Towards open CSCW systems

Applications designed to support the work of groups will becoming increasingly important to future distributed systems. This paper considers the role of distributed systems within the development of cooperative systems. In particular, we focus on the need to provide Open CSCW systems and their impact on distributed systems. The work currently being undertoken in Open Distributed Systems (ODP) is used to highlight significant trends for future open CSCW systems. It will be shown that the CSCW and ODP community share mutual interests and have complementary aims and goals developed from different perspectives. Within the paper we provide a brief introduction to CSCW highlighting the requirements CSCW places on distributed systems. The development of an environment to support open CSCW systems is introduced and briefly described. Finally, the relationships between requirements and models for Open CSCW systems and the Basic Reference Model of ODP are discussed.Peer ReviewedPostprint (published version

A Tutorial on Distributed Optimization for Cooperative Robotics: from Setups and Algorithms to Toolboxes and Research Directions

Several interesting problems in multi-robot systems can be cast in the framework of distributed optimization. Examples include multi-robot task allocation, vehicle routing, target protection and surveillance. While the theoretical analysis of distributed optimization algorithms has received significant attention, its application to cooperative robotics has not been investigated in detail. In this paper, we show how notable scenarios in cooperative robotics can be addressed by suitable distributed optimization setups. Specifically, after a brief introduction on the widely investigated consensus optimization (most suited for data analytics) and on the partition-based setup (matching the graph structure in the optimization), we focus on two distributed settings modeling several scenarios in cooperative robotics, i.e., the so-called constraint-coupled and aggregative optimization frameworks. For each one, we consider use-case applications, and we discuss tailored distributed algorithms with their convergence properties. Then, we revise state-of-the-art toolboxes allowing for the implementation of distributed schemes on real networks of robots without central coordinators. For each use case, we discuss their implementation in these toolboxes and provide simulations and real experiments on networks of heterogeneous robots

Random Walks in Random Environments

Random walks provide a simple conventional model to describe various transport processes, for example propagation of heat or diffusion of matter through a medium. However, in many practical cases the medium is highly irregular due to defects, impurities, fluctuations etc., and it is natural to model this as random environment. In the random walks context, such models are referred to as Random Walks in Random Environments (RWRE). This is a relatively new chapter in applied probability and physics of disordered systems, initiated in the 1970s. Early interest was motivated by some problems in biology, crystallography and metal physics, but later applications have spread through numerous areas. After 30 years of extensive work, RWRE remain a very active area of research, which has already led to many surprising discoveries. The goal of this article is to give a brief introduction to the beautiful area of RWRE. The principal model to be discussed is a random walk with nearest-neighbor jumps in independent identically distributed (i.i.d.) random environment in one dimension, although we shall also comment on some extensions and generalizations. The focus is on rigorous results; however, heuristics is used freely to motivate the ideas and explain the approaches and proofs. In a few cases, sketches of the proofs have been included, which should help the reader to appreciate the flavor of results and methods.Comment: A review article in the Encyclopedia of Mathematical Physics (Elsevier, 2006). http://www.elsevier.com/wps/find/bookdescription.cws_home/705128/descriptio

Simulation System for the Wendelstein 7-X Safety Control System

The Wendelstein 7-X (W7-X) Safety Instrumented System (SIS) ensures personal safety and investment protection. The development and implementation of the SIS are based on the international safety standard for the process industry sector, IEC 61511. The SIS exhibits a distributed and hierarchical organized architecture consisting of a central Safety System (cSS) on the top and many local Safety Systems (lSS) at the bottom. Each technical component or diagnostic system potentially hazardous for the staff or for the device is equipped with an lSS. The cSS is part of the central control system of W7-X. Whereas the lSSs are responsible for the safety of each individual component, the cSS ensures safety of the whole W7-X device. For every operation phase of the W7-X experiment hard- and software updates for the SIS are mandatory. New components with additional lSS functionality and additional safety signals have to be integrated. Already established safety functions must be adapted and new safety functions have to be integrated into the cSS. Finally, the safety programs of the central and local safety systems have to be verified for every development stage and validated against the safety requirement specification. This contribution focuses on the application of a model based simulation system for the whole SIS of W7-X. A brief introduction into the development process of the SIS and its technical realization will be give followed by a description of the design and implementation of the SIS simulation system using the framework SIMIT (Siemens). Finally, first application experiences of this simulation system for the preparation of the SIS for the upcoming operation phase OP 1.2b of W7-X will be discussed

Studies of CMS data access patterns with machine learning techniques

This thesis presents a study of the Grid data access patterns in distributed analysis in the CMS experiment at the LHC accelerator. This study ranges from the deep analysis of the historical patterns of access to the most relevant data types in CMS, to the exploitation of a supervised Machine Learning classification system to set-up a machinery able to eventually predict future data access patterns - i.e. the so-called dataset “popularity” of the CMS datasets on the Grid - with focus on specific data types. All the CMS workflows run on the Worldwide LHC Computing Grid (WCG) computing centers (Tiers), and in particular the distributed analysis systems sustains hundreds of users and applications submitted every day. These applications (or “jobs”) access different data types hosted on disk storage systems at a large set of WLCG Tiers. The detailed study of how this data is accessed, in terms of data types, hosting Tiers, and different time periods, allows to gain precious insight on storage occupancy over time and different access patterns, and ultimately to extract suggested actions based on this information (e.g. targetted disk clean-up and/or data replication). In this sense, the application of Machine Learning techniques allows to learn from past data and to gain predictability potential for the future CMS data access patterns. Chapter 1 provides an introduction to High Energy Physics at the LHC. Chapter 2 describes the CMS Computing Model, with special focus on the data management sector, also discussing the concept of dataset popularity. Chapter 3 describes the study of CMS data access patterns with different depth levels. Chapter 4 offers a brief introduction to basic machine learning concepts and gives an introduction to its application in CMS and discuss the results obtained by using this approach in the context of this thesis

OpTiX-II: A Software Environment for MCDM based on Distributed and Parallel Computing

The intention of the paper is to give an introduction to the OpTiX-II Software Environment, which supports the parallel and distributed solution of decision problems which can be represented as mathematical nonlinear programming tasks. First, a brief summary of nonsequential solution concepts for this class of decision problems on multiprocessor systems will be given. The focus of attention will be put on coarse-grained parallelization and its implementation on multi-computer clusters. The conceptual design objectives for the OpTiX-II Software Environment will be presented as well as the implementation on a workstation cluster, a transputer system and a multiprocessor workstation (shared memory). The OpTiX-II system supports the steps from the formulation of decision problems to their solution on networks of (parallel) computers. In order to demonstrate the use of OpTiX-II, the solution of a decision problem from the field of structural design is discussed and some numerical test results are supplied