A Distributed Calculus for Role-Based Access Control

Role-based access control (RBAC) is increasingly attracting attention because it reduces the complexity and cost of security administration by interposing the notion of role in the assignment of permissions to users. In this paper, we present a formal framework relying on an extension of the π calculus to study the behavior of concurrent systems in a RBAC scenario. We define a type system ensuring that the specified policy is respected during computations, and a bisimulation to equate systems. The theory is then applied to three meaningful examples, namely finding the ‘minimal’ policy to run a given system, refining a system to be run under a given policy (whenever possible), and minimizing the number of users in a given system without changing the overall behavior

Dynamic Parameter Allocation in Parameter Servers

To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed training---, but can induce severe communication overhead. To reduce communication overhead, distributed machine learning algorithms use techniques to increase parameter access locality (PAL), achieving up to linear speed-ups. We found that existing parameter servers provide only limited support for PAL techniques, however, and therefore prevent efficient training. In this paper, we explore whether and to what extent PAL techniques can be supported, and whether such support is beneficial. We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse, and experimentally compare its performance to existing parameter servers across a number of machine learning tasks. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

Variation propagation of bench vises in multi-stage machining processes

Comunicación presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)Variation propagation has been successfully modeled by the Stream of Variation (SoV) approach in multistage machining processes. However, the SoV model basically supports 3-2-1 fixtures based on punctual locators and other workholding systems such as conventional vises are not considered yet. In this paper, the SoV model is expanded to include the fixture- and datum-induced variations on workholding devices such as bench vises. The model derivation is validated through assembly and machining simulations on Computer Aided Design software. The case study analyzed shows an average error of part quality prediction between the SoV model and the CAD simulations of 0.26%

An improved cell controller for the aerospace manufacturing

The aerospace manufacturing industry is unique in that production typically focuses on high variety and quality but low volume. Existing flexible manufacturing cells are limited to certain types of machines, robots and cells which makes it difficult to introduce any changes. In this paper idea of treating machines, robots, any hardware and software as resource has been introduced. It describes the development of the Flexa Cell Coordinator (FCC), a system that is providing a solution to manage cells and their resources in a new flexible manner. It can control, organise and coordinate between cells and resources and is capable of controlling remote cells because of its distributed nature. It also provides connectivity with company systems e.g., Enterprise Resource Planner (ERP). It is extendable and capable of adding multiple cells inside the system. In FCC resources (e.g., tracker) can also be shared between cells. The paper presents its development and results of initial successful testing

Collocation Games and Their Application to Distributed Resource Management

We introduce Collocation Games as the basis of a general framework for modeling, analyzing, and facilitating the interactions between the various stakeholders in distributed systems in general, and in cloud computing environments in particular. Cloud computing enables fixed-capacity (processing, communication, and storage) resources to be offered by infrastructure providers as commodities for sale at a fixed cost in an open marketplace to independent, rational parties (players) interested in setting up their own applications over the Internet. Virtualization technologies enable the partitioning of such fixed-capacity resources so as to allow each player to dynamically acquire appropriate fractions of the resources for unencumbered use. In such a paradigm, the resource management problem reduces to that of partitioning the entire set of applications (players) into subsets, each of which is assigned to fixed-capacity cloud resources. If the infrastructure and the various applications are under a single administrative domain, this partitioning reduces to an optimization problem whose objective is to minimize the overall deployment cost. In a marketplace, in which the infrastructure provider is interested in maximizing its own profit, and in which each player is interested in minimizing its own cost, it should be evident that a global optimization is precisely the wrong framework. Rather, in this paper we use a game-theoretic framework in which the assignment of players to fixed-capacity resources is the outcome of a strategic "Collocation Game". Although we show that determining the existence of an equilibrium for collocation games in general is NP-hard, we present a number of simplified, practically-motivated variants of the collocation game for which we establish convergence to a Nash Equilibrium, and for which we derive convergence and price of anarchy bounds. In addition to these analytical results, we present an experimental evaluation of implementations of some of these variants for cloud infrastructures consisting of a collection of multidimensional resources of homogeneous or heterogeneous capacities. Experimental results using trace-driven simulations and synthetically generated datasets corroborate our analytical results and also illustrate how collocation games offer a feasible distributed resource management alternative for autonomic/self-organizing systems, in which the adoption of a global optimization approach (centralized or distributed) would be neither practical nor justifiable.NSF (CCF-0820138, CSR-0720604, EFRI-0735974, CNS-0524477, CNS-052016, CCR-0635102); Universidad Pontificia Bolivariana; COLCIENCIAS–Instituto Colombiano para el Desarrollo de la Ciencia y la Tecnología "Francisco José de Caldas

Enhanced cell controller for aerospace manufacturing

Aerospace manufacturing industry is unique in that production typically focuses on high variety and quality but extremely low volume. Manufacturing processes are also sometimes unique and not repeatable and, hence, costly. Production is getting more expensive with the introduction of industrial robots and their cells. This paper describes the development of the Flexa Cell Coordinator (FCC), a system that is providing a solution to manage resources at assembly cell level. It can control, organise and coordinate between the resources and is capable of controlling remote cells and resources because of its distributed nature. It also gives insight of a system to the higher management via its rich reporting facility and connectivity with company systems e.g., Enterprise Resource Planner (ERP). It is able to control various kinds of cells and resources (network based) which are not limited to robots and machines. It is extendable and capable of adding multiple numbers of cells inside the system. It also provides the facility of scheduling the task to avoid the deadlocking in the process. In FCC resources (e.g., tracker) can also be shared between cells