Distributed intelligence for multi-camera visual surveillance

Latest advances in hardware technology and state of the art of computer vision and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. The paper proposes a multi-agent architecture for the understanding of scene dynamics merging the information streamed by multiple cameras. A typical application would be the monitoring of a secure site, or any visual surveillance application deploying a network of cameras. Modular software (the agents) within such architecture controls the different components of the system and incrementally builds a model of the scene by merging the information gathered over extended periods of time. The role of distributed artificial intelligence composed of separate and autonomous modules is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. Results are presented to support the choice of a distributed architecture, and to prove that scene interpretation can be incrementally and efficiently built by modular software.     

Fifth generation and VLSI architectures

Most Western Governments (USA, Japan, EEC, etc.) have now launched national programmes to develop computer systems for use in the 1990s. These so-called Fifth Generation computers are viewed as “knowledge” processing systems which support the symbolic computation underlying Artificial Intelligence applications. The major driving force in Fifth Generation computer design is to efficiently support very high level programming languages (i.e. VHLL architecture).

Historycally, however, commercial VHLL architectures have been largely unsuccesful. The driving force in computer designs has principally been advances in hardware which at the present time means architectures to exploit very large scale integration (i.e. VLSI architecture).

This paper examines VHLL architectures and VLSI architectures and their probable influences on Fifth Generation computers. Interestingly the major problem for both architecture classes is parallelism; how to orchestrate a single parallel computation so that it can be distributed across an ensemble of processors.     

Workload balancing in distributed crowd simulations: the partitioning method

The simulation of large crowds of autonomous agents with a realistic behavior is still a challenge for several computer research communities. Distributed architectures can provide scalability to crowd simulations, but they require the use of efficient partitioning methods. Although convex hulls have been shown as very efficient structures for crowd partitioning, providing efficient workload balancing to large scale simulations is still an open issue. In this paper, we propose the integration of a workload balancing technique for crowd simulations within a partitioning method based on convex hulls. The region-based balancing technique reassigns agents to servers using a criterion of distance. The performance evaluation results show that this technique ensures the saturation avoidance of the servers in an homogeneous distributed system. This feature can increase the scalability of crowd simulations.     

An efficient distributed randomized algorithm for solving large dense symmetric indefinite linear systems

Randomized algorithms are gaining ground in high-performance computing applications as they have the potential to outperform deterministic methods, while still providing accurate results. We propose a randomized solver for distributed multicore architectures to efficiently solve large dense symmetric indefinite linear systems that are encountered, for instance, in parameter estimation problems or electromagnetism simulations. The contribution of this paper is to propose efficient kernels for applying random butterfly transformations and a new distributed implementation combined with a runtime (PaRSEC) that automatically adjusts data structures, data mappings, and the scheduling as systems scale up. Both the parallel distributed solver and the supporting runtime environment are innovative. To our knowledge, the randomization approach associated with this solver has never been used in public domain software for symmetric indefinite systems. The underlying runtime framework allows seamless data mapping and task scheduling, mapping its capabilities to the underlying hardware features of heterogeneous distributed architectures. The performance of our software is similar to that obtained for symmetric positive definite systems, but requires only half the execution time and half the amount of data storage of a general dense solver.     

Modeling and simulating software acquisition process architectures

In this paper, we describe our efforts to support the modeling and simulation of processes associated with software system acquisition activities. Software acquisition is generally a multi-organization endeavor concerned with the funding, management, engineering, system integration, deployment and long-term support of large software systems. We first describe our approach supporting the modeling and simulation of software acquisition processes using a software process architecture (SPA). We then introduce how we support the distribution, concurrent execution and interoperation of multiple software process simulations using the high-level architecture (HLA) and run-time infrastructure (RTI) to address the complexity of software acquisition process architectures. To illustrate this, we provide examples from the design and prototyping of a Web-based environment that supports the modeling and simulation of acquisition process architectures. This environment thus serves as a new kind of software process test-bed that can demonstrate and support experiments incorporating multiple software process simulation systems that interoperate in a distributed and concurrent manner across a network.     

Guest Editors' Introduction: Interaction of Many-Core Computer Architecture and Operating Systems

Rapid changes in platform hardware resources with the evolution of many-core architectures will require a fundamental reexamination of mainstream system-software design decisions to support multiple cores and to efficiently manage on-chip hardware resources shared among the multiple cores. In turn, the evolution of many-core processor architectures will be successfully sustained by the new capabilities and features added to the system software, perhaps while requiring substantial support from hardware. The guest editors introduce five articles on the interaction of computer architecture and operating systems for this special issue of IEEE Micro.     

