperative preemption. We give a tutorial introduction to the main features of Ur/Web and discuss the language implementation and the production Web applications that use it.

MuMMI (Multiple Metrics Modeling Infrastructure) environment is an infrastructure that facilitates systematic measurement, modeling, and prediction of performance, power consumption and performance-power tradeoffs for parallel systems. MuMMI builds upon three existing frameworks: Prophesy for performance modeling and prediction of parallel applications, PAPI for hardware performance counter monitoring, and PowerPack for power measurement and profiling. In this paper, we present the MuMMI framework, which consists of an Instrumentor, Databases and Analyzer. The MuMMI Instrumentor provides automatic performance and power data collection and storage with low overhead. The MuMMI Databases extend the databases of Prophesy to store power and energy consumption and hardware performance counters' data with different CPU frequency settings. The MuMMI Analyzer extends the data analysis component of Prophesy to support power consumption and hardware performance counters, and it entails performance and power modeling, performance-power tradeoff and optimizations, and web-based automated modeling system. Currently, our MuMMI online automated performance and power modeling system uses four modeling techniques: curve fitting, parameterization, kernel coupling and performance-counters-based, we discuss the effort to automate the process of developing performance and power models for scientific applications online, and focus on exploring performance-counters-based performance and power modeling. The MuMMI environment is able to aid in performance and power data measurement, storage, modeling and prediction of scientific applications on XSEDE resources in XSEDE community.

With the rapid growth of biological technology, large amount of biological data can be produced in few days or months. Many of common-used tools become computation-consuming in analyzing such big biological data. Cloud computing has emerged to provide the huge amount of computing power and play important role in development of bioinformatics tools. We propose a cloud computing framework that is able to easily deploy the bioinformatics tools on cloud virtualization platform based on Hadoop. This framework can work on the public cloud platform vendor such as Amazon EC2 and also private cloud platform. All the tools performed by cloud computing framework as Bioinformatics as a Services are available at http://bioinfo.cs.pu.edu.tw/CBBTS. The tools deployed on cloud platform by the proposed framework are tested in Providence University cloud platform and provided good proportional acceleration when scaled out onto many computational units. In the big biological data era, cloud computing based solutions are important role to develop bioinformatics services over internet. In the work, the proposed framework is able to simply deploy several well-known bioinformatics tools on cloud virtualization platform and as web services. This framework can work on the public cloud platform vendor such as Amazon EC2 and also private cloud platform.