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Journal articles on the topic '4606 Distributed computing and systems software'

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Published: 10 January 2023

Last updated: 28 January 2023

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1

Lundstrom, S. F., and E. E. Swartzlander. "Foreword Advances in Distributed Computing Systems." IEEE Transactions on Software Engineering SE-11, no. 10 (October 1985): 1092–96. http://dx.doi.org/10.1109/tse.1985.231856.

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2

Boffey, T. B. "Location of Software in Distributed Computing Systems." Journal of the Operational Research Society 40, no. 10 (October 1989): 863. http://dx.doi.org/10.2307/2583395.

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3

Boffey, T. B. "Location of Software in Distributed Computing Systems." Journal of the Operational Research Society 40, no. 10 (October 1989): 863–70. http://dx.doi.org/10.1057/jors.1989.154.

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4

Yau, Stephen S., Xiaoping Jia, and Doo-Hwan Bae. "Software design methods for distributed computing systems." Computer Communications 15, no. 4 (May 1992): 213–24. http://dx.doi.org/10.1016/0140-3664(92)90104-m.

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5

Wong, Kenneth F., and Mark Franklin. "Checkpointing in Distributed Computing Systems." Journal of Parallel and Distributed Computing 35, no. 1 (May 1996): 67–75. http://dx.doi.org/10.1006/jpdc.1996.0069.

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6

Fatoohi, R. A. "Performance evaluation of communication software systems for distributed computing." Distributed Systems Engineering 4, no. 3 (September 1997): 169–75. http://dx.doi.org/10.1088/0967-1846/4/3/006.

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7

Kulik, Lars. "Mobile Computing Systems Programming: A Graduate Distributed Computing Course." IEEE Distributed Systems Online 8, no. 5 (May 2007): 4. http://dx.doi.org/10.1109/mdso.2007.27.

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8

Denning, Peter J. "Systems abstractions." Communications of the ACM 65, no. 4 (April 2022): 22–24. http://dx.doi.org/10.1145/3517218.

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9

Morganti, M. "Communications in distributed fault-tolerant computing systems." Journal of Systems and Software 6, no. 1-2 (May 1986): 213–16. http://dx.doi.org/10.1016/0164-1212(86)90042-7.

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10

Kim, Tai-hoon, and Wai-chi Fang. "Special section: Grid/distributed computing systems security." Future Generation Computer Systems 25, no. 3 (March 2009): 351. http://dx.doi.org/10.1016/j.future.2008.10.001.

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11

Mansouri, Houssem, and Al Sakib Khan Pathan. "Checkpointing distributed computing systems: an optimisation approach." International Journal of High Performance Computing and Networking 15, no. 3/4 (2019): 202. http://dx.doi.org/10.1504/ijhpcn.2019.10027729.

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12

Mansouri, Houssem, and Al Sakib Khan Pathan. "Checkpointing distributed computing systems: an optimisation approach." International Journal of High Performance Computing and Networking 15, no. 3/4 (2019): 202. http://dx.doi.org/10.1504/ijhpcn.2019.106109.

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13

Rus, Primož, Boris Štok, and Nikolaj Mole. "Parallel computing with load balancing on heterogeneous distributed systems." Advances in Engineering Software 34, no. 4 (April 2003): 185–201. http://dx.doi.org/10.1016/s0965-9978(02)00141-2.

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14

Rudall, Brian H. "Distributed computing initiatives." Kybernetes 34, no. 6 (July 2005): 757–65. http://dx.doi.org/10.1108/03684920510595463.

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15

Zhou, H. B., and L. Richter. "Very fast distributed spreadsheet computing." Journal of Systems and Software 25, no. 2 (May 1994): 185–92. http://dx.doi.org/10.1016/0164-1212(94)90005-1.

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16

STUCKENSCHMIDT, HEINER. "Foreword: ontologies for distributed systems." Knowledge Engineering Review 18, no. 3 (September 2003): 193–95. http://dx.doi.org/10.1017/s0269888904000013.

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The benefits of using ontologies have been recognised in many areas such as knowledge and content management, electronic commerce and recently the emerging field of the Semantic Web. These new applications can be seen as a great success of research in ontologies. On the other hand, moving into real application comes with new challenges that need to be addressed on a principled level rather than for specific applications. This special issue will be devoted to less well-explored topics that have come into focus recently as a response to the new problems we face when trying to use ontologies in heterogeneous distributed environments. These environments include the use of ontologies in peer-to-peer and pervasive computing systems.