An interface to a reliable packet delivery service for parallelsystems

Modern distributed memory parallel computers provide hardware support for the efficient and reliable delivery of interprocessor messages. This facility needs to be accessed by lightweight protocols that do not waste the performance of the underlying hardware; the heavyweight layering techniques traditionally used in distributed systems are wholly inappropriate. A low-level communication interface is therefore presented which exploits modern architectures effectively, while maintaining a good match to existing parallel programming environments. The interface defines mechanisms to access an asynchronous reliable packet delivery service. It permits messaging protocols to be efficiently synthesized by considering the activity at their end-points alone. This arrangement effectively decouples the implementation of protocols from low-level architectural features, and hence aids the portability of parallel programming environments. Furthermore, the interface allows the communication network to be shared by multiple programming paradigms, giving additional flexibility over existing systems     

Efficient Hardware/Software Implementation of an Adaptive Neuro-Fuzzy System

This paper describes the development of efficient hardware/software (HW/SW) neuro-fuzzy systems. The model used in this work consists of an adaptive neuro-fuzzy inference system modified for efficient HW/SW implementation. The design of two different on-chip approaches are presented: a high-performance parallel architecture for offline training and a pipelined architecture suitable for online parameter adaptation. Details of important aspects concerning the design of HW/SW solutions are given. The proposed architectures have been implemented using a system-on-a-programmable-chip. The device contains an embedded-processor core and a large field programmable gate array (FPGA). The processor provides flexibility and high precision to implement the learning algorithms, while the FPGA allows the development of high-speed inference architectures for real-time embedded applications.     

A new paradigm for distributed,integrated, real-time control systems

To exploit the potential offered by integrated intelligent control systems, it is essential that underlying architectures are available that are able to integrate not only hardware and software, but also human beings into the control loop. By studying the way that a human workforce operates, this paper suggests how a human systems analogy can be drawn up that highlighted some fundamental differences between the way that humanbased, and computer-based systems operate. One of the key differences is that humans are able to reason not only logically, but also in terms of time. This paper addresses the philosphy behind the DENIS architecture — a distributed architecture that enables this high level of compatability between autonomous intelligent agents.     

ANOMALY DETECTION IN DISTRIBUTED COMPUTER COMMUNICATION SYSTEMS

Anomaly detection is a basic functionality of intrusion detection systems. The aim of such systems in distributed computer communication systems is to recognize and notify about various events that influence a system's security. In a gain to assure efficiency, flexibility, and a quality of detection of systems security violation in a distributed environment, required detection systems should be responsive, adaptive, proactive, and less centralized than those currently deployed. Such required properties are offered by agents and multiagent systems, i.e., agent-based technology has the continuously increasing potential to offer a solution to the growing problem of designing intelligent, efficient, and flexible management systems. An agent-based approach offers the potential to develop advanced and effective distributed, network-based strategies replacing traditional node-based approaches by more perspective network-based approaches.

This article is devoted to present various architectures of anomaly detection systems, which may be implemented as multiagent systems supporting the classification of observed activities as normal or abnormal. Some simple example presents hierarchical architecture of a distributed anomaly detection system, which may be implemented in the form of a multiagent decision supporting system.     

An architecture for real-time distributed artificial intelligent systems

This paper introduces a new architecture for a real-time distributed artificial intelligence system: DENIS—a Dynamic Embedded Noticeboard Information System. The fundamental idea underlying the architecture draws heavily upon a distributed human system analogy, as seen, for example, in the workplace. The aim of DENIS is to provide a simple, meaningful means by which autonomous intelligent agents can cooperate and coordinate their actions in order to enhance the reliability and effectiveness of a real-time distributed control system. Based on a human paradigm, the architecture inherently allows for the control of an intelligent agent to be taken over by a human operator, yet still to maintain consistency in the distributed system. The key to the thinking in this new approach is to try to model how humans work together, and to implement this in a distributed architecture. One of the main issues raised is that humans owe much of their flexibility to their ability to reason, not only logically, but also in terms of time.     

An overview of Dynamic Software Product Line architectures and techniques: Observations from research and industry

Over the last two decades, software product lines have been used successfully in industry for building families of systems of related products, maximizing reuse, and exploiting their variable and configurable options. In a changing world, modern software demands more and more adaptive features, many of them performed dynamically, and the requirements on the software architecture to support adaptation capabilities of systems are increasing in importance. Today, many embedded system families and application domains such as ecosystems, service-based applications, and self-adaptive systems demand runtime capabilities for flexible adaptation, reconfiguration, and post-deployment activities. However, as traditional software product line architectures fail to provide mechanisms for runtime adaptation and behavior of products, there is a shift toward designing more dynamic software architectures and building more adaptable software able to handle autonomous decision-making, according to varying conditions. Recent development approaches such as Dynamic Software Product Lines (DSPLs) attempt to face the challenges of the dynamic conditions of such systems but the state of these solution architectures is still immature. In order to provide a more comprehensive treatment of DSPL models and their solution architectures, in this research work we provide an overview of the state of the art and current techniques that, partially, attempt to face the many challenges of runtime variability mechanisms in the context of Dynamic Software Product Lines. We also provide an integrated view of the challenges and solutions that are necessary to support runtime variability mechanisms in DSPL models and software architectures.     

