A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

ASCR/HEP Exascale Requirements Review Report

This draft report summarizes and details the findings, results, and recommendations derived from the ASCR/HEP Exascale Requirements Review meeting held in June, 2015. The main conclusions are as follows. 1) Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude -- and in some cases greater -- than that available currently. 2) The growth rate of data produced by simulations is overwhelming the current ability, of both facilities and researchers, to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. 3) Data rates and volumes from HEP experimental facilities are also straining the ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. 4) A close integration of HPC simulation and data analysis will aid greatly in interpreting results from HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. 5) Long-range planning between HEP and ASCR will be required to meet HEP's research needs. To best use ASCR HPC resources the experimental HEP program needs a) an established long-term plan for access to ASCR computational and data resources, b) an ability to map workflows onto HPC resources, c) the ability for ASCR facilities to accommodate workflows run by collaborations that can have thousands of individual members, d) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, e) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio

Support for flexible and transparent distributed computing

Modern distributed computing developed from the traditional supercomputing community rooted firmly in the culture of batch management. Therefore, the field has been dominated by queuing-based resource managers and work flow based job submission environments where static resource demands needed be determined and reserved prior to launching executions. This has made it difficult to support resource environments (e.g. Grid, Cloud) where the available resources as well as the resource requirements of applications may be both dynamic and unpredictable. This thesis introduces a flexible execution model where the compute capacity can be adapted to fit the needs of applications as they change during execution. Resource provision in this model is based on a fine-grained, self-service approach instead of the traditional one-time, system-level model. The thesis introduces a middleware based Application Agent (AA) that provides a platform for the applications to dynamically interact and negotiate resources with the underlying resource infrastructure. We also consider the issue of transparency, i.e., hiding the provision and management of the distributed environment. This is the key to attracting public to use the technology. The AA not only replaces user-controlled process of preparing and executing an application with a transparent software-controlled process, it also hides the complexity of selecting right resources to ensure execution QoS. This service is provided by an On-line Feedback-based Automatic Resource Configuration (OAC) mechanism cooperating with the flexible execution model. The AA constantly monitors utility-based feedbacks from the application during execution and thus is able to learn its behaviour and resource characteristics. This allows it to automatically compose the most efficient execution environment on the fly and satisfy any execution requirements defined by users. Two policies are introduced to supervise the information learning and resource tuning in the OAC. The Utility Classification policy classifies hosts according to their historical performance contributions to the application. According to this classification, the AA chooses high utility hosts and withdraws low utility hosts to configure an optimum environment. The Desired Processing Power Estimation (DPPE) policy dynamically configures the execution environment according to the estimated desired total processing power needed to satisfy users’ execution requirements. Through the introducing of flexibility and transparency, a user is able to run a dynamic/normal distributed application anywhere with optimised execution performance, without managing distributed resources. Based on the standalone model, the thesis further introduces a federated resource negotiation framework as a step forward towards an autonomous multi-user distributed computing world

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing

Theoretical and Computational Basis for CATNETS - Annual Report Year 3

In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined. --Grid Computing

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Reinforcement learning for efficient network penetration testing

Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way