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17

Yamauchi, Yukiko. "Distributed Computing Theory for Molecular Robot Systems." New Generation Computing 38, no. 2 (May 2020): 325–40. http://dx.doi.org/10.1007/s00354-020-00092-1.

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18

Korotkikh, Victor, and Galina Korotkikh. "On principles in engineering of distributed computing systems." Soft Computing 12, no. 2 (May 23, 2007): 201–6. http://dx.doi.org/10.1007/s00500-007-0191-x.

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19

WICKE, DANIEL. "DATA REPROCESSING ON WORLDWIDE DISTRIBUTED SYSTEMS." International Journal of Modern Physics A 20, no. 16 (June 30, 2005): 3880–82. http://dx.doi.org/10.1142/s0217751x05027874.

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The DØ experiment faces many challenges in terms of enabling access to large datasets for physicists on four continents. The strategy for solving these problems on worldwide distributed computing clusters is presented. Since the beginning of Run II of the Tevatron (March 2001) all Monte-Carlo simulations for the experiment have been produced at remote systems. For data analysis, a system of regional analysis centers (RACs) was established which supply the associated institutes with the data. This structure, which is similar to the tiered structure foreseen for the LHC was used in Fall 2003 to reprocess all DØ data with a much improved version of the reconstruction software. This makes DØ the first running experiment that has implemented and operated all important computing tasks of a high energy physics experiment on systems distributed worldwide.

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20

Barreiro, Fernando, Doug Benjamin, Taylor Childers, Kaushik De, Johannes Elmsheuser, Andrej Filipcic, Alexei Klimentov, et al. "The Future of Distributed Computing Systems in ATLAS: Boldly Venturing Beyond Grids." EPJ Web of Conferences 214 (2019): 03047. http://dx.doi.org/10.1051/epjconf/201921403047.

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Since 2010 the Production and Distributed Analysis system (PanDA) for the ATLAS experiment at the Large Hadron Colliderhas seen big changes to accommodate new types of distributed computing resources: clouds, HPCs, volunteer computers and other external resources. While PanDA was originally designed for fairly homogeneous resources available through the Worldwide LHC Computing Grid, the new resources are heterogeneous, at diverse scales and with diverse interfaces. Up to a fifth of the resources available to ATLAS are of such new types and require special techniques for integration into PanDA. In this talk, we present the nature and scale of these resources. We provide an overview of the various challenges faced, spanning infrastructure, software distribution, workload requirements, scaling requirements, workflow management, data management, network provisioning, and associated software and computing facilities. We describe the strategies for integrating these heterogeneous resources into ATLAS, and the new software components being developed in PanDA to efficiently use them. Plans for software and computing evolution to meet the needs of LHC operations and upgrade in the long term future will be discussed.

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21

Di Girolamo, Alessandro, Federica Legger, Panos Paparrigopoulos, Alexei Klimentov, Jaroslava Schovancová, Valentin Kuznetsov, Mario Lassnig, et al. "Operational Intelligence for Distributed Computing Systems for Exascale Science." EPJ Web of Conferences 245 (2020): 03017. http://dx.doi.org/10.1051/epjconf/202024503017.

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In the near future, large scientific collaborations will face unprecedented computing challenges. Processing and storing exabyte datasets require a federated infrastructure of distributed computing resources. The current systems have proven to be mature and capable of meeting the experiment goals, by allowing timely delivery of scientific results. However, a substantial amount of interventions from software developers, shifters and operational teams is needed to efficiently manage such heterogeneous infrastructures. A wealth of operational data can be exploited to increase the level of automation in computing operations by using adequate techniques, such as machine learning (ML), tailored to solve specific problems. The Operational Intelligence project is a joint effort from various WLCG communities aimed at increasing the level of automation in computing operations. We discuss how state-of-the-art technologies can be used to build general solutions to common problems and to reduce the operational cost of the experiment computing infrastructure.

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22

Pan, Lei, Ming Kin Lai, Koji Noguchi, Javid J. Huseynov, Lubomir F. Bic, and Michael B. Dillencourt. "Distributed Parallel Computing Using Navigational Programming." International Journal of Parallel Programming 32, no. 1 (February 2004): 1–37. http://dx.doi.org/10.1023/b:ijpp.0000015563.36375.17.

