A knowledge-based maintenance of legacy systems: METASOFT

The maintenance of legacy systems is a continuous problem in the field of software maintenance. To assist in the maintenance of legacy systems, we have represented the legacy systems and the maintenance requirement in a compatible manner so that the maintenance requirement can be a clue for identifying the relevant program clauses and data items in the database. For this purpose, a maintenance component is represented by the maintenance mode (add, modify or delete) and property and key words. The corresponding information about the program's clauses is extracted from the source code of the legacy program by reverse engineering. The maintenance point identification algorithm—MPI algorithm—proposed in this research is theoretically complete and relatively efficient, and is proved so empirically. Using this approach, the system METASOFT has been developed for the Korea Electric Power Corporation which uses the COBOL programs and IMS database. It turns out that the system is well accepted by the users.     

An argument for simple COMA

We present design details and some initial performance results of a novel scalable shared memory multiprocessor architecture. This architecture features the automatic data migration and replication capabilities of cache-only memory architecture (COMA) machines, without the accompanying hardware complexity. A software layer manages cache space allocation at a page-granularity — similarly to distributed virtual shared memory (DVSM) systems —leaving simpler hardware to maintain shared memory coherence at a cache line granularity.

By reducing the hardware complexity, the machine cost and development time are reduced. We call the resulting hybrid hardware and software multiprocessor architecture Simple COMA. Preliminary results indicate that the performance of Simple COMA is comparable to that of more complex contemporary all-hardware designs.     

Convergence and complexity of interpolatory—newton iteration in a Banach space

The class of interpolatory—Newton iterations is defined and analyzed for the computation of a simple zero of a non-linear operator in a Banach space of finite or infinite dimension. Convergence of the class is established.

The concepts of “informationally optimal class of algorithms” and “optimal algorithm” are formalized. For the multivariate case, the optimality of Newton iteration is established in the class of one-point iterations under an “equal cost assumption”.     

Distributed control of spatially invariant systems

We consider distributed parameter systems where the underlying dynamics are spatially invariant, and where the controls and measurements are spatially distributed. These systems arise in many applications such as the control of vehicular platoons, flow control, microelectromechanical systems (MEMS), smart structures, and systems described by partial differential equations with constant coefficients and distributed controls and measurements. For fully actuated distributed control problems involving quadratic criteria such as linear quadratic regulator (LQR), H₂ and H_∞, optimal controllers can be obtained by solving a parameterized family of standard finite-dimensional problems. We show that optimal controllers have an inherent degree of decentralization, and this provides a practical distributed controller architecture. We also prove a general result that applies to partially distributed control and a variety of performance criteria, stating that optimal controllers inherit the spatial invariance structure of the plant. Connections of this work to that on systems over rings, and systems with dynamical symmetries are discussed     

Robust Stabilization and Performance Recovery of Nonlinear Systems With Unmodeled Dynamics

In this technical note we present a time-scale separation redesign for stabilization and performance recovery of nonlinear systems with unmodeled dynamics. The unmodeled dynamics studied do not change the relative degree of the plant and are minimum phase. We design two sets of high gain filters—the first to estimate the uncertain input to the plant over a fast time-scale, and the second to force this estimate to converge to the nominal input on an intermediate time-scale. The control input then acts over the slow time-scale and guarantees that the closed-loop trajectories approach those of the nominal system.      

Efficient environment management for distributed simulation of large‐scale situated multi‐agent systems

Multi‐agent systems have been proven very effective for the modelling and simulation (M&S) of complex systems like those related to biology, engineering, social sciences and so forth. The intrinsic spatial character of many such systems leads to the definition of a situated agent. A situated agent owns spatial coordinates and acts and interacts with its peers in a hosting territory. In the context of parallel/distributed simulation of situated agent models, the territory represents a huge shared variable that requires careful handling. Frequent access by agents to territory information easily becomes a bottleneck degrading system performance and scalability. This paper proposes an original approach to modelling and distributed simulation of large‐scale situated multi‐agent systems. Time management is exploited for resolving conflicts and achieving data consistency while accessing the environment. The approach allows a simplification of the M&S tasks by making the modeller unaware of distribution concerns while ensuring the achievement of good scalability and performance during the distributed simulation. Practical aspects of the approach are demonstrated through some modelling examples based on Tileworld. Copyright © 2014 John Wiley & Sons, Ltd.     

