Advertisement

SN Computer Science

, 1:44 | Cite as

Introduction to the Special Issue on the French–Polish Collaboration in Mathematical Models of Computer Systems, Networks and Bioinformatics

  • Erol GelenbeEmail author
Editorial
Part of the following topical collections:
  1. Modelling methods in Computer Systems, Networks and Bioinformatics

Abstract

On the occasion of the celebration in 2019 of the 100th anniversary of official French–Polish Scientific Collaboration, this paper explores the origins and outcomes of a scientific collaboration that we launched in the 1980s together with the late Professor Stefan Wȩgrzyn of the Polytechnic University of Silesia in Gliwice, Poland, Founding Director of the Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences, and Fellow of the Polish Academy of Sciences. We survey the themes of this long-standing collaboration, outline the work that was accomplished, and the reasons that resulted in these themes being at its core. We outline the main scientific outcomes, and discuss the current work and projects that relate to this exemplary Franco-Polish collaboration. Finally, we introduce the papers of this Special Issue in the light of these ongoing themes.

Introduction

This paper first briefly reviews the scientific history and current status of our long-term Franco-Polish collaboration that the French-speaking Fellow of the Science Academy of Poland, the late Professor Stefan Wegrzyn (1925–2011) founder of IITIS-PAN in Gliwice, Poland, initiated with me and my team in the early 1980s.

Then the paper introduces the Special Issue on Modelling of Computer Systems, Networks and Bio-Informatics Systems.

Interestingly enough, Professor Wegrzyn’s own interests in the modeling and analysis of control systems [142] and in certain aspects of bioinformatics [140], as presented in these two books, are quite in line with the directions of the present Special Issue.

When he visited me near Paris at IRIA in Rocquencourt, France, which later became the well-known research organization INRIA, Professor Wegrzyn was already collaborating with the University of Lille. Fluent in French, he also developed collaborations with the French speaking University of Sherbrooke in Canada. For his many contributions to Poland, Professor Wegrzyn was awarded the honour of Commander of the Order of Polonia Restituta. France recognized his contributions to Franco-Polish Scientific Collaboration by making him an Officer of the Ordre des Palmes Académiques of France. His scientific recognition and links to France and French-speaking Canada won him Honoris Causa Doctorates from the University of Lille, France (1973), the University of Sherbooke in Canada (1978). In Poland, he also received Honoris Causa Doctorates from his alma mater the Polytechnic University of Silesia (1988), from the AGH University of Science and Technology of Cracow (1989), and from the Polytechnic University of Rzeszow (2004).

On his first visit with us, Professor Wegrzyn was impressed with the ideas and work on Modelling Methods for Computer Systems and Networks that we were pioneering, and was persuaded to set up a similar activity at IITIS-PAN. He was successful in this initiative, with the sustained diligence of his successor as Director of IITIS, Professor Tadeusz Czachorski, who has himself actively taken part and developed this collaboration and continued it to this day. An important element of this success was that in France I had the good fortune to train over 35 PhD students (of a total of more than 87 that I have graduated), and many of them chose a career in academia and in research. Thus, many of the methods that I had been developing in the 1970s and 1980s in France were successfully transferred to Poland and pursued in many joint papers between my former students, such as Professors Atmaca, Jean-Michel Fourneau, Ferhan Pekergin and Nihal Pekergin, and Professor Tadeusz Czachorski and his other collaborators in Poland.

In the sequel, we will first go over a certain number of areas that contributed to this collaboration, and then introduce the papers in this Special Issue.

Research on High-Performance Networks

Over the years, several international cooperative research projects were conducted by IITIS-PAN together with French research laboratories where my former students were active. In particular, my former Turkish PhD student Prof. Tülin Atmaca of the Institut National des Télécommunications organized projects running from 2009 to 2014 with the support of CNRS (France) on energy savings [75, 92, 93] on integrated access networks with Prof. Czachòrski [102].

In the period 1996–2007, they ran three projects on modelling the congestion control schemes in high-speed networks, on the performance of traffic admission and differentiated quality of serve on multi-service networks, and on load balancing based on different traffic characteristics in optical networks. In the period 1998–2000, they received a Polonium Project from Poland to study the performance modeling of congestion control in high-speed networks [8, 116, 117].