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23

Ozturan, Can, and Dan Grigoras. "Guest Editorial: Parallel and Distributed Computing." International Journal of Parallel Programming 39, no. 5 (February 25, 2011): 582–83. http://dx.doi.org/10.1007/s10766-011-0166-5.

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24

Chiun-Chieh, Hsu, and Wang Sheng-De. "Heuristic task assignment for distributed computing systems." Information Sciences 62, no. 1-2 (July 1992): 65–86. http://dx.doi.org/10.1016/0020-0255(92)90025-4.

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25

Krämer, Bernd J., and Thomas Koch. "Distributed Systems Management Software-in-the-Loop." International Journal of Software Engineering and Knowledge Engineering 08, no. 01 (March 1998): 55–76. http://dx.doi.org/10.1142/s0218194098000066.

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IT experts expect open distributed processing to become the predominant computing infrastructure in the late nineties. All computer supported work places of large enterprises and organizations will then be networked and will be integrated into cross-regional and cross-sector business and information processes. The size and complexity of such applications, the local autonomy, distribution and heterogeneity of participating subsystems, and their asynchronous interaction, however, require new architectures, strategies, and tools for their technical management. In previous work we placed a production rule interpreter into the monitoring, decision, control action loop to provide flexible, operational semantics of well-understood management policies. In this article we extend this work in two directions. First we map the structure and dynamic behavior of policies into a graph representation. This semantic representation enables a systematic prediction of the effects of policy executions and allows for a better impact analysis in case of policy changes. Then we introduce a declarative event definition mechanism. It supports a causal and temporal correlation of individual events and serves to instantiate and adapt a predefined generic event handler to the specific needs of the actual management application. Such event handlers join in the interaction between monitoring agents and policy interpreter. By event correlation they may reduce the number of events triggering management actions significantly and help to filter secondary events.

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26

Zhang, Cui, Brian R. Becker, Dave Peticolas, Ronald A. Olsson, and Karl N. Levitt. "LVT: a layered verification technique for distributed computing systems." Software Testing, Verification and Reliability 9, no. 2 (June 1999): 107–33. http://dx.doi.org/10.1002/(sici)1099-1689(199906)9:2<107::aid-stvr176>3.0.co;2-y.

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27

Alnoman, Ali, Shree Krishna Sharma, Waleed Ejaz, and Alagan Anpalagan. "Emerging Edge Computing Technologies for Distributed IoT Systems." IEEE Network 33, no. 6 (November 2019): 140–47. http://dx.doi.org/10.1109/mnet.2019.1800543.

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28

Toporkov, V. V. "Supporting schedules of resource co-allocation for distributed computing in scalable systems." Programming and Computer Software 34, no. 3 (May 2008): 160–72. http://dx.doi.org/10.1134/s0361768808030043.

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29

Krauter, Klaus, Rajkumar Buyya, and Muthucumaru Maheswaran. "A taxonomy and survey of grid resource management systems for distributed computing." Software: Practice and Experience 32, no. 2 (2002): 135–64. http://dx.doi.org/10.1002/spe.432.

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30

Tan, K. C., A. Tay, and J. Cai. "Design and implementation of a distributed evolutionary computing software." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 33, no. 3 (August 2003): 325–38. http://dx.doi.org/10.1109/tsmcc.2003.817359.

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31

Khan, Samee U. "The Curious Case of Distributed Systems and Continuous Computing." IT Professional 18, no. 2 (March 2016): 4–7. http://dx.doi.org/10.1109/mitp.2016.24.

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32

Satyanarayanan, R., and C. R. Muthukrishnan. "Multiple Instance Resource Allocation in Distributed Computing Systems." Journal of Parallel and Distributed Computing 23, no. 1 (October 1994): 94–100. http://dx.doi.org/10.1006/jpdc.1994.1122.

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33

Chlebus, Bogdan, Bhaskar Krishnamachari, and Sotiris Nikoletseas. "Preface for Special Issue “Distributed Computing in Sensor Systems”." Ad Hoc Networks 9, no. 6 (August 2011): 985–86. http://dx.doi.org/10.1016/j.adhoc.2010.11.001.

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34

Lee, HwaMin, DooSoon Park, HeonChang Yu, and Giyeol Lee. "FRASystem: fault tolerant system using agents in distributed computing systems." Cluster Computing 14, no. 1 (July 17, 2009): 15–25. http://dx.doi.org/10.1007/s10586-009-0095-x.

