Microservice architecture has transformed the way developers are building and deploying applications in the nowadays cloud computing centers. This new approach provides increased scalability, flexibility, manageability, and performance while reducing the complexity of the whole software development life cycle. The increase in cloud resource utilization also benefits microservice providers. Various microservice platforms have emerged to facilitate the DevOps of containerized services by enabling continuous integration and delivery. Microservice platforms deploy application containers on virtual or physical machines provided by public/private cloud infrastructures in a seamless manner. In this article, we study and evaluate the provisioning performance of microservice platforms by incorporating the details of all layers (i.e., both micro and macro layers) in the modeling process. To this end, we first build a microservice platform on top of Amazon EC2 cloud and then leverage it to develop a comprehensive performance model to perform what-if analysis and capacity planning for microservice platforms at scale. In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both the microservice platform and the back-end macroservice infrastructures.

Adaptability is an expected property of modern software systems in order to cope with changes in the environment by self-adjusting their structure and behaviour. Robustness is a crucial component of adaptability and it refers to the ability of the systems to deal with uncertainty, i.e. perturbations or unmodelled system dynamics that can affect the quality of the adaptation. Cost is another important property to ensure that resources are used prudently and frugally, whenever possible. Engineering robust and cost-effective adaptive systems can be accomplished using a control theory approach. In this paper, we show how to implement a model identification adaptive controller (MIAC) using a combination of performance and control models and how such a system satisfies the goals for robustness and cost-effectiveness. The controller we employ is multi-input, meaning that it can issue a variety of commands to adapt the system and multi-output, meaning it can regulate multiple performance indicators simultaneously. We show that such a solution can account for uncertainty and modelling errors and efficiently adapt a web application with multiple tiers of functionality spanning multiple layers of deployment, software and virtual machines, on Amazon EC2, an actual cloud environment.

Virtualization of resources in cloud computing has enabled developers to commission and recommission resources at will and on demand. This virtualization is a coin with two sides. On one hand, the flexibility in managing virtual resources has enabled developers to efficiently manage their costs; they can easily remove unnecessary resources or add resources temporarily when the demand increases. On the other hand, the volatility of such environment and the velocity with which changes can occur may have a greater impact on the economic position of a stakeholder and the business balance of the overall ecosystem. In this work, we recognise the business ecosystem of cloud computing as an economy of scale and explore the effect of this fact on decisions concerning scaling the infrastructure of web applications to account for fluctuations in demand. The goal is to reveal and formalize opportunities for economically optimal scaling that takes into account not only the cost of infrastructure but also the revenue from service delivery and eventually the profit of the service provider. The end product is a scaling mechanism that makes decisions based on both performance and economic criteria and takes adaptive actions to optimize both performance and profitability for the system.

Web software systems generally reside in highly volatile environments, their incoming traffic may be subject to sharp fluctuations from reasons that cannot always be captured or predicted. Cloud computing provides a solution to this problem by offering flexible resources, like containers, which can be quickly and easily scaled according to the current workload needs. Automating this process is a key aspect for the management of modern web software systems, and there is a plethora of methods to implement autonomic management systems. In this work, we review three of these methods, a threshold-based approach, a control-based approach and a model-based approach. We design and run a number of experiments for all three systems with different workloads to evaluate their ability to manage the software system and how well they do so. Our experiments were conducted on the Amazon EC2 cloud with Docker containers.

This paper presents an adaptive service management mechanism that maintains service level agreement through use of overlay networks that are deployed over the cloud provider network. The application autonomic manager strives to maintain the SLA without provisioning new resources for as long as possible. Through continuous monitoring and analysis, autonomic manager uses software defined networking (SDN) to dynamically apply policies to the flows of requests that travel through the application components. We implement and evaluate the proposed method on a hybrid cloud environment. Through extensive experiments, we show that the management mechanism can successfully maintain the SLA of services while it avoids provisioning extra resources which is the common approach in cloud.

In this paper, we present an architecture and implementation for self-managing cloud application using overlay networks and software defined networking (SDN). Through real world experiments on Amazon EC2 and Smart Applications on Virtual Infrastructure (SAVI) cloud, we demonstrate how our management mechanism autonomously maintains SLAs of application scenarios without provisioning extra resources.

Web technologies along with the virtualization of computation and storage resources have allowed extensive flexibility in the development, deployment and delivery of software solutions. On one hand, this flexibility has allowed engineers to commission and re purpose resources on demand and at will. On the other hand, the flexibility has given rise to new business models and interesting implications in the economics of software ecosystems. In this work, we propose a model to capture the technical and economic transactions within a web software ecosystem deployed on a dynamic cloud environment. Next, we demonstrate the use of such model to define rules for a more economically efficient adaptation strategy for web applications as their incoming traffic fluctuates.

Microservice architecture has started a new trend for application development/deployment in cloud due to its flexibility, scalability, manageability and performance. Various microservice platforms have emerged to facilitate the whole software engineering cycle for cloud applications from design, development, test, deployment to maintenance. In this paper, we propose a performance analytical model and validate it by experiments to study the provisioning performance of microservice platforms. We design and develop a microservice platform on Amazon EC2 cloud using Docker technology family to identify important elements contributing to the performance of microservice platforms. We leverage the results and insights from experiments to build a tractable analytical performance model that can be used to perform what-if analysis and capacity planning in a systematic manner for large scale microservices with minimum amount of time and cost.

