Container Based Scalability and Performance Analysis of Multitenant SaaS Applications

In this paper the information technologies stack is presented. Thesetechnologies are used during network architecture deployment. The analysis of technological advantages and drawbacks under investigation for monolithic and network architectures will be useful during of cyber security analysis in telecom networks. The analysis of the main numeric characteristics was carried out with the aid of Kubectl. The results of a series of numerical experiments on the evaluation of the response speed to requests and the fault tolerance are presented. The characteristics of the of monolithic and microservice-based architectures scalability are under investigation. For the time series sets, which characterize the network server load, the value of the Hurst exponent was calculated.
 The research main goal is the monolithic and microservice architecture main characteristics analysis, time series data from the network server accruing, and their statistical analysis.
 The methodology of Kubernetes clusters deploying using Minikube, Kubectl, Docker has been used. Application deploy on AWS ECS virtual machine with monolithic architecture and on the Kubernetes cluster (AWS EKS) were conducted.
 The investigation results gives us the confirmation, that the microservices architecture would be more fault tolerance and flexible in comparison with the monolithic architecture. Time series fractal analysis on the server equipment load showed the presence of long-term dependency, so that we can treat the traffic implementation as a self-similar process.
 The scientific novelty of the article lies in the application of fractal analysis to real time series: use of the kernel in user space, kernel latency, RAM usage, caching of RAM collected over 6 months with a step of 10 seconds, establishing a long-term dependence of time series data.
 The practical significance of the research is methodology creation of the monolithic and microservice architectures deployment and exploitation, as well as the use of time series fractal analysis for the network equipment load exploration.

The previous chapter introduces the concept of Enterprise Internet of things with the help of sample applications. This chapter introduces the architectural concerns of Enterprise Internet of Things. The chapter starts with a reference architecture of Enterprise Internet of Things where all the components of an end to end IoT solution is explained. The critical component in the architecture is the middleware solution. The middleware component enables communication across the disparate hardware and software components in the solution. Enterprise IoT solutions are more complicated to develop and maintain due to the participation of several vendors. The interoperability challenges are handled with the help of middleware solutions and this chapter addresses two important architectures namely Monolithic architecture and Microservices architecture. These architectures are not specific to IoT solutions but generic for Enterprise software development. This chapter illustrates how the Microservices architecture is the most suitable choice for designing Enterprise IoT applications. In the last part, there is a detailed discussion on the trade-off between Monolithic and Micro services architectures.

Most developers still use the monolithic architecture, where all components of an application are combined into one integrated system, so each part depends on other components. The monolithic architecture has weaknesses, such as when a failure occurs in one component, all parts cannot be executed because each component relies on one other component. Microservices can be a solution to this, considering that in the microservices architecture, each element or service is created and put separately, so when a failure occurs in one component, other components will not be affected and can still run normally. This research aims to determine the implementation and performance comparison between monolithic architecture and microservices Architecture in the Agreeculture Market web app. Agreeculture Market is a web application that aims to facilitate the transaction process of agricultural commodities and make it easier for agricultural commodity producers to market their products. The measurement method used to measure the performance of both architectures is load testing using JMeter and performance tools from task manager and comparing the response time, throughput, disk usage, CPU usage, and memory usage of both used architectures. With two measurement schemes with Docker and without Docker, the result of this research is a performance comparison between the two architectures, where the backend application Agreeculture Market, which uses microservices architecture with Docker and API gateway, performs better than the monolithic architecture version. Conversely, the monolithic architecture performs better than the microservices architecture in the scheme without Docker and API gateway.