In addition, in the years 1996–1998, Prof. Atmaca and Czachòrski developed a cooperative network including France, Poland and Slovakia with the help of Dr Pavol Podharadsky of the Technical University of Bratislava, Prof. Martin Klimo of the University of Zilima in Slovakia together with IITIS-PAN and INT in Evry, France, focusing on the performance modelling of very fast networks.

Diffusion Approximations

At the time when this Franco-Polish collaboration started, one of the significant areas of work in my team in France was the development of new methods for the approximate analysis of computer systems, networks and queues, based on Diffusion Approximations [3, 57, 94] which were also discussed in books and monographs that we initially published in those years [91, 95, 96].

Andrzej Duda, who had worked with Tadeusz Czachorski in Gliwice for his Master’s thesis then joined my group at Orsay to prepare a PhD thesis, and one of the topics that I suggested to him was the matter of diffusion approximations, to which he made several noted contributions to transient analysis of queueing systems under my research supervision [47, 48, 134].

Diffusion approximations also became a favourite research topic for Prof. Czachorski who continues actively in this direction to this day, for instance, in [23, 33, 37], and also [28, 31, 32, 34, 35, 36], among many other papers, very often in close collaboration with my former PhD student Dr Ferhan Pekergin of the University of Paris-Nord, with my former PhD students Prof. Tülin Atmaca [8] and Jean-Michel Fourneau [52]. Professor Czachorski’s team has also developed related work on the performance of high-speed data networks [27, 41, 131].

In direct relation to my earlier work on average packet travel times in wireless networks [2, 65, 67], Prof Czachorski (again with Dr Ferhan Pekergin) studied analogous models to compute the probability distribution of the travel times in sensor networks [29, 30].

Yet another area of application of the diffusion models that I developed was the block loss analysis of ATM networks [88] that served as the basis for admission control for “calls” or connections [89, 90, 121]. Similar work followed between IITIS-PAN researchers and my former students [8].

Distributed Systems and Databases

The initial contacts with Poland had also resulted in contacts with other researchers, notably Professors Jacek Blazewicz and Roman Slowinski and their team who were interested in topics related to scheduling of tasks, which is relevant to many areas in computer science including system performance evaluation [14, 141].

Among them was Dr Wojciech Cellary from Poznan, with whom we started a steady collaboration which first resulted in a joint book on Distributed Databases that included Dr J. Morzy of Poznan Technical University [18]. On my side, this work was initially motivated by my earlier work in the control and information sharing in concurrent computation [97], and my interest in the performance of distributed algorithms which can be slowed down by the data that they share and the need to synchronize parallel computations [10, 21, 22].

Some of these questions were also pursued by my Polish PhD student Andrzej Duda together with my other former PhD students such as Guy Bernard, Yoram Haddad and Gilbert Harrus at Orsay with regard to the issue of time in distributed systems [12, 51].

The 1980s were also the years when we also conducted other work on the performance of multiprocessor systems [60] where the objective was to determine the speedup that could be obtained as a function of the number of available processors and other architectural aspects such as the interconnection networks used to link the processors to the memory system. However, our work also addressed the intrinsic limitations of the parallelism within the programs themselves which we had studied with my student Zhen Liu [87] who years later was the Director of the Nokia Laboratory. Similar problems were then also studied by my Polish student Andrzej Duda and Professor Czachorski [49, 50].

Database Research

I believe it was in 1986, when Professor Cellary spent a year in my laboratory at Orsay, and taught a course to replace my absence that year as a Ministerial Advisor for Science and Technology, that Wojciech built important links with members of my team.

During that time, a new and very productive collaboration was launched on Database Theory and design with my former doctoral student Geneviève Jomier, including the work in [16, 17, 19, 110], and this collaboration has lasted until a fairly recent period prior to Professor Jomier’s unfortunate and untimely death.