Distributed Denial of Service (DDoS) attacks are one of the main concerns for online service providers because of their impact on cost/revenue and reputation. This paper presents Completely Automated DDoS Attack Mitigation Platform (CAAMP), a novel platform to mitigate DDoS attacks on public cloud applications using capabilities of software defined infrastructure and network function virtualization techniques. When suspicious traffic is identified, CAAMP deploys a copy of the application's topology on-the-fly (a shark tank) on an isolated environment in a private cloud. It then creates a virtual network that will host the shark tank. Software defined networking (SDN) controller programs the virtual switches dynamically to redirect the suspicious traffic to the shark tank until final decision is made. If traffic is proved to be non-malicious, SDN controller installs flow rules on the switches to redirect the traffic back to the original application. Thus, CAAMP autonomically protects applications against potential DDoS threats and lowers the false positives associated with common detection mechanisms by leveraging resources from a private cloud.

Large-scale software systems like Amazon and eBay must be load tested to ensure they can handle hundreds and millions of current requests in the field. Load testing usually lasts for a few hours or even days and generates large volumes of system behavior data (execution logs and counters). This data must be properly analyzed to check whether there are any performance problems in a load test. However, the sheer size of the data prevents effective manual analysis. In addition, unlike functional tests, there is usually no test oracle associated with a load test. To cope with these challenges, there have been many analysis techniques proposed to automatically detect problems in a load test by comparing the behavior of the current test against previous test(s). Unfortunately, none of these techniques compare their performance against each other. In this paper, we have proposed a framework, which evaluates and compares the effectiveness of different test analysis techniques. We have evaluated a total of 23 test analysis techniques using load testing data from three open source systems. Based on our experiments, we have found that all the test analysis techniques can effectively build performance models using data from both buggy or non-buggy tests and flag the performance deviations between them. It is more cost-effective to compare the current test against two recent previous test(s), while using testing data collected under longer sampling intervals (≥ 180 seconds). Among all the test analysis techniques, Control Chart, Descriptive Statistics and Regression Tree yield the best performance. Our evaluation framework and findings can be very useful for load testing practitioners and researchers. To encourage further research on this topic, we have made our testing data publicity available to download.

This paper presents a model and an algorithm for optimal service placement (OSP) of a set of N-tier software systems, subject to dynamic changes in the workload, Service Level Agreements (SLA), and administrator preferences. The objective function models the resources' cost, the service level agreements and the trashing cost. The optimization algorithm is predictive: its allocation or reallocation decisions are based not only on the current metrics but also on predicted evolution of the system. The solution of the optimization, in each step, is a set some service replicas to be added or removed from the available hosts. These deployment changes are optimal with regards to overall objectives defined over time. In addition, the optimization considers the restrictions imposed on the number of possible service migrations at each time interval. We present experimental results that show the effectiveness of our approach.

Denial of Service (DoS) attacks overwhelm online services, preventing legitimate users from accessing a service, often with impact on revenue or consumer trust. Approaches exist to filter network-level attacks, but application-level attacks are harder to detect at the firewall. Filtering at this level can be computationally expensive and difficult to scale, while still producing false positives that block legitimate users. This article presents a model-based adaptive architecture and algorithm for detecting DoS attacks at the web application level and mitigating them. Using a performance model to predict the impact of arriving requests, a decision engine adaptively generates rules for filtering traffic and sending suspicious traffic for further review, where the end user is given the opportunity to demonstrate they are a legitimate user. If no legitimate user responds to the challenge, the request is dropped. Experiments performed on a scalable implementation demonstrate effective mitigation of attacks launched using a real-world DoS attack tool.

ABSTRACT Performance engineering for Web applications must take into account both the development and runtime information about the target system and its environment. At development time, the architects have to choose from many architecture styles and consider all performance requirements across a multitude of workloads. At runtime, an Autonomic Manager has to compensate for the changing operating and environment conditions not accounted for at the design time and make decisions about changes in the architecture so the performance requirements are met. This paper proposes a formal framework called Software Performance for Autonomic Computing for making decisions with regard to a possible set of candidate architectures: usage scenarios are criteria according to which architectures are evaluated; actual performance metrics, such as response time or throughput, are obtained by solving performance models and then matched against the performance requirements; performance requirements are defined by modeling user satisfaction with a utility function. Criteria can be weighted to reflect their importance. The framework can be used both at design and run time. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we present a method for performance testing of transactional systems. The methods models the system under test, finds the software and hardware bottlenecks and generate the workloads that saturate them. The framework is adaptive, the model and workloads are determined during the performance test execution by measuring the system performance, fitting a performance model and by analytically computing the number and mix of users that will saturate the bottlenecks.

Estimation techniques have been largely applied to track hidden performance parameters (e.g. service demands) of web based software systems. In this paper we investigate dynamic multiclass modeling of such systems, with variable classes of service, aiming at finding a low complexity model yet with enough accuracy. We propose a combination of clustering algorithm and tracking filter for effective grouping of classes of services. The tracking estimator is based on a layered queuing model with parameters for CPU demands and the user load intensity of each class of service. Clustering uses the K-means algorithm. The target application is autonomic control of web clusters, where changes occur at different rates and amplitudes and at random time instants. Experiments show that the tracking is effective, and reveal good filter settings for different variations.