Detecting and Refactoring Operational Smells within the Domain Name System

The Domain Name System (DNS) is one of the most important components of the Internet infrastructure. DNS relies on a delegation-based architecture, where resolution of names to their IP addresses requires resolving the names of the servers responsible for those names. The recursive structures of the inter dependencies that exist between name servers associated with each zone are called dependency graphs. System administrators' operational decisions have far reaching effects on the DNSs qualities. They need to be soundly made to create a balance between the availability, security and resilience of the system. We utilize dependency graphs to identify, detect and catalogue operational bad smells. Our method deals with smells on a high-level of abstraction using a consistent taxonomy and reusable vocabulary, defined by a DNS Operational Model. The method will be used to build a diagnostic advisory tool that will detect configuration changes that might decrease the robustness or security posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244

Identifying and improving reusability based on coupling patterns

Open Source Software (OSS) communities have not yet taken full advantage of reuse mechanisms. Typically many OSS projects which share the same application domain and topic, duplicate effort and code, without fully leveraging the vast amounts of available code. This study proposes the empirical evaluation of source code folders of OSS projects in order to determine their actual internal reuse and their potential as shareable, fine-grained and externally reusable software components by future projects. This paper empirically analyzes four OSS systems, identifies which components (in the form of folders) are currently being reused internally and studies their coupling characteristics. Stable components (i.e., those which act as service providers rather than service consumers) are shown to be more likely to be reusable. As a means of supporting replication of these successful instances of OSS reuse, source folders with similar patterns are extracted from the studied systems, and identified as externally reusable components

Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.Comment: To appear on ACM Transactions on Modeling and Computer Simulatio

COVID-19 Kindness: Patterns of Neighborly Cooperation during a Global Pandemic

The COVID-19 pandemic outbreak, which began in late 2019, brought unprecedented impact to healthcare, the economy, and social structure, and infrastructures experienced breakdowns in the initial phase. Demands in social and material needs surged, and they could not be met solely by unprepared infrastructures. Although local communities complemented them in previous disasters, physical distancing measures to prevent the spread of the virus undermined human connection, and local communities had to come up with novel ways to provide support. To develop insights from such adaptations of local communities, we explored civic activities for immediate disaster relief in multiple local communities across the United States and interviewed civic initiative organizers and attendees. In this paper, we articulate our findings into pattern language, a schema of reusable solutions for recurring problems. We present two patterns for community-based disaster recovery and discuss the effectiveness of codifying civic activities for disaster relief into patterns

Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges