The Non-preemptive Scheduling of Periodic Tasks upon Multiprocessors

The non-preemptive scheduling of periodic task systems upon processing platforms comprised of several identical processors is considered. The exact problem has previously been proven intractable even upon single processors; sufficient conditions are presented here for determining whether a given periodic task system will meet all deadlines if scheduled non-preemptively upon a multiprocessor platform using the earliest-deadline first scheduling algorithm. Supported in part by the National Science Foundation (Grant Nos. CCR-9988327 and ITR-0082866). Sanjoy Baruah is a professor of Computer Science at the University of North Carolina at Chapel Hill. He received his Ph.D. from the University of Texas at Austin in 1993. His research and teaching interests are in scheduling theory, real-time and safety-critical system design, and resource-allocation and sharing in distributed computing environments.     

基于动态优先级策略的最优软非周期任务调度算法

周期任务与非周期任务的混合调度是实时调度研究的一个重要方向 通过定义“调度”和“逆调度” ,对实时周期任务集在使用EDF算法调度时的可挪用时间进行分析 ,求出了周期任务集在使用EDF调度时的最大可挪用时间 在此基础上 ,提出用于缩短非周期任务响应时间和周转时间的调度算法———ISA(idlestealingalgorithm) ISA算法充分使用最大可挪用时间 ,在保证周期任务满足最后期限的同时能取得非周期任务的最优响应时间和周转时间 证明了ISA算法的最优性 ,并使用仿真实验进行了性能验证     

Analysis of window-constrained execution time systems

Feasibility tests for hard real-time systems provide information about the schedulability of the task set. However, this information is a yes or a no answer, that is, whether the task set achieves the test or not. From the real-time system design point of view, having more information available would be useful. For example, how much the computation time can vary without jeopardising the system feasibility. This work specifically provides methods to determine off-line how much a task can increase its computation time, by maintaining the system feasibility under a dynamic priority scheduling. The extra time can be determined not only in all the task activations, but in n of a window of m invocations. This is what we call a window-constrained execution time system. The results presented in this work can be used in all kinds of real-time systems: fault tolerance management, imprecise computation, overrun handling, control applications, etc. Patricia Balbastre is an assistant professor of Computer Engineering. She graduated in Electronic Engineering at the Technical University of Valencia, Spain, in 1998. And the Ph.D. degree in Computer Science at the same university in 2002. Her main research interests include real-time operating systems, dynamic scheduling algorithms and real-time control. Ismael Ripoll received the B.S. degree from the Polytechnic University of Valencia, Spain, in 1992; the Ph.D. degree in Computer Science at the Polytechnic University of Valencia, Spain, in 1996. Currently he is Professor in the DISCA Department of the same University. His research interests include embedded and real-time operating systems. Alfons Crespo is Professor of the Department of Computer Engineering of the Technical University of Valencia. He received the PhD in Computer Science from the Technical University of Valencia, Spain, in 1984. He held the position of Associate professor in 1986 and full Professor in 1991. He leads the group of Industrial Informatics and has been the responsible of several European and Spanish research projects. His main research interest include different aspects of the real-time systems (scheduling, hardware support, scheduling and control integration, …). He has published more than 60 papers in specialised journals and conferences in the area of real-time systems.     

