Mark Shtern

Clustering methodologies for software engineering

The size and complexity of industrial strength software systems are constantly increasing. This means that the task of managing a large software project is becoming even more challenging, especially in light of high turnover of experienced personnel. Software clustering approaches can help with the task of understanding large, complex software systems by automatically decomposing them into smaller, easier-to-manage subsystems. The main objective of this paper is to identify important research directions in the area of software clustering that require further attention in order to develop more effective and efficient clustering methodologies for software engineering. To that end, we first present the state of the art in software clustering research. We discuss the clustering methods that have received the most attention from the research community and outline their strengths and weaknesses. Our paper describes each phase of a clustering algorithm separately. We also present the most important approaches for evaluating the effectiveness of software clustering.

Model-based adaptive DoS attack mitigation

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 paper 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, which may ultimately result in dropping the request or presenting the end user with a CAPTCHA to verify they are a legitimate user. Experiments performed on a scalable implementation demonstrate effective mitigation of attacks launched using a real-world DoS attack tool.

A framework for the comparison of nested software decompositions

The evaluation of results obtained from software clustering algorithms has attracted the attention of many reverse engineering researchers. Several methods that compare flat decompositions of software systems have been presented in the literature. However, software clustering algorithms often produce nested decompositions. Converting nested decompositions to flat ones in order to compare them may remove significant information. We introduce a framework called END that reuses comparison methods for flat decompositions in order to compare nested decompositions without loss of information. We also present experimental results with END using several existing methods as plugins that demonstrate its usefulness.

Pattern-Based Deployment Service for Next Generation Clouds

This paper presents a flexible deployment service for cloud computing. The service facilitates the specification and the execution of cloud deployment plans for applications. An application is described through a pattern, an abstract view that captures the logical view of the application and its mapping into cloud resources. The services instantiate the pattern in the cloud and allows for runtime updates of the deployment. The service is accessible through a RESTful interface. We identify the requirements for the service, describe its interfaces and show several case studies that capture the main features of the service.

Towards Mitigation of Low and Slow Application DDoS Attacks

Distributed Denial of Service attacks are a growing threat to organizations and, as defense mechanisms are becoming more advanced, hackers are aiming at the application layer. For example, application layer Low and Slow Distributed Denial of Service attacks are becoming a serious issue because, due to low resource consumption, they are hard to detect. In this position paper, we propose a reference architecture that mitigates the Low and Slow Distributed Denial of Service attacks by utilizing Software Defined Infrastructure capabilities. We also propose two concrete architectures based on the reference architecture: a Performance Model-Based and Off-The-Shelf Components based architecture, respectively. We introduce the Shark Tank concept, a cluster under detailed monitoring that has full application capabilities and where suspicious requests are redirected for further filtering.

Lossless Comparison of Nested Software Decompositions

Reverse engineering legacy software systems often involves the employment of clustering algorithms that automatically decompose a software system into subsystems. The decompositions created by existing software clustering algorithms are often nested, i.e. subsystems may contain other finer-grained subsystems as well as system resources, such as source files. It is rather surprising then, that almost all existing methods for decomposition comparison assume flat decompositions, i.e. subsystems only contain system resources. In this paper, we introduce UpMoJo, a novel comparison method for software decompositions that can be applied to both nested and flat decompositions. The benefits of utilizing this method are presented in both analytical and experimental fashion. We also compare UpMoJo to the END framework, the only other existing method for nested decomposition comparison.

Hogna: a platform for self-adaptive applications in cloud environments

We propose Hogna, a platform for deploying self-managing web applications on cloud. The platform enables the deployment of the applications based on the automation of a set of operations (starting instances, installing necessary software and configuring the instances, etc.), and then the continuous monitoring of the health of the applications. The gathered monitoring data is analyzed using a performance model and an action plan is created and executed. Any components involved (for monitoring, analyzing, planning and deployment changes) can be customized to fit the needs of the application and/or researcher.

On the Comparability of Software Clustering Algorithms

Evaluation of software clustering algorithms is typically done by comparing the clustering results to an authoritative decomposition prepared manually by a system expert. A well-known drawback of this approach is the fact that there are many, equally valid ways to decompose a software system, since different clustering objectives create different decompositions. Evaluating all clustering algorithms against a single authoritative decomposition can lead to biased results. In this paper, we introduce and quantify the notion of clustering algorithm comparability. It is based on the concept that algorithms with different objectives should not be directly compared. Not surprisingly, we find that several of the published algorithms in the literature are not comparable to each other.

Refining clustering evaluation using structure indicators

The evaluation of the effectiveness of software clustering algorithms is a challenging research question. Several approaches that compare clustering results to an authoritative decomposition have been presented in the literature. Existing evaluation methods typically compress the evaluation results into a single number. They also often disagree with each other for reasons that are not well understood. In this paper, we introduce a novel set of indicators that evaluate structural discrepancies between software decompositions. They also allow researchers to investigate the differences between existing evaluation approaches in a reduced search space. Several experiments with real software systems showcase the usefulness of the introduced indicators.

Designing Adaptive Applications Deployed on Cloud Environments

Designing an adaptive system to meet its quality constraints in the face of environmental uncertainties can be a challenging task. In a cloud environment, a designer has to consider and evaluate different control points, that is, those variables that affect the quality of the software system. This article presents a methodology for designing adaptive systems in cloud environments. The proposed methodology consists of several phases that take high-level stakeholders’ adaptation goals and transform them into lower-level MAPE-K loop control points. The MAPE-K loops are then activated at runtime using search-based algorithms. Our methodology includes the elicitation, ranking, and evaluation of control points, all meant to enable a runtime search-based adaptation. We conducted several experiments to evaluate the different phases of our methodology and to validate the runtime adaptation efficiency.