6,330 research outputs found

    Context-Aware Framework for Performance Tuning via Multi-action Evaluation

    Get PDF
    Context-aware systems perform adaptive changes in several ways. One way is for the system developers to encompass all possible context changes in a context-aware application and embed them into the system. However, this may not suit situations where the system encounters unknown contexts. In such cases, system inferences and adaptive learning are used whereby the system executes one action and evaluates the outcome to self-adapts/self-learns based on that. Unfortunately, this iterative approach is time-consuming if high number of actions needs to be evaluated. By contrast, our framework for context-aware systems finds the best action for unknown context through concurrent multi-action evaluation and self-adaptation which reduces significantly the evolution time in comparison to the iterative approach. In our implementation we show how the context-aware multi-action system can be used for a context-aware evaluation for database performance tuning

    Teaching Agile Development with DevOps in a Software Engineering and Database Technologies Practicum

    Full text link
    [EN] DevOps is a new concept for Software Engineering. Teaching DevOps can be challenging with the limited resources that are available at many universities. This paper exams how to teach of an Agile Development Methodology using a DevOps approach for the Regis University (RU) M.S. in Software Engineering and Database Technologies Practicum. With faculty support, heavy stakeholder involvement and RU Information Technology Services (Operations Support) mentoring, students were able to successfully follow the Agile Development methodology to create an application that was incoporated into the RU Web-site infrastructure.Mason, R.; Masters, W.; Stark, A. (2017). Teaching Agile Development with DevOps in a Software Engineering and Database Technologies Practicum. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 1353-1362. https://doi.org/10.4995/HEAD17.2017.5607OCS1353136

    MLOS: An Infrastructure for Automated Software Performance Engineering

    Full text link
    Developing modern systems software is a complex task that combines business logic programming and Software Performance Engineering (SPE). The later is an experimental and labor-intensive activity focused on optimizing the system for a given hardware, software, and workload (hw/sw/wl) context. Today's SPE is performed during build/release phases by specialized teams, and cursed by: 1) lack of standardized and automated tools, 2) significant repeated work as hw/sw/wl context changes, 3) fragility induced by a "one-size-fit-all" tuning (where improvements on one workload or component may impact others). The net result: despite costly investments, system software is often outside its optimal operating point - anecdotally leaving 30% to 40% of performance on the table. The recent developments in Data Science (DS) hints at an opportunity: combining DS tooling and methodologies with a new developer experience to transform the practice of SPE. In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering. MLOS enables continuous, instance-level, robust, and trackable systems optimization. MLOS is being developed and employed within Microsoft to optimize SQL Server performance. Early results indicated that component-level optimizations can lead to 20%-90% improvements when custom-tuning for a specific hw/sw/wl, hinting at a significant opportunity. However, several research challenges remain that will require community involvement. To this end, we are in the process of open-sourcing the MLOS core infrastructure, and we are engaging with academic institutions to create an educational program around Software 2.0 and MLOS ideas.Comment: 4 pages, DEEM 202

    Final Report on StratusLab Adoption

    No full text
    The StratusLab cloud distribution has been adopted by users from a wide range of scientific disciplines: astrophysics, software engineering, machine learning, high- energy physics, meteorology, and bioinformatics. In addition, there has been commercial update of the distribution for a turnkey private cloud solution aimed at SMEs and a large public deployment by Atos within the Helix Nebula initiative. Both partner and non-partner institutes have used the StratusLab distribution to provide cloud services to their users

    Prototyping Operational Autonomy for Space Traffic Management

    Get PDF
    Current state of the art in Space Traffic Management (STM) relies on a handful of providers for surveillance and collision prediction, and manual coordination between operators. Neither is scalable to support the expected 10x increase in spacecraft population in less than 10 years, nor does it support automated manuever planning. We present a software prototype of an STM architecture based on open Application Programming Interfaces (APIs), drawing on previous work by NASA to develop an architecture for low-altitude Unmanned Aerial System Traffic Management. The STM architecture is designed to provide structure to the interactions between spacecraft operators, various regulatory bodies, and service suppliers, while maintaining flexibility of these interactions and the ability for new market participants to enter easily. Autonomy is an indispensable part of the proposed architecture in enabling efficient data sharing, coordination between STM participants and safe flight operations. Examples of autonomy within STM include syncing multiple non-authoritative catalogs of resident space objects, or determining which spacecraft maneuvers when preventing impending conjunctions between multiple spacecraft. The STM prototype is based on modern micro-service architecture adhering to OpenAPI standards and deployed in industry standard Docker containers, facilitating easy communication between different participants or services. The system architecture is designed to facilitate adding and replacing services with minimal disruption. We have implemented some example participant services (e.g. a space situational awareness provider/SSA, a conjunction assessment supplier/CAS, an automated maneuver advisor/AMA) within the prototype. Different services, with creative algorithms folded into then, can fulfil similar functional roles within the STM architecture by flexibly connecting to it using pre-defined APIs and data models, thereby lowering the barrier to entry of new players in the STM marketplace. We demonstrate the STM prototype on a multiple conjunction scenario with multiple maneuverable spacecraft, where an example CAS and AMA can recommend optimal maneuvers to the spacecraft operators, based on a predefined reward function. Such tools can intelligently search the space of potential collision avoidance maneuvers with varying parameters like lead time and propellant usage, optimize a customized reward function, and be implemented as a scheduling service within the STM architecture. The case study shows an example of autonomous maneuver planning is possible using the API-based framework. As satellite populations and predicted conjunctions increase, an STM architecture can facilitate seamless information exchange related to collision prediction and mitigation among various service applications on different platforms and servers. The availability of such an STM network also opens up new research topics on satellite maneuver planning, scheduling and negotiation across disjoint entities
    • …
    corecore