Feasibility Analysis of Real-Time Periodic Tasks with Offsets

The problem of feasibility analysis of asynchronous periodic task sets, where tasks can have an initial offset, is known to be co-NP-complete in the strong sense. A sufficient pseudo-polynomial test has been proposed by Baruah, Howell and Rosier, which consists in analyzing the feasibility of the corresponding synchronous task set (i.e. all offsets are set equal to 0). If the test gives a positive result, then the original asynchronous task set is feasible; else, no definitive answer can be given. In many cases, this sufficient test is too pessimistic, i.e. it gives no response for many feasible task sets.In this paper, we present a new sufficient pseudo-polynomial test for asynchronous periodic task sets. Our test reduces the pessimism by explicitely considering the offsets in deriving a small set of critical arrival patterns. We show, trough a set of extensive simulations, that our test outperforms the previous sufficient test.Rodolfo Pellizzoni received the Laurea degree in Computer Engineering from the Università di Pisa and the Diploma degree from the Scuola Superiore SantAnna, in 2004. He is presently a Ph.D. student in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His main research interests are in real-time operating systems, scheduling theory and resource-allocation in distributed and multiprocessor systems.Giuseppe Lipari graduated in Computer Engineering at the University of Pisa in 1996, and received the Ph.D. degree in Computer Engineering from Scuola Superiore SantAnna in 2000. During 1999, he was a visiting student at University of North Carolina at Chapel Hill, collaborating with professor S.K. Baruah and professor K. Jeffay on real-time scheduling. Currently, he is assistant professor of Operating Systems with Scuola Superiore SantAnna. His main research activities are in real-time scheduling theory and its application to real-time operating systems, soft real-time systems for multimedia applications and component-based real-time systems.     

Scheduling algorithms based on weakly hard real-time constraints

The problem of scheduling weakly hard real-time tasks is addressed in this paper.The paper first analyzes the characters of μ-pattern and weakly hard real-time constraints,then,presents two scheduling algorithms,Meet Any Algorithm and Meet Row Algorithm,for weakly hard real-time systems.Different from traditional algorithms used to guarantee deadlines,MeetAny Algorithm and Meet Row Algorithm can guarantee both deadlines and constraints.Meet Any Algorithm and Meet Row Algorithm try to find out the probabilities of tasks breaking constraints and increase task‘s priority in advance,but not till the last moment.Simulation results show that these two algorithms are better than other scheduling algorithms dealing with constraints and can largely decrease worst-case computation time of real-time tasks.     

无线传感器网络混合任务的系统级低功耗实时调度算法研究

现有的无线传感器网络节能研究主要集中在无线通信上面,针对传感器节点CPU节能的研究还不够充分.本文以多任务、多跳网络传输的实时无线传感器网络为研究对象,从节点的调度分析出发,在保证其实时性的前提下,结合动态电压调节技术,提出两个低功耗实时调度算法:(1)基于任务最坏执行时间来计算任务CPU速度的静态低功耗调度算法;(2)将任务在实际执行过程中产生的空闲时间,分配给余下将要执行的任务,进一步调整其电压等级,即动态低功耗调度算法.仿真实验结果表明,本文提出的算法能够有效降低节点CPU能耗.     

A Technique for Adaptive Scheduling of Soft Real-Time Tasks

A number of multimedia and process control applications can take advantage from the ability to adapt soft real-time load to available computational capacity. This capability is required, for example, to react to changed operating conditions as well as to ensure graceful degradation of an application under transient overloads. In this paper, we illustrate a novel adaptive scheduling technique based on rate modulation of a set of periodic tasks in a range of admissible rates. By casting constraints on rate ranges in a linear programming formulation, several adaptation policies can be considered, along with additional constraints reflecting various application requirements. The paper investigates the effectiveness of rate modulation strategies both on simulated task sets and on real experiments. Partial support for this research has been provided by MURST, Italy (PRIN project ISIDE on “Dependable reactive computing systems for industrial applications” and special project “RoboCare” funded by L. 449/97), and by ASI, Agenzia Spaziale Italiana (contract I/R/134/00). Giuseppe Beccari received the Laurea degree in Electronic Engineering in 1993, and the Ph.D. in Information Technology in 1999, both from the University of Parma, Italy. In 1995 he was visiting scholar at the Technical University of Delft, Holland, and at the Laboratoire de Robotique de Paris, France. In 1999 he was employed by CSELT (Centro Studi E Laboratori Telecomunicazioni, currently TILAB, the Telecom Italia Group research center). In 2002 he moved to a spin off company involved in the EUROSAM/FSAF (Future Surface-to-Air Family self defense missile system) project. While his current professional duties focus more on software development and team coordination, dr. Beccari still enjoys investigating real-time scheduling issues and technology. Stefano Caselli received a Laurea degree in Electronic Engineering in 1982 and the Ph.D. degree in Computer and Electronic Engineering in 1987, both from the University of Bologna, Italy. In 1989-90 he has been visiting scholar at the University of Florida. From 1990 to 1999 he has held research fellow and associate professor positions at the University of Parma, Italy. He is now professor of Computer Engineering at the University of Parma, where he is also director of the Laboratory of Robotics and Intelligent Machines (RIMLab). His current research interests include development of autonomous and remotely operated robot systems, service robotics, and real-time systems. Francesco Zanichelli received a Laurea degree in Electronic Engineering in 1987 from the University of Bologna, Italy and the Ph.D. degree in Information Technologies in 1994 from the University of Parma, Italy. Since 1996 he has been an Assistant Professor with the Department of Information Engineering of the University of Parma where he is currently teaching Operating Systems, Information Systems and Multimedia Systems courses. His current research interests include distributed multimedia architectures and protocols, real-time systems, security and Quality of Service technologies for wireless networks, as well as service-oriented Grid middleware.     