System Reliability and Checkpoints

Interestingly enough, the reliability of the execution of programs is crucial to multiprocessing, where very long running programs for high-performance computing (HPC) applications should be able to recover quickly from any hardware or system failures to avoid having to execute the whole program from its start. Similarly, this is also the case for databases where an “audit trail” records all the updates that have been made in the database, and when a failure occurs one should be able to avoid having to re-run the whole “history” or “audit trail” of the database, and re-start it from some recent state.

In both cases, the key technique that is used is based on checkpoints which are periodically created in these long-running programs to be able to re-establish the database or program state with low overhead after a failure. In both cases, the issue is to select an inter-checkpoint interval which minimizes system overhead. In recent work, this overhead needs to include the cost of energy.

Thus, we pioneered research in this area starting in the 1970s [58, 77, 82], and this also generated ideas for work for our Polish students and colleagues [45, 46].

We can also cite some further joint work with American colleagues regarding checkpoints in large-scale distributed systems [79, 80, 135], and further recent work being pursued in the current SDK4ED project with colleagues from Greece [130]and now Poland.

Energy Savings in ICT and Energy Packet Networks

The large amount of energy consumption by Information and Communication Technologies (ICT) [75] has resulted in the study of computer and communication systems that use renewable energy, e.g. photovoltaic [119], ambient electromagnetics [1], vibrations [20, 115] and piezo-electricity [109]. Their effectiveness depends on intermittent energy sources, converters, and energy storage to achieve steady energy flow to devices when the sources’ supply of energy is interrupted. The quality of service (QoS) and energy consumption of sensor systems, the IoT [112], Cloud Computing [11], servers [84], and embedded systems [118] are of great interest for a “greener” ICT [106].

Wireless systems [39, 81] require much research to maximize throughput maximization and minimize transmission delay with energy harvesting [108, 127, 144]. Energy cooperation can also use wireless energy exchange among devices [105]. Multiple access with intermittent data and energy [143], energy cooperation [104]. In [5], online power scheduling and delay minimization [6], transmitted data maximization [38], and throughput maximization are also studied [114, 136]. Energy savings in wired networks include measurements [98] and simulations [126].

The Energy Packet Network (EPN) model is a mathematical abstraction for interconnected processing units or data transmission nodes that receive random flows of energy and data or jobs [68, 69] based on G-Networks [54, 55, 63, 107, 120]. It has been used for the QoS analysis of systems that operate with intermittent sources of energy [76]. A related concept has also been suggested in work that originated in Japan [132, 133]. In [71], service times for forwarding packets are neglected and arrivals of EPs cause the forwarding of data packets [111]. Multihop EPN models are studied in [72] to represent the backbone of a mobile network, and related models can be found in [44]. In [145], a utility function including wasted power and the average delay of jobs is used to optimize an EPN.

Thus, in the closely paper by Jean-Michel Fourneau from the DAVID/CNRS Laboratory of the University of Versailles Saint-Quentin on “Energy Packet Networks with Interrupted Poisson Energy Arrivals and Job Balancing” that is included in this Special Issue [53], the flow of EPs is represented by an interrupted Poisson process and the job flow is balanced among servers.

High-Impact Conference Proceedings

As part of this Franco-Polish collaboration, we should mention the series of annual conferences known as ISCIS or International Symposium on Computer and Information Sciences that I started in Ankara (Turkey) in 1986 and which were held regularly since then. Many of their Proceedings have been published in the Springer Lecture Notes Series, some others by the IEEE and other academic publishers such as Nova Science Press, and they have had great measurable international impact. As examples of this impact, officially stated by Springer:
  • The Proceedings of ISCIS 2010 [86] received over 51,600 downloads shared among some 40 papers, or over 1200 downloads on average per paper, with similar results for [85].

  • The Proceedings of ISCIS 2012 published in 2013 [83] received over 39,927 downloads, or roughly 1000 per paper on average.

  • The Proceedings of ISCIS 2015 [4] received 20,477 downloads, or on average roughly 500 downloads per paper.

Of course, the downloads are cumulative over time and the older proceedings have typically accumulated more downloads.

Thus, through ongoing collaborations, often with my former students and team members, also with their Polish co-authors, many researchers from Poland have published at these conferences. At the same time, French researchers have also expanded their interactions and the variety of problems that they have worked on, thanks to the contributions of Polish researchers. They have also benefited from these conferences to establish additional international contacts due to the presence of other researchers from Europe, North America and other regions.