Many HPC and modern large graph processing applications belong to a class of scale-out applications, where the application dataset is partitioned and processed by a cluster of machines. Assessing the application scalability is one of the primary goals during such application implementation. Typically, in the design phase, programmers are limited by a small size cluster available for their experiments. Therefore, predictive modeling is required for the analysis of the application scalability and its performance in a larger cluster. While in an increased size cluster, each node will process a smaller portion of the original dataset, a higher communication volume between a larger number of nodes may cripple the application scalability and provide diminishing performance benefits. One of the main challenges is the analysis of bandwidth demands due to an increased communication volume in a larger size cluster. In this paper1, we introduce a novel regression-based approach to assess the scalability and performance of a distributed memory program for execution in a large-scale cluster. Our solution involves 1) a limited set of traditional experiments performed in a small size cluster and 2) an additional set of similar experiments performed with an “interconnect bandwidth throttling” tool, which exposes the bandwidth impact on the application performance. These measurements are used in creating an ensemble of analytical models for performance and scalability analysis. Using a linear regression approach, step by step, we incorporate into the model the following important parameters: i) the number of cluster nodes and application processes, ii) the dataset size, and iii) interconnect bandwidth. We demonstrate our solution, its power, and accuracy using a popular Graph500 benchmark, which implements a Breadth First Search algorithm on large, synthetically generated graphs. By utilizing measurements collected in a 32-node cluster, we are able to project the program performance in a large size cluster with hundreds of nodes. The proposed approach and derived models help to provide an early feedback to programmers on the scalability and efficiency of their solution.

Software as a service (SaaS) is a promising service model of cloud computing. Its key characteristic is the ability for clients to use a software application on a pay-as-you-go subscription basis. To be economically sustainable, a SaaS application must be multitenant. However, it is generally agreed that designing a multitenant enterprise application in SaaS environments is a non-trival task. In this work, we propose an integrated service middleware that addresses cross-cutting concerns when developing and deploying multitenant enterprise SaaS applications. To verify the feasibility of our approach, a sample SaaS application have been implemented on the proposed middleware. Also, two tenant-specific virtual applications are constructed to demonstrate multi-tenancy. Finally, a series of performance evaluations are conducted to assess the overheads of making an enterprise application multitenant enabled.

In this paper, the conceptual architecture of a SaaS platform that enables executing of configurable and multitenant SaaS application is proposed. Even though most of organizations utilize a standardized SaaS application developed by a SaaS developer, each of tenants would have unique requirements regarding service components. The platform provides several features for configuring these aspects of SaaS software such as organizational structure (role sets and access control), user interface, data model, workflow, and business logic. To satisfy multitenancy of SaaS application, we applied metadata driven architecture proposed by Force.com. The target SaaS platform is composed of the key components that supports SaaS Application execution environment that serves multiple tenant using a single service instance. An example scenario of SaaS software lifecycle is also described to explain how the target platform would be operated from development and deployment of SaaS application to configuration by tenants.

Intuitive visual representations of architecture capabilities and the performance of applications are critical to enabling effective performance analysis, which in turn guides optimizations. The Roofline Model and its derivatives provide such an intuitive representation of the best achievable performance on a given architecture. The Roofline Toolkit project is a collaboration among researchers at Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the University of Oregon and consists of three principal components: hardware characterization, software characterization, and data manipulation, which includes a visualization interface. These components address the different aspects of performance data acquisition and manipulation required for performance analysis, modeling and optimization of applications. In this paper we introduce an implementation of the third component, a system for visualizing roofline charts and managing roofline performance analysis data. We demonstrate analysis of an application use case within this framework and outline future directions for this type of performance analysis and visualization.

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple "tenants" of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants--i.e. multi-tenancy--increases tenants' concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants' needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants' security requirements. We use abstract models to capture service provider and multiple tenants' security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.

Microservice architecture reduces the coupling between application modules, which is conducive to the development, deployment and operation and maintenance of complex application systems. However, compared with the monolithic architecture, the access control of its resources becomes more complex. Based on the application scenario of enterprise system micro service architecture, aiming at the high security requirements of business system, through the analysis and research of micro service architecture, this paper designs a set of access control solution, which can meet the requirements of unified authentication, improving the efficiency of authority verification and accelerating the system development speed under the microservice architecture, which has reference for enterprise application system design significance.