提高软非周期任务响应性能的调度算法

实时环境中常常既包含硬周期任务,又包含软非周期任务,引入一种改进软非周期实时任务响应时间的算法.已有的解决混合任务调度问题的方法都是基于速率单调（Rate Monotonic）策略的,其中从周期任务“挪用时间”的算法被证明优于其他所有算法.但是,速率单调算法限制了处理器的使用率,从而使周期任务的可“挪用”时间受到限制.最后期限驱动（Deadline Driven）策略DD可使潜在的处理器利用率达到100%.新算法正是在周期任务的调度中适当加入了DD策略,从而使非周期任务的响应时间得以缩短.仿真实验的结果表明,这种算法的性能优于已有的所有算法,而由它所带来的额外开销却不算很高.     

From eager to lazy constrained data acquisition: A general framework

^*1 Constraint Satisfaction Problems (CSPs)¹⁷⁾ are an effective framework for modeling a variety of real life applications and many techniques have been proposed for solving them efficiently. CSPs are based on the assumption that all constrained data (values in variable domains) are available at the beginning of the computation. However, many non-toy problems derive their parameters from an external environment. Data retrieval can be a hard task, because data can come from a third-party system that has to convert information encoded with signals (derived from sensors) into symbolic information (exploitable by a CSP solver). Also, data can be provided by the user or have to be queried to a database. For this purpose, we introduce an extension of the widely used CSP model, called Interactive Constraint Satisfaction Problem (ICSP) model. The variable domain values can be acquired when needed during the resolution process by means of Interactive Constraints, which retrieve (possibly consistent) information. A general framework for constraint propagation algorithms is proposed which is parametric in the number of acquisitions performed at each step. Experimental results show the effectiveness of the proposed approach. Some applications which can benefit from the proposed solution are also discussed. This paper is an extended and revised version of the paper presented at IJCAI’99 (Stockholm, August 1999)⁴⁾. Paola Mello, Ph.D.: She received her degree in Electronic Engineering from University of Bologna, Italy, in 1982 and her Ph.D. degree in Computer Science in 1989. Since 1994 she is full Professor. She is enrolled, at present, at the Faculty of Engineering of the University of Bologna where she teaches Artificial Intelligence. Her research activity focuses around: programming languages, with particular reference to logic languages and their extensions towards modular and object-oriented programming; artificial intelligence; knowledge representation; expert systems. Her research has covered implementation, application and theoretical aspects and is presented in several national and international publications. She took part to several national (Progetti Finalizzati e MURST) and international (UE) research projects in the context of computational logic. Michela Milano, Ph.D.: She is a Researcher in the Department of Electronics, Computer Science and Systems at the University of Bologna. From the same University she obtained her master degree in 1994 and her Ph.D. in 1998. In 1999 she had a post-doc position at the University of Ferrara. Her research focuses on Artificial Intelligence, Constraint Satisfaction and Constraint Programming. In particular, she worked on using and extending the constraint-based paradigm for solving real-life problems such as scheduling, routing, object recognition and planning. She has served on the program committees of several international conferences in the area of Constraint Satisfaction and Programming, and she has served as referee in several related international journals. Marco Gavanelli: He is currently a Ph.D. Student in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer Science Engineering in 1998 at the University of Bologna, Italy. His research interest include Artificial Intelligence, Constraint Logic Programming, Constraint Satisfaction and visual recognition. He is a member of ALP (the Association for Logic Programming) and AI^*IA (the Italian Association for Artificial Intelligence). Evelina Lamma, Ph.D.: She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on logic programming languages, Artificial Intelligence and software engineering. She was co-organizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. She is a member of the Executive Committee of the Italian Association for Artificial Intelligence (AI^*IA). Currently, she is Full Professor at the University of Ferrara, where she teaches Artificial Intelligence and Fondations of Computer Science. Massimo Piccardi, Ph.D.: He graduated in electronic engineering at the University of Bologna, Italy, in 1991, where he received a Ph.D. in computer science and computer engineering in 1995. He currently an assistant professor of computer science with the Faculty of Engineering at the University of Ferrara, Italy, where he teaches courses on computer architecture and microprocessor systems. Massimo Piccardi participated in several research projects in the area of computer vision and pattern recognition. His research interests include architectures, algorithms and benchmarks for computer vision and pattern recognition. He is author of more than forty papers on international scientific journals and conference proceedings. Dr. Piccardi is a member of the IEEE, the IEEE Computer Society, and the International Association for Pattern Recognition — Italian Chapter. Rita Cucchiara, Ph.D.: She is an associate professor of computer science at the Faculty of Engineering at the University of Modena and Reggio Emilia, Italy, where she teaches courses on computer architecture and computer vision. She graduated in electronic engineering at the University of Bologna, Italy, in 1989 and she received a Ph.D. in electronic engineering and computer science from the same university in 1993. From 1993 to 1998 she been an assistant professor of computer science with the University of Ferrara, Italy. She participated in many research projects, including a SIMD parallel system for vision in the context of an Italian advanced research program in robotics, funded by CNR (the Italian National Research Council). Her research interests include architecture and algorithms for computer vision and multimedia systems. She is author of several papers on scientific journals and conference proceedings. She is member of the IEEE, the IEEE Computer Society, and the International Association for Pattern Recognition — Italian Chapter.     