In addition, in 2014 [26], we proposed to take the 29th ISCIS Symposium to Poland for the first time, and with IITIS-PAN it was held in Krakow very successfully. The Springer Proceedings have obtained over 28,000 downloads since its publication or some 700 on average per paper.

This was repeated with the 31st ISCIS in Krakow which was a phenomenal success. Indeed, the papers in this conference’s Proceedings [24] have to this day been downloaded 122,965 times according to Springer Nature, the conference’s publisher, with some 2000 downloads on average per paper.

The experience was repeated in 2018, jointly with the IFIP World Computing Conference (WCC). The ISCIS 2018 Springer Proceedings [25], which were not published in Open Access due to the rules of the 2018 IFIP WCC, have nevertheless attracted over 8,827 paid downloads or over 200 per paper on average.

Let us also mention the 2018 ISCIS Cyber-Security Conference [74] that I organized, also published by Springer in Open Access mode, which has already received 48,694 downloads for a total of less than 20 papers.

Current Developments: Cybersecurity of the IoT, Dependability and Energy Savings

As we celebrate a Franco-Polish Collaboration that is focused on the Modelling and Performance Evaluation of Computer Systems and Networks that has continued uninterruptedly since the 1980s for more than 30 years, let me outline some of its current highlights.

In 2016, with my close collaborators at the Informatics and Telematics Institute of CERTH in Thessaloniki, we submitted a proposal for a 5M Euro Research and Innovation Action, the Project SerIoT, to the European Commission with the understanding that I would coordinate the project. The notification of success reached us after the UK had voted in favour of Brexit, and the UK government invoked Article 50 to leave the EU. With my collaborators, we then decided that the project should be housed in an institution that was clearly part of the EU, and the decision was taken to move it to the Institute of Theoretical and Applied Informatics (IITIS-PAN) in Gliwice, Poland, where I could coordinate it as a Professor and Foreign Fellow of the Polish Academy of Sciences.

Thus, IITIS-PAN is currently the only Institute of the Polish Academy of Sciences that is coordinating such a European Research and Innovation action.

The SerIoT project began its work in January of 2018, and started with our earlier research on using Machine Learning to dynamically enhance network security using Software Defined Networks (SDN) to implement network control functions [56]. SerIoT uses concepts from Self-Aware Networks [13, 40] and more specifically the Cognitive Packet Network routing algorithm [66, 139].

The aims, plans and first results of the project that relate to SerIoT’s cognitive and secure SDN routing engine for the Internet of Things are outlined in several recent papers [42, 73, 78, 124, 125].

Also, as part of the EU Horizon 2020 Project SDK4ED which I transferred to IITIS-PAN, we study the enhancement of dependability in embedded software and have shown how checkpoints can be used in programs to guarantee their dependability at minimum cost in terms of program execution time and overhead [130]. We also study the energy consumption minimization in software and in [100], we show how to allocate the flow of energy to energy store of servers which operate with intermittent energy so as to minimize the average response time of jobs, and how the judicious offloading of a job from a given server to another can be used to minimize the job response time. In [99], we prove the intriguing result that when the number of jobs that can be processed by one EP has a geometric distribution, then to minimize the average response time of jobs, the share of jobs that are assigned to a given server must be identical to the share of energy that is assigned to that server’s energy store, and we obtain the optimum value explicitly.

Currently, a total of five senior researchers, two research students, and two administrative staff members, are fully involved in these two research actions funded by the European Commission.

Content of this Special Issue

This Special Issue which is based on selected papers from the Conference celebrating French–Polish scientific cooperation on Mathematical Modelling of Computer Systems, Networks and Bioinformatics that was held in Paris during October 14–15, 2019.

Its full program can be found at https://projekty.iitis.pl/agenda-3, and includes papers that are grouped in several related sub-areas.

The first set of papers discuss different aspects in computer system and network modelling, and include four papers from Poland, one from France, a joint Franco-Polish paper, and two papers from our long-term visitors and collaborators from Greece and Italy. Thus, our work on G-Networks [62] leads to the results in [53], when combined with our development of the Energy Packet Network model [70] to achieve job balance and lower energy consumption in data centers. The next paper [101] addresses similar issues using measurements and analytical models.