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35

KIM, K. H. (KANE). "REAL-TIME OBJECT-ORIENTED DISTRIBUTED SOFTWARE ENGINEERING AND THE TMO SCHEME." International Journal of Software Engineering and Knowledge Engineering 09, no. 02 (April 1999): 251–76. http://dx.doi.org/10.1142/s0218194099000164.

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Real-time (RT) object-oriented (OO) distributed computing is a form of RT distributed computing realized with a distributed computer system structured in the form of an object network. Several approaches proposed in recent years for extending the conventional object structuring scheme to suit RT applications, are briefly reviewed. Then the approach to RT OO distributed computing which the author and his collaborators have been establishing in recent years will be reviewed in more detail. The approach named the TMO (Time-triggered Message-triggered Object) structuring scheme was formulated with the goal of instigating a quantum productivity jump in the design of complex real-time computing systems (RTCS's). The TMO scheme is intended to facilitate the pursuit of a new paradigm in designing RTCS's which is to realize real-time computing with a common and general design style that does not alienate the main-stream computing industry and yet to allow system engineers to confidently produce certifiable RTCS's for safety-critical applications. The TMO structuring scheme is a syntactically simple but semantically powerful extension of the conventional object structuring approaches and as such, its support tools can be based on various well-established OO programming languages such as C++ and JAVA and on ubiquitous commercial RT operating system kernels. The scheme enables a great reduction of the designer's efforts in guaranteeing timely service capabilities of application systems. Also, the scheme is applicable to structuring of not only complex distributed RTCS's but also application environment descriptors/simulators and requirement specifications.

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36

Endres, Christoph, Andreas Butz, and Asa MacWilliams. "A Survey of Software Infrastructures and Frameworks for Ubiquitous Computing." Mobile Information Systems 1, no. 1 (2005): 41–80. http://dx.doi.org/10.1155/2005/654215.

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In this survey, we discuss 29 software infrastructures and frameworks which support the construction of distributed interactive systems. They range from small projects with one implemented prototype to large scale research efforts, and they come from the fields of Augmented Reality (AR), Intelligent Environments, and Distributed Mobile Systems. In their own way, they can all be used to implement various aspects of the ubiquitous computing vision as described by Mark Weiser [60]. This survey is meant as a starting point for new projects, in order to choose an existing infrastructure for reuse, or to get an overview before designing a new one. It tries to provide a systematic, relatively broad (and necessarily not very deep) overview, while pointing to relevant literature for in-depth study of the systems discussed.

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37

CHOI, M., N. KIM, and S. MAENG. "A New Single Image Architecture for Distributed Computing Systems." IEICE Transactions on Communications E90-B, no. 11 (November 1, 2007): 3034–41. http://dx.doi.org/10.1093/ietcom/e90-b.11.3034.

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38

Cheung, Lap-sun, and Yu-kwok Kwok. "On Load Balancing Approaches for Distributed Object Computing Systems." Journal of Supercomputing 27, no. 2 (February 2004): 149–75. http://dx.doi.org/10.1023/b:supe.0000009320.90845.0c.

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39

Barabash, O., and V. Kolumbet. "MULTIAGENT APPROACH TO COMPUTER MANAGEMENT IN A HETEROGENEOUS DISTRIBUTED COMPUTER ENVIRONMENT." Системи управління, навігації та зв’язку. Збірник наукових праць 1, no. 67 (April 1, 2022): 38–42. http://dx.doi.org/10.26906/sunz.2022.1.038.

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The reliability of modern computing control systems in a heterogeneous distributed computing environment, along with efficiency, survivability, security, control efficiency, is an important component of their quality. Increasingly, these systems fall into the category of "critical", have an absolute impact on the activities of organizations and enterprises within which they operate. The loss of such systems, even for a short time, leads to serious problems related to loss of income, unforeseen costs, downtime of production and personnel, loss of time, and sometimes man-made disasters. As you know, the greatest impact on the reliability of control systems has the reliability and fault tolerance of a set of software and hardware. Therefore, solving problems related to improving the reliability of the software part of the systems is the most urgent task. Currently, significant results have been obtained in the field of evaluation and forecasting of reliability indicators of elements and typical software packages at the stage of their design; a large number of methods known to algorithms and programs are known; a number of normative documents on project reliability assessment have been developed. However, the task of real-time reliability assessment, when accurate and operational accounting of a number of factors is required, has not been sufficiently solved. To solve the problem of multi-agent approach to computing control in a heterogeneous distributed computing environment used methods of systems analysis, set theory - to develop models of task distribution, models of tasks and computing resources, general systems theory - to study and develop methods of task distribution, logic-theory theory. for modeling computational processes. The article considers a multi-agent approach to computing control in a heterogeneous distributed computing environment. The algorithm is based on the use of economic mechanisms to regulate the supply and demand of resources in the computing environment. The architecture of the multi-agent approach and the functions of the agents are described. Particular attention is paid to calculating the reliability of the task plan based on the logical-probabilistic method.