强实时环境下调度非周期任务的时限寻优方法

文章提出了强实时环境下调度弱时限非周期任务的时限寻优方法（ＤＯＡ）,该方法在保证周期任务和偶发性任务满足时限要求的前提下,使非周期任务的响应时间达到最优。它还可根据实时应用的需要对算法的执行性能和计算复杂度进行折衷调整。仿真实验表明,ＤＯＡ与现有的动态调度算法相比,使非周期任务响应时间更短,同时它收敛快,额外开销小,计算复杂度低,实现方便,因此是强实时环境下对周期任务与非周期任务进行混合调度的一种较好的方法。     

实时系统中的非定期任务调度算法综述

非定期任务调度是实时系统中的一个重要研究内容，综述了实时系统中非定期任务调度算法的研究与进展，按照这些算法的特征分为基于服务器的算法与基于空闲时间的算法两大类别，并着重对每个类别中的不同算法的特征与性能进行了分析，通过对这些算法的比较与分析，希望为实时系统的研究与开发者提供有意义的参考，最后还给出了非定期任务调度进一步研究的思路与建议。     

单机实时系统中的一种混合型实时容错调度算法

目前研究单机实时系统的调度算法文章大多只能调度单一类型的任务。本文在PKSA算法的基础上，建立了一种混合型实时容错模型，提出一种调度算法不仅可以调度有容错需求的周期任务，同时也能够调度无容错需求的周期任务和非周期非实时任务，实现了调度混合型任务的目的。     

单机实时系统中的一种混合型实时容错调度算法

目前研究单机实时系统的调度算法文章大多只能调度单一类型的任务。本文在PKSA算法的基础上,建立了一种混合型实时容错模型,提出一种调度算法不仅可以调度有容错需求的周期任务,同时也能够调度无容错需求的周期任务和非周期非实时任务,实现了调度混合型任务的目的。     