Then in [103], the performance of massive repositories that are needed to support Smart Cities is discussed. The work in [43] uses diffusion approximations, which have been a steady theme in our collaborations [59], and the following paper [113] develops certain analytical computational methods for diffusion approximation models. The work discussed in [9] models a control issue that is specific to optical networks, while the research discussed in [7] examines how networked systems can support the specific needs of smart automated vehicles.

The next two papers focus on data science, and in [123] networked data are used to create alerts regarding international risks, while [122] describes a general purpose data analysis tool.

The third set of papers concern neuronal models, and the work in [129] uses the Random Neural Network that was introduced in [61, 64] to develop hierarchical neural networks that are designed to conduct cognitive routing that directs traffic in wireless networks that are subject to wireless radio interference and overload and loss effects, using ideas similar to those that are developed in earlier work [15, 137, 138, 139].

On the other hand, the work in [128] develops a combination of analytically based methods and simulation techniques to accelerate the discrete event simulation of very large neural networks. Finally, the last paper [146] studies inherent physical properties of networks of blood vessels, such as the relative diameters of interconnected vessels, that appear to optimize blood flow.
  1. 1.

    [53] Jean-Michel Fourneau “Modeling green data-centers and jobs balancing with Energy Packet Networks and interrupted Poisson energy arrivals”.

     
  2. 2.

    [101] Dipak Ghosal, Goldwayne Yeh, Sambit Shukla, Matthew Farrens and Jian Wu. Model Driven “Joint Optimization of Power and Latency Guarantee in Datacenter Applications”.

     
  3. 3.

    [103] Michal Gorawski and Krzysztof Grochla “Performance tests of Smart City IoT data repositories for universal linear infrastructure data and graph databases”.

     
  4. 4.

    [43] Adam Domanski, Joanna Domanska, Tadeusz Czachorski, Jerzy Klamka, Jakub Szygua and Dariusz Marek “Diffusion approximation model of TCP NewReno congestion control mechanism”.

     
  5. 5.

    [113] Dzmitry Kopats and Mikhail Matalytski “About diffusion approximation of open queueing network with limited number of customer and time-dependent service parameters”.

     
  6. 6.

    [9] Tülin Atmaca, Artur Rataj and Amira Kamli “Analysis of a frequency response of a noisy optical network for its self-adaptation”.

     
  7. 7.

    [7] Leonardo Arcari, Marco Gribaudo, Gianluca Palermo and Giuseppe Serazzi “Performance-driven Analysis for an Adaptive Car Navigation Service on HPC Systems”.

     
  8. 8.

    [123] Xiang Niu, Gyorgy Korniss and Boleslaw Szymanski “Supervised learning of the global risk network activation from media event reports”.

     
  9. 9.

    [122] Karol Niedzielewski, Maciej Marchwiany, Radoslaw Piliszek, Marek Michalewicz and Witold Rudnicki “Multidimensional feature selection and high performance ParalleX”.

     
  10. 10.

    [129] Artur Rataj “Random neural networks with hierarchical committees for improved routing in wireless mesh networks with interference”.

     
  11. 11.

    [128] Tien Cuong Phi, Alexandre Muzy and Patricia Reynaud-Bouret “Event-scheduling algorithms with Kalikov decomposition for simulating potentially infinite neuronal networks”.

     
  12. 12.

    [146] Jakub Zielinski and Jedrzej Nowosielski “Finet’s law as a special case of the generalised Murray’s law”.

     
This set of papers comprising eight papers from Poland, three from France, two from the USA, and one from Italy, illustrates the wealth of topics and exchanges from this international scientific collaboration.

Notes

Acknowledgements

This work has been funded by the European Unions Horizon 2020 Research and Innovation Programme Project SDK4ED, under Grant agreement no. 780572.