Context-Based Operational Transformation in Distributed Collaborative Editing Systems

Operational Transformation (OT) is a consistency maintenance technique for collaborative editing systems—a special class of distributed applications for supporting human-computer-human interaction and collaboration over communication networks. The theory of causality has been the foundation of all prior OT systems, but it is inadequate to meet essential OT requirements in functionality and correctness. In this paper, we analyze the limitation of the causality theory, propose a novel theory of operation context as the new foundation for OT systems, and present a new OT algorithm—Context-based OT (COT)—which provides uniform and efficient solutions to both consistency maintenance and undo problems. The COT algorithm has been implemented and used for supporting a range of novel collaborative applications. The context theory and context vectors are potentially applicable to other distributed computing applications.     

Optimisation of application execution on dynamic systems

In this paper, we demonstrate the impact of using a dynamic (on-the-fly) performance prediction tool-set, PACE, for optimising application execution on dynamic systems. The need for steering the application execution arises from the ever-growing use of distributed and GRID systems. The unquestionable aim to overcome bottleneck problems, allocation, and performance degradation due to shared CPU time has prompted many investigations into the best way in which the performance of an application can be enhanced. In this work, we present a novel approach to dynamically optimise the performance of an application.

An example application, the FFTW (the fastest Fourier transform in the west), is used to illustrate the approach which itself is a novel method that optimises the execution of an FFT. It is shown that performance prediction can provide the same quality of information as a measurement process for application optimisation but in a fraction of the time and thus improving the overall application performance.     

H2 and H∞ control forinfinite-dimensional systems with jumps

We consider a semigroup model with jumps in the state that covers distributed parameter systems with impulse control or sampled-data distributed parameter systems with control realized through zero-order or first-order hold. We then introduce the H₂ and H_∞ problems for this system and give the solutions in terms of the solutions of Riccati equations with jumps     

Automated discovery of concise predictive rules for intrusion detection

This paper details an essential component of a multi-agent distributed knowledge network system for intrusion detection. We describe a distributed intrusion detection architecture, complete with a data warehouse and mobile and stationary agents for distributed problem-solving to facilitate building, monitoring, and analyzing global, spatio-temporal views of intrusions on large distributed systems. An agent for the intrusion detection system, which uses a machine learning approach to automated discovery of concise rules from system call traces, is described.

We use a feature vector representation to describe the system calls executed by privileged processes. The feature vectors are labeled as good or bad depending on whether or not they were executed during an observed attack. A rule learning algorithm is then used to induce rules that can be used to monitor the system and detect potential intrusions. We study the performance of the rule learning algorithm on this task with and without feature subset selection using a genetic algorithm. Feature subset selection is shown to significantly reduce the number of features used while improving the accuracy of predictions.     

Application of AI in software and information engineering

Methodologies already exist for information systems analysis and design (e.g. SSADM, JSP, Merise, etc.) and supporting tools, namely, CASE (Computer Aided Software Engineering) and RDBMS (Relational Database Management System) and/or 4GL tools. All of these tools contain a data dictionary at the core of certain facilities.

In the underlying research and in this paper, the following questions need to be addressed:

• —How can the capability of a recently available data dictionary be enhanced with some knowledge-based modules?

• —What would be the architecture of such a system, based on the data dictionary of some CASE tools?

• —How can the informal and formal modelling approach information system design be combined?

• —What sort of knowledge-representation techniques would be suitable for the different tasks during the analysis and the design of the system?

The system outlined here would work as an intelligent assistant and workbench supporting the developer, but not as an automatic programming environment.     

dOpenCL: Towards uniform programming of distributed heterogeneous multi-/many-core systems

Modern computer systems become increasingly distributed and heterogeneous by comprising multi-core CPUs, GPUs, and other accelerators. Current programming approaches for such systems usually require the application developer to use a combination of several programming models (e.g., MPI with OpenCL or CUDA) in order to exploit the system’s full performance potential. In this paper, we present dOpenCL (distributed OpenCL)—a uniform approach to programming distributed heterogeneous systems with accelerators. dOpenCL allows the user to run unmodified existing OpenCL applications in a heterogeneous distributed environment. We describe the challenges of implementing the OpenCL programming model for distributed systems, as well as its extension for running multiple applications concurrently. Using several example applications, we compare the performance of dOpenCL with MPI + OpenCL and standard OpenCL implementations.     