QoS Management Through Adaptive Reservations

Reservation based (RB) scheduling is a class of scheduling algorithms that is well-suited for a large class of soft real-time applications. They are based on a bandwidth abstraction, meaning that a task is given the illusion of executing on a dedicated slower processor. In this context, a crucial design issue is deciding the bandwidth that each task should receive. The point we advocate is that, in presence of large fluctuations on the computation requirements of the tasks, it can be a beneficial choice to dynamically adapt the bandwidth based on QoS measurements and on the subsequent application of feedback control (adaptive reservations).In this paper, we present two novel contributions to this research area. First, we propose three new control algorithms inspired to the ideas of stochastic control. Second, we present a flexible and modular software architecture for adaptive reservations. An important feature of this architecture is that it is realised by means of a minimally invasive set of modifications to the Linux kernel.This work has been partially supported by the European OCERA IST-2001-35102 and RECSYS IST-2001-32515 projects.Luca Abeni is a Ph.D. in Computer Engineering at the Scuola Superiore SantAnna of Pisa (Italy). He graduated in Computer Engineering at the University of Pisa in 1998, and received a Ph.D. in Computer Engineering at the Scuola SuperioreS. Anna of Pisa in 2002. During 2000 he was a visiting student at the Carnegie Mellon University (Pittsburgh, PA), working with Prof. Ragunathan Rajkumar on resource reservation algorithms for real-time kernels. During 2001 he was a visiting student a Oregon Graduate Institute (Portland, OR) working with Prof. Jonathan Walpole on the support for time-sensitive applications in the Linux kernel. He has been working in Broadsat S.R.L. since 2003, developing audio/video streaming solutions and IPTV applications.Tommaso Cucinotta got the degree in Computer Engineering at the University of Pisa (Italy) in 2000. He got the Ph.D. inComputer Engineering at the Scuola Superiore SantAnna(SSSA) of Pisa in 2004 with a thesis titled Issues in authentication by means of smart-card devices. He held a course on cryptography in the International Master on Software Engineering organized at SSSA in 2002 and 2004. At the moment, he cooperates in research activities at the Scuola Superiore S. Anna in the areas of computer security and Quality of Service control for soft real-time systems.Giuseppe Lipari graduated in Computer Engineering at the University of Pisa in 1996, and received the Ph.D. degree in Computer Engineering from Scuola Superiore SantAnna in 2000. During 1999, he was avisiting student at University of North Carolina at Chapel Hill, collaborating with professor S.K. Baruah and professor K. Jeffay on real-time scheduling. Currently, he is assistant professor of Operating Systems with Scuola Superiore SantAnna. His main research activities are in real-time scheduling theory and its application to real-time operating systems, soft real-time systems for multimedia applications and component-based real-time systems.Luca Marzario is a Ph.D. student in Computer Engineering at the Scuola Superiore S. Anna of Pisa, Italy. In 2002, he graduated in Computer Engineering at University of Pisa. His main research interest include real-time systems scheduling, aperiodic service mechanism, feedback-scheduling, QoS in multimedia systems, Linux kernel and Real Time Linux executives (RTAI, RTLinux).Luigi Palopoli graduated in Control Engineering at the University of Pisa in 1998 and received his Ph.D. degree in Computer Engineering in 2002 from the Scuola Superiore S. Anna, Pisa. During 2001 he was a visiting scholar at the Department of EECS, University of California at Berkeley, where he worked on design techniques for real-time controllers under the supervision of Professor Alberto Sangiovanni-Vincentelli. He is currently Assistant Professor at the Scuola Superiore S. Anna (Pisa). His main research activities include Quality of Service control, control of systems under communication and computation constraints and design of embedded systems.     

基于多处理机的混合实时任务容错调度

提出了一种混合实时任务容错调度算法．该算法采用Rate Monotonic(RM)算法完成周期任务的静态调度；采用预订处理机时间方法和Earlier Deadline First(EDF)算法动态调度非周期任务；采用主／副版本备份技术确保系统的容错能力．通过充分利用周期任务的剩余处理机时间调度非周期任务和主动备份与被动备份相结合的方法有效地减少了处理机数．仿真结果证明了算法的有效性．     

A New Approach for Scheduling of Parallelizable Tasks in Real-Time Multiprocessor Systems