References

  1. 1.
    Abadal G, Alda J, Agust J. Electromagnetic radiation energy harvesting the rectenna based approach. Rijeka: InTech Publishing; 2014.  https://doi.org/10.5772/57118.CrossRefGoogle Scholar
  2. 2.
    Abdelrahman OH, Gelenbe E. Time and energy in team-based search. Phys Rev E. 2013;87:032125.  https://doi.org/10.1103/PhysRevE.87.032125.CrossRefGoogle Scholar
  3. 3.
    Abdelrahman OH, Gelenbe E. Search in big networks and big data. In: Mityushev V, Ruzhansky MV, editors. Analytic methods in interdisciplinary applications. Springer proceedings in mathematics and statistics. Berlin: Springer; 2015.Google Scholar
  4. 4.
    Abdelrahman OH, Gelenbe E, Görbil g, Lent R., editors. Information sciences and systems 2015—30th international symposium on computer and information sciences, ISCIS 2015, London, UK, 21–24 September 2015, Lecture notes in electrical engineering, vol. 363. Cham: Springer; 2016.  https://doi.org/10.1007/978-3-319-22635-4.Google Scholar
  5. 5.
    Arafa A, Baknina A, Ulukus S. Online fixed fraction policies in energy harvesting communication systems. IEEE Trans Wirel Commun. 2018;17(5):2975–86.  https://doi.org/10.1109/TWC.2018.2805336.CrossRefGoogle Scholar
  6. 6.
    Arafa A, Tong T, Fu M, Ulukus S, Chen W. Delay minimal policies in energy harvesting communication systems. IEEE Trans Commun. 2018;66(7):2918–30.  https://doi.org/10.1109/TCOMM.2018.2805357.CrossRefGoogle Scholar
  7. 7.
    Arcari L, Gribaudo M, Palermo G, Serazzi G. Performance-driven analysis for an adaptive car navigation service on HPC systems. Berlin: Springer; 2019 (this issue 2019).Google Scholar
  8. 8.
    Atmaca T, Czachrski T, Pekergin F. A diffusion model of the dynamic effects of closed-loop feedback control mechanisms in atm networks. Arch Inf Teor Stosowanej. 1999;11(1):41–56.Google Scholar
  9. 9.
    Atmaca T, Rataj A, Kamli A. Analysis of a frequency response of a noisy optical network for its self-adaptation. Berlin: Springer; 2019 (this issue 2019).Google Scholar
  10. 10.
    Baccelli F, Gelenbe E, Plateau B. An end-to-end approach to the resequencing problem. J ACM. 1984;31(3):474–85.MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Berl A, Gelenbe E, Di Girolamo M, Giuliani G, De Meer H, Quan DM, Pentikousis K. Energy-efficient cloud computing. Comput J. 2010;53(7):1045–51.CrossRefGoogle Scholar
  12. 12.
    Bernard G, Duda A, Haddad Y, Harrus G. Primitives for distributed computing in a heterogeneous local area network environment. IEEE Trans Softw Eng. 1989;15(12):1567–78.CrossRefGoogle Scholar
  13. 13.
    Birke R, Cámara J, Chen LY, Esterle L, Geihs K, Gelenbe E, Giese H, Robertsson A, Zhu X. Self-aware computing systems: open challenges and future research directions. In: Kounev S, Kephart J, Milenkoski A, Zhu X, editors. Self-aware computing systems. Cham: Springer; 2017. p. 709–22.CrossRefGoogle Scholar
  14. 14.
    Blazewicz J, Drabowski M, Wȩglarz J. Scheduling multiprocessor tasks to minimize schedule length. Comput IEEE Trans. 1985;100(5):389–93.MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Brun O, Wang L, Gelenbe E. Big data for autonomic intercontinental overlays. IEEE J Sel Areas Commun. 2016;34(3):575–83.CrossRefGoogle Scholar
  16. 16.
    Cellary W, Jomier G. Consistency of versions in object-oriented databases. In: VLDB 90; 1990. pp. 432–41.Google Scholar
  17. 17.
    Cellary W, Jomier G, Koszlajda T. Formal model of an object-oriented database with versioned objects and schema. In: Karagiannis D, editors. Proceedings of the international conference on database and expert systems applications. Wien: Springer; 1991. pp. 239–44.CrossRefGoogle Scholar
  18. 18.
