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Safety-critical networks often have stringent real-time requirements; they must also be resilient to failures. In this paper, we propose the RealFlow framework that uses commodity software-defined networks (SDNs) to realize networks with end-to-end timing guarantees, while also: (a) increasing resiliency against link/switch failures and (b) increasing network utilization. The use of SDNs in this space also improves the management capabilities of the system due to the global visibility into the network. RealFlow is implemented as a northbound SDN controller application compatible with standard OpenFlow protocols with little to no runtime overheads. We demonstrate feasibility on a real hardware testbed (Pica8 SDN switches+Raspberry Pi endhosts) and a practical avionics case study. Our evaluations show that RealFlow can accommodate 63% more network flows with safety-critical guarantees when compared to current designs and up to 18% when link resiliency (via backup paths) is also considered.

Modern computational research increasingly spans multiple, geographically distributed data centers and leverages instruments, experimental facilities and a network of national and regional cyberinfrastructure (CI). Tapis is an open-source API platform developed at the Texas Advanced Computing Center at the University of Texas at Austin to increase reproducibility and minimize time-to-solution for distributed computational experiments. Core features of Tapis include data management and code execution, a fine-grained permissions system enabling objects to be saved privately, shared with individuals or “published” to a community, and provenance endpoints exposing the detailed history Tapis collects on analyses, enabling workflows to be repeated and results reproduced. In this paper, we describe the evolution of the Tapis platform, from its origins in 2008, and discuss the growth and success of the project as well as challenges and limitations that have led to a new design effort, funded by the National Science Foundation in September of 2019. We present a detailed overview of the new system, including reference architecture and new features such as support for streaming/sensor data, and we discuss some of the early science use cases driving its design. We conclude with the roadmap for future work.

In the last decade, the rise of hosted Software-as-a-Service (SaaS) application programming interfaces (APIs) across both academia and industry has exploded, and simultaneously, microservice architectures have replaced monolithic application platforms for the flexibility and maintainability they offer. These SaaS APIs rely on small, independent and reusable microservices that can be assembled relatively easily into more complex applications. As a result, developers can focus on their own unique functionality and surround it with fully functional, distributed processes developed by other specialists, which they access through APIs. The Tapis framework, a NSF funded project, provides SaaS APIs to allow researchers to achieve faster scientific results, by eliminating the need to set up a complex infrastructure stack. In this paper, we describe the best practices followed to create Tapis APIs using Python and the Stream API as an example implementation illustrating authorization and authentication with the Tapis Security Kernel, Tenants and Tokens APIs, leveraging OpenAPI v3 specification for the API definitions and docker containerization. Finally, we discuss our deployment strategy with Kubernetes, which is an emerging orchestration technology and the early adopter use cases of the Streams API service.

Brown Dog is an extensible data cyberinfrastructure, that provides a set of extensible and distributed data conversion and metadata extraction services to enable access and search within unstructured, un-curated and inaccessible research data across different domains of sciences and social science, which ultimately aids in supporting reproducibility of results. We envision that Brown Dog, as a data cyberinfrastructure, is an essential service in a comprehensive cyberinfrastructure which includes data services, high performance computing services and more that would enable scholarly research in a variety of disciplines that today is not yet possible. Brown Dog focuses on four initial use cases, specifically, addressing the conversion and extraction needs in the research areas of ecology, civil and environmental engineering, library and information science, and use by the general public. In this paper, we describe an architecture that supports contribution of data transformation tools from users, and automatic deployment of the tools as Brown Dog services in diverse infrastructures such as cloud or high performance computing (HPC) based on user demands and load on the system. We also present results validating the performance of the initial implementation of Brown Dog.

We present Brown Dog, two highly extensible services that aim to leverage any existing pieces of code, libraries, services, or standalone software (past or present) towards providing users with a simple to use and programmable means of automated aid in the curation and indexing of distributed collections of uncurated and/or unstructured data. Data collections such as these encompassing large varieties of data, in addition to large amounts of data, pose a significant challenge within modern day "Big Data" efforts. The two services, the Data Access Proxy (DAP) and the Data Tilling Service (DTS), focusing on format conversions and content based analysis/extraction respectively, wrap relevant conversion and extraction operations within arbitrary software, manages their deployment in an elastic manner, and manages job execution from behind a deliberately compact REST API. We describe both the motivation and need/scientific drivers for such services, the constituent components that allow for arbitrary software/code to be used and managed, and lastly an evaluation of the systems capabilities and scalability.

DIBBs Brown Dog is a recent cyberinfrastructure effort which aims to create two new services to aid users in the searching, accessing, and usage of digital data and provide these services in a manner that is as broadly and easily accessible as possible. At its lowest level, the Data Access Proxy (DAP) providing file format conversion capabilities and the Data Tilling Service (DTS) providing content based extractions, will be accessible via a deliberately compact REST interface. On top of this a number of client libraries and applications can and are being constructed to even further reduce the overhead of accessing the provided functionality (e.g. libraries in Javascript, Python, R, Matlab and interfaces such as bookmarklets, browser extensions, and other standalone applications). At the heart of the two services, however, is their extensibility, allowing one to potentially incorporate any library or tool as a conversion or extraction component within the services towards both the leveraging of the functionality they provide as well as the preservation of the tools themselves.

Current monitoring systems for cloud infrastructures are based on local, centralized or hierarchical model approaches such as HP Openview and ArcSight. Additionally, they do not look deep into resilience and delivering trustworthy data of its own services under crash or Byzantine failures caused by attackers or any other kind of sources. This work proposes a fault and intrusion tolerant monitoring system for cloud computing infrastructures. We assume a Byzantine failure model and use state machine replication for providing the trustworthy and resilient monitoring service.

Compute platforms for wireless sensor networks and Vehicle-to-Vehicle (V2V) communications employ random channel access for message transmission and typically suffer from limited processing capability and on-board memory on a per-application basis because of the multiple processes going on in parallel. Appending digital signatures to transmitted messages in such systems increases information reliability, but requires an intemperate use of scarce resources, more so with an increased security requirement.It thus appears imperative to tradeoff security for network performance to conserve scarce resources for a given resource constrained platform. This requires a good understanding of the communication performance of these systems. We observe that resource constraints in these systems induce complex interaction between the security and MAC layers at a node, obviating the possibility of layer specific optimizations to improve system performance for broadcast applications. For example, reducing the channel access probability at the MAC layer reduces collision probabilities, in turn increasing the verification load on the security layer. There is hence a need to take a holistic approach to dimension such systems to improve performance. In achieving this objective, we provide:• an analytical framework to model these systems with or without an impersonation attacker. This analysis also takes into consideration the possibility of multiple digital signatures being attached to a message, so that the receiver has a choice of verifying one of these.• a characterization of the stability region of the system.• an information-theoretic approach towards reliable communication of application data over these systems. We view the combination of security-MAC-security layers as an erasure channel and provide attainable rates, jointly optimizing on sampling rate and channel transmission rate.

Application of sensor networks in different fields is an interesting area to work with and has already drawn widespread attention. Since sensors have limited supply of on-board energy, efficient management of network is a compulsion in extending life of the sensor. At the same time, frequent damage to sensors and link failure occur because of the adverse environment in which they are deployed. A sensor network has to tolerate and recover from these failures themselves with no external help. In this respect, we have designed a self-stabilizing energy-aware routing protocol in a sensor network. Our protocol ensures the sensor network, starting from an arbitrary state, eventually set up reliable communication in network with minimum energy consumption and in a finite number of steps.