In a parallelizable task model, a task can be parallelized and the component tasks can be executed concurrently on multiple processors. We use this parallelism in tasks to meet their deadlines and also obtain better processor utilisation compared to non-parallelized tasks. Non-preemptive parallelizable task scheduling combines the advantages of higher schedulability and lower scheduling overhead offered by the preemptive and non-preemptive task scheduling models, respectively. We propose a new approach to maximize the benefits from task parallelization. It involves checking the schedulability of periodic tasks (if necessary, by parallelizing them) off-line and run-time scheduling of the schedulable periodic tasks together with dynamically arriving aperiodic tasks. To avoid the run-time anomaly that may occur when the actual computation time of a task is less than its worst case computation time, we propose efficient run-time mechanisms.We have carried out extensive simulation to study the effectiveness of the proposed approach by comparing the schedulability offered by it with that of dynamic scheduling using Earliest Deadline First (EDF), and by comparing its storage efficiency with that of the static table-driven approach. We found that the schedulability offered by parallelizable task scheduling is always higher than that of the EDF algorithm for a wide variety of task parameters and the storage overhead incurred by it is less than 3.6% of the static table-driven approach even under heavy task loads.     

应用于实时系统的RMS算法的改进

本文阐述了以PLC为核心的HGJD001实验台电气控制系统，并采用上位计算机实现对实验台运行状态的通讯监测。     

队头阻塞优化的EDF可重构任务调度算法

针对最早截止时刻优先(earliest deadline first,EDF)调度算法队头阻塞任务导致资源利用率和配置端口复用率低下的问题,提出一种队头阻塞优化的EDF实时调度算法.通过定义无效阻塞任务并引入无效阻塞任务丢弃策略,提前判定和丢弃无法调度成功的任务,以利于后续任务调度;通过定义队头阻塞任务最早布局成功时刻...     

Approximation algorithm for periodic real-time tasks with workload-dependent running-time functions