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40

Di Modica, Giuseppe, and Orazio Tomarchio. "A Hierarchical Hadoop Framework to Process Geo-Distributed Big Data." Big Data and Cognitive Computing 6, no. 1 (January 6, 2022): 5. http://dx.doi.org/10.3390/bdcc6010005.

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In the past twenty years, we have witnessed an unprecedented production of data worldwide that has generated a growing demand for computing resources and has stimulated the design of computing paradigms and software tools to efficiently and quickly obtain insights on such a Big Data. State-of-the-art parallel computing techniques such as the MapReduce guarantee high performance in scenarios where involved computing nodes are equally sized and clustered via broadband network links, and the data are co-located with the cluster of nodes. Unfortunately, the mentioned techniques have proven ineffective in geographically distributed scenarios, i.e., computing contexts where nodes and data are geographically distributed across multiple distant data centers. In the literature, researchers have proposed variants of the MapReduce paradigm that obtain awareness of the constraints imposed in those scenarios (such as the imbalance of nodes computing power and of interconnecting links) to enforce smart task scheduling strategies. We have designed a hierarchical computing framework in which a context-aware scheduler orchestrates computing tasks that leverage the potential of the vanilla Hadoop framework within each data center taking part in the computation. In this work, after presenting the features of the developed framework, we advocate the opportunity of fragmenting the data in a smart way so that the scheduler produces a fairer distribution of the workload among the computing tasks. To prove the concept, we implemented a software prototype of the framework and ran several experiments on a small-scale testbed. Test results are discussed in the last part of the paper.

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41

Mondal, Ranjan Kumar, Payel Ray, Enakshmi Nandi, Biswajit.Biswas, Manas Kumar Sanyal, and Debabrata Sarddar. "Load Balancing of Unbalanced Assignment Problem With Hungarian Method." International Journal of Ambient Computing and Intelligence 10, no. 1 (January 2019): 46–60. http://dx.doi.org/10.4018/ijaci.2019010103.

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The cloud computing presents a type of assignments and systems which occupy distributed resources to execute a role in a distributed way. Cloud computing make use of the online systems on the web to assist the implementation of complicated assignments; that need huge-scale computation. It was said with the intention of in our living world; we can find it challenging to balance workloads of cloud computing among assignments (jobs or tasks) and systems (machines or nodes), so the majority of the time we have to promote a condition to unbalanced assignment problems (unequal task allocations). The present article submits a new technique to solve the unequal task allocation problems. The technique is offered in an algorithmic model and put into practice on the several groups of input to investigate the presentation and usefulness of the works. An evaluation is prepared with the presented approach. It makes sure that the proposed approach provides a better outcome by comparing with some other existing algorithms.

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42

Li, Wei, Zhiyuan Han, Jian Shen, Dandan Luo, Bo Gao, and Jin Xie. "Distributed AI embedded cluster for real-time video analysis systems with edge computing." MATEC Web of Conferences 355 (2022): 03036. http://dx.doi.org/10.1051/matecconf/202235503036.

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Herein, on the basis of a distributed AI cluster, a real-time video analysis system is proposed for edge computing. With ARM cluster server as the hardware platform, a distributed software platform is constructed. The system is characterized by flexible expansion, flexible deployment, data security, and network bandwidth efficiency, which makes it suited to edge computing scenarios. According to the measurement data, the system is effective in increasing the speed of AI calculation by over 20 times in comparison with the embedded single board and achieving the calculation effect that matches GPU. Therefore, it is considered suited to the application in heavy computing power such as real-time AI computing.

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43

Konoplev, A. S., and M. O. Kalinin. "Access control system for distributed computing networks." Automatic Control and Computer Sciences 50, no. 8 (December 2016): 664–68. http://dx.doi.org/10.3103/s0146411616080095.