<p><b>The client-centric multi-cloud has become a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud providers to achieve high-quality services with lower operation costs and higher application resilience. From the perspective of application providers, the location of cloud resources for application deployment significantly impacts the deployment cost and performance of applications, e.g., request response time. This gives rise to the problem of location-aware application deployment in multi-cloud to select suitable cloud resources from widely distributed multi-cloud data centers to balance the cost and performance. Existing research works did not pay full attention to the key impact of the location for application deployment. Therefore, it is urgent to study the problem both theoretically and in practice. In this thesis, innovative optimization methods and machine learning techniques are proposed for three common scenarios, namely composite application deployment, application replication and deployment, and elastic application deployment.</b></p> <p>First, this thesis studies the composite application deployment problem with the goal to minimize the average response time of composite applications subject to a budget constraint. We propose a Hybrid Genetic Algorithm (GA)-based approach, i.e., H-GA, for solving the problem with an extremely large search space. H-GA features a newly designed and domain-tailored service clustering algorithm, repair algorithm, solution representation, population initialization, and genetic operators. Experiments show that H-GA can outperform significantly several state-of-the-art approaches, achieving up to about 8% performance improvement in terms of response time, and 100% budget satisfaction in the meantime.</p> <p>Second, this thesis studies the application replication and deployment problem with the goal to minimize the total deployment cost of all application replicas subject to a stringent requirement on average response time. We propose two approaches under different optimization frameworks to solve the problem. With user requests dispatched to the closest application replicas, we develop an approach under a GA framework for Application Replication and Deployment (ARD), i.e., GA-ARD. GAARD features problem-specific solution representation, fitness measurement, and population initialization, which are effective to optimize the deployment of application replicas in multi-cloud. The experiments show that GA-ARD outperforms common application replication and placement strategies in the industry. With user requests flexibly dispatched among different application replicas, we develop another approach under a two-stage optimization framework, i.e., MCApp. MCApp can optimize both replica deployment and request dispatching by combining mixed-integer linear programming with domain-tailored large neighborhood search. Our experiments show that MCApp can achieve up to 25% reduction in total deployment cost compared with several recently developed approaches.</p> <p>Third, this thesis studies the elastic application deployment problem to minimize the deployment cost over a time span such as a billing day while satisfying the constraint on average response time. The goal of adapting resources for application deployment in response to dynamic and distributed workloads motivates us to adopt deep reinforcement learning (DRL) techniques. The proposed approach, namely DeepScale, applies a deep Q-network (DQN) to capture the optimal scaling policy that can perform online resource scaling. DeepScale also includes a long short-term memory-based prediction model to allow the DQN to consider predicted future requests while making cost-effective scaling decisions. Besides, we design a penalty-based reward function and a safety-aware action executor to ensure that any scaling decisions made by DRL can satisfy the response time constraint. The experiments show that DeepScale can significantly reduce the deployment cost of applications compared with the state-of-the-art baselines, including Amazon auto-scaling service and recently proposed RL-based algorithms. In the meanwhile, DeepScale can effectively satisfy the constraint on the average response time.</p> <p>In summary, this thesis studies three new problems for location-aware application deployment in multi-cloud. We propose four novel approaches under different optimization and machine learning frameworks, i.e., HGA, GA-ARD, MCApp, and DeepScale, for solving these problems. New constraint handling techniques are developed to satisfy the practical deployment requirements of enterprise applications.</p>

The subject of the study is the methods of customization of SaaS solutions. The purpose of the article is to determine a holistic strategy for customizing SaaS solutions developed using cloud-native technologies. Objectives: to analyze modern approaches to the architecture of SaaS applications; to identify the main methods of customization for modern SaaS applications; to investigate and establish a method for customizing the interface of SaaS applications; based on the study, to determine a comprehensive strategy for customizing SaaS applications. The following methods are implemented: analysis and synthesis - to study the technologies that are used to build SaaS applications; abstraction and generalization - to determine the overall architecture of the SaaS application; synthesis of web technologies - to select methods of customization of SaaS applications and build an algorithm for selecting a customization method. Results achieved: the modern architecture of SaaS applications is studied; methods of customization of SaaS applications are selected, and a holistic strategy for customization of SaaS applications is defined; a method for modifying the interface of SaaS applications is determined, which allows customization of the web interface of a SaaS application regardless of the specific front-end framework. Conclusions: modern architecture of SaaS applications is complex and based on microservice architecture, multi-tenant approach, cloud technologies and web browser; methods of customization of SaaS applications should be developed independently of certain technology stacks to meet the customization needs of most or all users of SaaS applications; the API-first approach is fundamental in building a customized SaaS, as it is the basis for creating any automation or related business logic; webhooks are the best option for implementing subscriptions to events occurring in a SaaS application, regardless of the technology stack; a defined strategy for customizing SaaS applications allows customization regardless of a particular technology stack and, in addition to implementing business logic, also covers interface modifications.