带有欺骗攻击的分布式滤波器型迭代学习控制

研究了一类受欺骗攻击的离散系统的分布式滤波器型迭代学习控制,对于欺骗攻击的描述采用满足Bernoulli分布的随机序列,通过构造Lyapunov函数,导出滤波误差系统均方指数稳定且满足l₂-l_∞性能指标的充分条件,设计受欺骗攻击的分布式l₂-l_∞滤波器,并通过线性矩阵不等式方法求解滤波器参数。给出分布式滤波器型迭代控制收敛的充分条件,通过仿真实例说明该设计方法的有效性。     

A distributed switch scheduling algorithm

The maximum weight matching algorithm is a high-performance scheduling algorithm for cross-bar switches. It is known that it performs optimally under heavy loads. However, its centralized nature and high computational complexity limit the algorithm’s applicability. This paper presents a randomized algorithm for distributed switch scheduling that is capable of delivering high throughput.     

Queuing Network Modeling of a Real-Time Psychophysiological Index of Mental Workload—P300 in Event-Related Potential (ERP)

Modeling and predicting of mental workload are among the most important issues in studying human performance in complex systems. Ample research has shown that the amplitude of the P300 component of event-related potential (ERP) is an effective real-time index of mental workload, yet no computational model exists that is able to account for the change of P300 amplitude in dual-task conditions compared with that in single-task situations. We describe the successful extension and application of a new computational modeling approach in modeling P300 and mental workload—a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. Based on the neurophysiological mechanisms underlying the generation of P300, the current modeling approach accurately accounts for P300 amplitude both in temporal and intensity dimensions. This approach not only has a basis in its biological plausibility but also has the ability to model and predict workload in real time and can be applied to other applied domains. Further model developments in simulating other dimensions of mental workload and its potential applications in adaptive system design are discussed.      

Computational steering of a multi-objective evolutionary algorithm for engineering design

The execution process of an evolutionary algorithm typically involves some trial and error. This is due to the difficulty in setting the initial parameters of the algorithm—especially when little is known about the problem domain. This problem is magnified when applied to many-objective optimisation, as care is needed to ensure that the final population of candidate solutions is representative of the trade-off surface. We propose a computational steering system that allows the engineer to interact with the optimisation routine during execution. This interaction can be as simple as monitoring the values of some parameters during the execution process, or could involve altering those parameters to influence the quality of the solutions produced by the optimisation process. The implementation of this steering system should provide the ability to tailor the client to the hardware available, for example providing a lightweight steering and visualisation client for use on a PDA.     

INCOME/STAR: Methodology and tools for the development of distributed information systems

INCOME/STAR is an experimental environment for the cooperative development of distributed information systems. This paper presents some of INCOME/STAR's innovative features in the area of information systems engineering: First a new type of high-level Petri nets, so-called Nested Relation/Transition nets (NR/T-nets), is described. NR/T-nets allow the modeling of concurrent processes and related complex structured objects in distributed business applications. New concepts for entity and relationship clustering were developed to support a stepwise top-down approach for Entity/Relationship based object modeling. Distributed multi-user simulation and prototyping are used for the evaluation and analysis of NR/T-nets and the involved object schema. Finally, ProMISE — an evolutionary process model for information system development — is introduced. A role-based groupware component is part of the INCOME/STAR architecture to support communication, organization and social interaction in development projects.     

Predictive resource management for meta-applications

This paper defines meta-applications as large, related collections of computational tasks, designed to achieve a specific overall result, running on a (possibly geographically) distributed, non-dedicated meta-computing platform. To carry out such applications in an industrial context, one requires resource management and job scheduling facilities (including capacity planning), to ensure that the application is feasible using the available resources, that each component job will be sent to an appropriate resource, and that everything will finish before the computing resources are needed for other purposes.

This requirement has been addressed by the PAC in three major European collaborative projects: PROMENVIR, TOOLSHED and HPC-VAO, leading to the creation of job scheduling software, in which scheduling is brought together with performance modelling of applications and systems, to provide meta-applications management facilities. This software is described, focusing on the performance modelling approach which was needed to support it.

Early results from this approach are discussed, raising some new issues in performance modelling and software deployment for meta-applications. An indication is given about ongoing work at the PAC designed to overcome current limitations and address these outstanding issues.     

A new routing algorithm for multirate rearrangeable Clos networks

In this paper, we study the problem of finding routing algorithms on the multirate rearrangeable Clos networks which use as few number of middle-stage switches as possible. We propose a new routing algorithm called the “grouping algorithm”. This is a simple algorithm which uses fewer middle-stage switches than all known strategies, given that the number of input-stage switches and output-stage switches are relatively small compared to the size of input and output switches. In particular, the grouping algorithm implies that m = 2n+(n−1)/2^k is a sufficient number of middle-stage switches for the symmetric three-stage Clos network C(n,m,r) to be multirate rearrangeable, where k is any positive integer and rn/(2^k−1).