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44

Neff, R. K. "Data bases, compound objects, and networked workstations: Beyond distributed computing (Abstract)." ACM SIGMOD Record 16, no. 3 (December 1987): 1. http://dx.doi.org/10.1145/38714.38767.

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45

Li, Hongchao, Tao Shen, Fenhua Bai, and Bei Gong. "Blockchain-Assisted Distributed Fog Computing Control Flow Attestation." Security and Communication Networks 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.1155/2022/6128155.

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The control flow hijacking attack poses a serious threat to the integrity of the software. The attacker exploits the loophole to hijack the control flow of the running program to achieve the purpose of the attack. Remote control flow attestation is a method for embedded devices to ensure the integrity of the software. With the continuous development of Internet of Things (IoT) technology, embedded devices have exploded. None of the existing control flow attestation schemes can adapt to the real-time attestation requests of such massive embedded devices. This paper proposes a blockchain-assisted distributed fog computing control flow attestation scheme BDFCFA to deal with this scenario. The scheme uses a simplified control flow representation model, which can effectively represent the control flow of the program and reduce the runtime overhead of the prover in the attestation process. We use SGX technology to protect the integrity and confidentiality of verifier and prover data during the attestation process. Our proposed bidirectional control flow attestation protocol based on the elliptic curve can greatly protect the communication security between verifiers and provers without incurring excessive performance overhead and communication cost. We evaluate the performance of BDFCFA through the SNU real-time benchmark and demonstrate that BDFCFA has better performance. Finally, compared to the existing remote control flow attestation scheme, the results show that BDFCFA has the highest security.

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46

Shukur, Hanan, Subhi Zeebaree, Rizgar Zebari, Diyar Zeebaree, Omar Ahmed, and Azar Salih. "Cloud Computing Virtualization of Resources Allocation for Distributed Systems." Journal of Applied Science and Technology Trends 1, no. 3 (June 27, 2020): 98–105. http://dx.doi.org/10.38094/jastt1331.

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Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

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47

Zheng, Weiwei, Zhili Wang, Haoqiu Huang, Luoming Meng, and Xuesong Qiu. "SPSRG: a prediction approach for correlated failures in distributed computing systems." Cluster Computing 19, no. 4 (September 13, 2016): 1703–21. http://dx.doi.org/10.1007/s10586-016-0633-2.

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48

Dzitac, Ioan, and Boldur E. Bărbat. "Artificial Intelligence + Distributed Systems = Agents." International Journal of Computers Communications & Control 4, no. 1 (March 1, 2009): 17. http://dx.doi.org/10.15837/ijccc.2009.1.2410.

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The connection with Wirth’s book goes beyond the title, albeit confining the area to modern Artificial Intelligence (AI). Whereas thirty years ago, to devise effective programs, it became necessary to enhance the classical algorithmic framework with approaches applied to limited and focused subdomains, in the context of broad-band technology and semantic web, applications - running in open, heterogeneous, dynamic and uncertain environments-current paradigms are not enough, because of the shift from programs to processes. Beside the structure as position paper, to give more weight to some basic assertions, results of recent research are abridged and commented upon in line with new paradigms. Among the conclusions: a) Nondeterministic software is unavoidable; its development entails not just new design principles but new computing paradigms. b) Agent-oriented systems, to be effectual, should merge conventional agent design with approaches employed in advanced distributed systems (where parallelism is intrinsic to the problem, not just a mean to speed up).

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Cronje, G. A., and W. H. Steeb. "Genetic Algorithms in a Distributed Computing Environment Using PVM." International Journal of Modern Physics C 08, no. 02 (April 1997): 327–44. http://dx.doi.org/10.1142/s012918319700028x.

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Abstract:

The Parallel Virtual Machine (PVM) is a software system that enables a collection of heterogeneous computer systems to be used as a coherent and flexible concurrent computation resource. We show that genetic algorithms can be implemented using a Parallel Virtual Machine and C++. Problems with constraints are also discussed.

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Debnath, Hillol, Mohammad A. Khan, Nafize R. Paiker, Xiaoning Ding, Narain Gehani, Reza Curtmola, and Cristian Borcea. "The Moitree middleware for distributed mobile-cloud computing." Journal of Systems and Software 157 (November 2019): 110387. http://dx.doi.org/10.1016/j.jss.2019.07.089.

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