Software architecture is a set of important decisions about the organization of the software system. The success and speed of the project development, the complexity of its support and understanding of the program structure depends on the correct choice of software architecture. For this reason, developers often use new architectural solutions to build software, trying to find the best solutions to a problem. The common practice of program development was the development on the principle of monolithic architecture, which envisages the project as a single program, which is responsible for all necessary functionality. Of course, this approach had its advantages, such as ease of development, testing and deploying. But with project`s growth, the monolithic architecture begins to make some flaws in the development process: the addition of a certain new functionality is accompanied by an increase in the code base, observance of limits of responsibility of internal modules is blurred, compiling and deploying takes much more time. Given the drawbacks of monolithic architecture above, developers have begun to take other approaches. One is microservice architecture. Microservice Architecture – A variant of service architecture (modular software development approach) software, focused on the interaction of as small as possible, loosely coupled and easily replaceable modules – microservices [1]. With such an architecture, the project is a set of small services, the communication between which is done through lightweight mechanisms (for example, HTTP, which can be used in REST architectural style to get an increased productivity and simplified architecture) [2]. Each of these services must fulfil its clearly defined business task. This architecture has the following advantages: better scaling, independent module development and deploying, better reusability etc.

The shortage of traditional web development makes researchers turn to web development using Progressive Web App (PWA) in developing a web, which has been increasingly done. PWA has the ability to function even with a low internet connection, maintain interactive user and access speeds, and provide real-time notifications. In modern web development, where the demand for data, features, and users can increase rapidly, an architecture is required to handle this problem. Microservices architecture can answer this. This research will develop a scholarship application using PWA and microservices architecture. The scholarship application was developed using Veu.js for the front end of the client application, MySQL for the application database, and the SLIM framework for microservices implementation. Furthermore, we utilized RAbbitMQ to develop a message broker and Docker for microservices contaner. The scholarship application developed using PWA technology and microservices in this research has been tested by conducting a User Acceptance Test. The results are the implementation of PWA in the scholarship application using microservice architecture has worked well, and the application performance has been able to overcome problems in terms of scalability, speed, and reliability.

For an IaaS cloud, the primary task is to satisfy users' demands for resources. Besides, administrators also have to deal with problems such as how to optimize allocation and utilization of resources at the data center level, how to guarantee the application's performance and scalability and how to cut the costs of maintenance and management. This paper focuses on the runtime optimization of IaaS private clouds. In such an environment, administrators usually have more autonomy and control over cloud resources and applications, which brings more space for optimization. In order to achieve appropriate resource allocation for virtual machines and improve utilization, this paper first proposes WarMops: a workload-aware method to optimize the resource configuration of virtual machines and an allocation scheme based on resource reservation and sharing, in an effort to arrive at the proper size of resources to meet the real needs of virtual machines. Integrating the methods mentioned above, this paper puts forward a systematic framework and modules for the runtime optimization of cloud resource management. This paper uses a mainstream benchmark application in this field - RUBiS to test the framework which, in turn, verifies the correctness and validity of the schemes.

The growing interest in the Internet of Things (IoT) has prompted a more innovative architectural design approach and evolutionary frameworks to integrate heterogeneous IoT devices better. Edge computing, which provides services close to end-user devices without requiring high-speed Internet connectivity, has become a popular paradigm for reducing network congestion and allowing real-time Internet of Things applications. However, the heterogeneous and resource-constrained edge nodes make it difficult to design edge computing frameworks. However, because the edge computing environment is dynamic, with irregular connections and limited bandwidth, it's critical to deal with the challenge of determining the relative performance of applications deployed in the edge computing paradigm when input traffic, networking protocols, or system software changes. So, this paper explores three software architectures, namely monolithic architecture, microservices architecture (MSA), and service-oriented architecture (SOA), providing a framework for edge computing's many requirements. A comparative analysis of these three software architectures is also covered based on the fundamental characteristics of an edge computing framework.