PipeRench: a reconfigurable architecture and compiler

With the proliferation of highly specialized embedded computer systems has come a diversification of workloads for computing devices. General-purpose processors are struggling to efficiently meet these applications' disparate needs, and custom hardware is rarely feasible. According to the authors, reconfigurable computing, which combines the flexibility of general-purpose processors with the efficiency of custom hardware, can provide the alternative. PipeRench and its associated compiler comprise the authors' new architecture for reconfigurable computing. Combined with a traditional digital signal processor, microcontroller or general-purpose processor, PipeRench can support a system's various computing needs without requiring custom hardware. The authors describe the PipeRench architecture and how it solves some of the pre-existing problems with FPGA architectures, such as logic granularity, configuration time, forward compatibility, hard constraints and compilation time     

CORBA-Based Analysis of Multi Agent Behavior

An agent is a computer software that is capable of taking independent action on behalf of its user or owner. It is an entity with goals, actions and domain knowledge, situated in an environment. Multiagent systems comprises of multiple autonomous, interacting computer software, or agents. These systems can successfully emulate the entities active in a distributed environment. The analysis of multiagent behavior has been studied in this paper based on a specific board game problem similar to the famous problem of GO. In this paper a framework is developed to define the states of the multiagent entities and measure the convergence metrics for this problem. An analysis of the changes of states leading to the goal state is also made. We support our study of multiagent behavior by simulations based on a CORBA framework in order to substantiate our findings.     

Enabling simulation interoperability

Over the past years a series of architectures have addressed the need to link multiple simulations. These efforts have been driven primarily by the desire to reuse existing "best of breed" simulations in new combinations to avoid developing any single, monolithic architecture with the impossible goal of meeting all simulation needs. The US Department of Defense began developing the high level architecture (HLA) for distributed computer simulation systems. The high level architecture addresses the need to link multiple computer simulation systems. HLA separates the data model from the architecture's functions for exchanging information.     

Performance comparison and analysis of reactive and planning-based control architectures for manufacturing

Although numerous distributed architectures ranging from hierarchical to non-hierarchical (or heterarchical) have been proposed for the control of manufacturing systems, very little research has focused on quantitative comparisons of these architectures. In this paper, an objective comparison of two architectures, each required to control the same manufacturing cell, is presented. The objective of this work is to gain insight into the behaviour of alternative control architectures that will ultimately be used to determine the best control architecture for a given manufacturing system. In particular, this research focuses on the role of planning horizon in control architecture design to determine whether intelligent control agents should plan ahead or simply react to change in their environment.     

Generating CAM aspect-oriented architectures using Model-Driven Development

Aspect-Oriented Software Development promotes the separation of those concerns that cut across several components and/or are tangled with the base functionality of a component, through all phases of the software lifecycle. The benefit of identifying these crosscutting concerns (aspects) at the architectural level in particular is to improve the architecture design and its subsequent evolution, before moving onto detailed design and implementation. However, software architects are not usually experts on using specific AO architecture notations. Therefore, the aim of this paper is to provide support to define and specify aspect-oriented (AO) architectures using non-AO ones as the source. We will use the Model-Driven Development approach to transform a component-based architecture model into an AO architecture model. The CAM (component and aspect model) model and the DAOP–ADL language are the proposals used for modelling and specifying AO architectures. We will show how we automated part of the process and the tool support.     

New trends in parallel and distributed simulation: From many-cores to Cloud Computing

Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are happening at the ends of the computing spectrum: at the “small” scale, processors now include an increasing number of independent execution units (cores), at the point that a mere CPU can be considered a parallel shared-memory computer; at the “large” scale, the Cloud Computing paradigm allows applications to scale by offering resources from a large pool on a pay-as-you-go model. Multi-core processors and Clouds both require applications to be suitably modified to take advantage of the features they provide. Despite laying at the extreme of the computing architecture spectrum – multi-core processors being at the small scale, and Clouds being at the large scale – they share an important common trait: both are specific forms of parallel/distributed architectures. As such, they present to the developers well known problems of synchronization, communication, workload distribution, and so on. Is parallel and distributed simulation ready for these challenges? In this paper, we analyze the state of the art of parallel and distributed simulation techniques, and assess their applicability to multi-core architectures or Clouds. It turns out that most of the current approaches exhibit limitations in terms of usability and adaptivity which may hinder their application to these new computing architectures. We propose an adaptive simulation mechanism, based on the multi-agent system paradigm, to partially address some of those limitations. While it is unlikely that a single approach will work well on both settings above, we argue that the proposed adaptive mechanism has useful features which make it attractive both in a multi-core processor and in a Cloud system. These features include the ability to reduce communication costs by migrating simulation components, and the support for adding (or removing) nodes to the execution architecture at runtime. We will also show that, with the help of an additional support layer, parallel and distributed simulations can be executed on top of unreliable resources.