This paper addresses the problem of resource allocation for distributed real-time periodic tasks, operating in environments that undergo unpredictable changes and that defy the specification of meaningful worst-case execution times. These tasks are supplied by input data originating from various environmental workload sources. Rather than using worst-case execution times (WCETs) to describe the CPU usage of the tasks, we assume here that execution profiles are given to describe the running time of the tasks in terms of the size of the input data of each workload source. The objective of resource allocation is to produce an initial allocation that is robust against fluctuations in the environmental parameters. We try to maximize the input size (workload) that can be handled by the system, and hence to delay possible (costly) reallocations as long as possible. We present an approximation algorithm based on first-fit and binary search that we call FFBS. As we show here, the first-fit algorithm produces solutions that are often close to optimal. In particular, we show analytically that FFBS is guaranteed to produce a solution that is at least 41% of optimal, asymptotically, under certain reasonable restrictions on the running times of tasks in the system. Moreover, we show that if at most 12% of the system utilization is consumed by input independent tasks (e.g., constant time tasks), then FFBS is guaranteed to produce a solution that is at least 33% of optimal, asymptotically. Moreover, we present simulations to compare FFBS approximation algorithm with a set of standard (local search) heuristics such as hill-climbing, simulated annealing, and random search. The results suggest that FFBS, in combination with other local improvement strategies, may be a reasonable approach for resource allocation in dynamic real-time systems. David Juedes is a tenured associate professor and assistant chair for computer science in the School of Electrical Engineering and Computer Science at Ohio University. Dr. Juedes received his Ph.D. in Computer Science from Iowa State University in 1994, and his main research interests are algorithm design and analysis, the theory of computation, algorithms for real-time systems, and bioinformatics. Dr. Juedes has published numerous conference and journal papers and has acted as a referee for IEEE Transactions on Computers, Algorithmica, SIAM Journal on Computing, Theoretical Computer Science, Information and Computation, Information Processing Letters, and other conferences and journals. Dazhang Gu is a software architect and researcher at Pegasus Technologies (NeuCo), Inc. He received his Ph.D. in Electrical Engineering and Computer Science from Ohio University in 2005. His main research interests are real-time systems, distributed systems, and resource optimization. He has published conference and journal papers on these subjects and has refereed for the Journal of Real-Time Systems, IEEE Transactions on Computers, and IEEE Transactions on Parallel and Distributed Systems among others. He also served as a session chair and publications chair for several conferences. Frank Drews is an Assistant Professor of Electical Engineering and Computer Science at Ohio Unversity. Dr. Drews received his Ph.D. in Computer Science from the Clausthal Unversity of Technolgy in Germany in 2002. His main research interests are resource management for operating systems and real-time systems, and bioinformatics. Dr. Drews has numerous publications in conferences and journals and has served as a reviewer for IEEE Transactions on Computers, the Journal of Systems and Software, and other conferences and Journals. He was Publication Chair for the OCCBIO’06 conference, Guest Editor of a Special Issue of the Journal of Systems and Software on “Dynamic Resource Management for Distributed Real-Time Systems”, organizer of special tracks at the IEEE IPDPS WPDRTS workshops in 2005 and 2006. Klaus Ecker received his Ph.D. in Theoretical Physics from the University of Graz, Austria, and his Dr. habil. in Computer Science from the University of Bonn. Since 1978 he is professor in the Department of Computer Science at the Clausthal University of Technology, Germany, and since 2005 he is visiting professor at the Ohio University. His research interests are parallel processing and theory of scheduling, especially in real time systems, and bioinformatics. Prof. Ecker published widely in the above mentioned areas in well reputed journals and proceedings of international conferences as well. He is also the author of two monographs on scheduling theory. Since 1981 he is organizing annually international workshops on parallel processing. He is associate editor of Real Time Systems, and member of the German Gesellschaft fuer Informatik (GI) and of the Association for Computing Machinery (ACM). Lonnie R. Welch received a Ph.D. in Computer and Information Science from the Ohio State University. Currently, he is the Stuckey Professor of Electrical Engineering and Computer Science at Ohio University. Dr. Welch performs research in the areas of real-time systems, distributed computing and bioinformatics. His research has been sponsored by the Defense Advanced Research Projects Agency, the Navy, NASA, the National Science Foundation and the Army. Dr. Welch has twenty years of research experience in the area of high performance computing. In his graduate work at Ohio State University, he developed a high performance 3-D graphics rendering algorithm, and he invented a parallel virtual machine for object-oriented software. For the past 15 years his research has focused on middleware and optimization algorithms for high performance computing. His research has produced three successive generations of adaptive resource management (RM) middleware for high performance real-time systems. The project has resulted in two patents and more than 150 publications. Professor Welch also collaborates on diabetes research with faculty at Edison Biotechnology Institute and on genomics research with faculty in the Department of Environmental and Plant Biology at Ohio University. Dr. Welch is a member of the editorial boards of IEEE Transactions on Computers, The Journal of Scalable Computing: Practice and Experience, and The International Journal of Computers and Applications. He is also the founder of the International Workshop on Parallel and Distributed Real-time Systems and of the Ohio Collaborative Conference on Bioinformatics. Silke Schomann graduated in 2003 with a M.Sc. in Computer Science from Clausthal University Of Technology, where she has been working as a scientific assistant since then. She is currently working on her Ph.D. thesis in computer science at the same university.     

Efficient Incremental Maintenance of Frequent Patterns with FP-Tree

Mining frequent patterns has been studied popularly in data mining area. However, little work has been done on mining patterns when the database has an influx of fresh data constantly. In these dynamic scenarios, efficient maintenance of the discovered patterns is crucial. Most existing methods need to scan the entire database repeatedly, which is an obvious disadvantage. In this paper, an efficient incremental mining algorithm, Incremental-Mining (IM), is proposed for maintenance of the frequent patterns when new incremental data come. Based on the frequent pattern tree (FP-tree) structure, IM gives a way to make the most of the things from the previous mining process, and requires scanning the original data once at most. Furthermore, IM can identify directly the differential set of frequent patterns, which may be more informative to users. Moreover, IM can deal with changing thresholds as well as changing data, thus provide a full maintenance scheme. IM has been implemented and the performance study shows it outperforms three other incremental algorithms: FUP, DB-tree and re-running frequent pattern growth (FP-growth).