    Cellary W, Morzy T, Gelenbe E. Concurrency control in distributed database systems. Amsterdam: Elsevier; 2014.zbMATHGoogle Scholar
  19. 19.
    Cellary W, Vossen G, Jomier G. Multiversion object constellations: a new approach to support a designer’s database work. Eng Comput. 1994;3(4):230–44.CrossRefGoogle Scholar
  20. 20.
    Chamanian S, Baghaee S, Uluşan H, Zorlu O, Uysal-Biyikoglu E, Külah H. Implementation of energy-neutral operation on vibration energy harvesting WSN. IEEE Sens J. 2019;19(8):3092–9.CrossRefGoogle Scholar
  21. 21.
    Chesnais A, Gelenbe E, Mitrani I. On the modeling of parallel access to shared data. Commun ACM. 1983;26(3):196–202.zbMATHCrossRefGoogle Scholar
  22. 22.
    Coffman EG, Gelenbe E, Plateau B. Optimization of the number of copies in a distributed data base. IEEE Trans Softw Eng. 1981;1:78–84.MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Czachorski T, Domanska J, Pagano M. On stochastic models of internet traffic. In: Dudin A, Nazarov A, Yakupov R, editors. Information technologies and mathematical modelling: queueing theory and applications, ITMM 2015, Sudzhensk, Russia, November 18–22, 2015. Communications in computer and information science, vol. 564. Cham: Springer; 2015. pp. 289–303.  https://doi.org/10.1007/978-3-319-25861-4_25.Google Scholar
  24. 24.
    Czachórski T, Gelenbe E, Grochla K, Lent R, editors. Computer and information sciences—31st international symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, Proceedings, communications in computer and information science, vol. 659. Cham: Springer; 2016.  https://doi.org/10.1007/978-3-319-47217-1.Google Scholar
  25. 25.
    Czachórski T, Gelenbe E, Grochla K, Lent R, editors. Computer and information sciences—32nd international symposium, ISCIS 2018, Held at the 24th IFIP world computer congress, WCC 2018, Poznan, Poland, September 20–21, 2018, Proceedings, communications in computer and information science, vol. 935. Cham: Springer; 2018.  https://doi.org/10.1007/978-3-030-00840-6.Google Scholar
  26. 26.
    Czachórski T, Gelenbe E, Lent R, editors. Information sciences and systems 2014—proceedings of the 29th international symposium on computer and information sciences, ISCIS 2014, Krakow, Poland, October 27–28, 2014. Cham: Springer; 2014.  https://doi.org/10.1007/978-3-319-09465-6.Google Scholar
  27. 27.
    Czachorski T, Grochla K, Jozefiok A, Nycz T, Pekergin F. Performance evaluation of a multiuser interactive networking system: a comparison of modelling methods. In: Gelenbe E, Lent R, Sakellari G, editors. Computer and information sciences II, Imperial Coll London. Cham: Springer; 2012. pp. 215–21.  https://doi.org/10.1007/978-1-4471-2155-8_27.CrossRefGoogle Scholar
  28. 28.
    Czachorski T, Grochla K, Nycz T, Pekergin F. A diffusion approximation model for wireless networks based on IEEE 802.11 standard. Comput Commun. 2010;33(1, SI):S86–92.  https://doi.org/10.1016/j.comcom.2010.07.006.CrossRefGoogle Scholar
  29. 29.
    Czachorski T, Grochla K, Pekergin F. Diffusion approximation model for the distribution of packet travel time at sensor networks. In: Cerda-Alabern L, editors. Wireless systems and mobility in next generation internet, Lecture notes in computer science, vol. 5122. European Union (2008). 3rd international workshop on network of excellence on next-generation internet, Univ Politec Catalunya, CompNet Res, Barcelona, Spain, Jan 16–18; 2008. p. 10+.Google Scholar
  30. 30.
    Czachorski T, Grochla K, Pekergin F. Un modèle d’approximation de diffusion pour la distribution du temps d’acheminement des paquets dans les réseaux de senseurs. In: Colloque Francophone sur l’Ingénierie des Protocoles (CFIP); 2008.Google Scholar
  31. 31.
    Czachorski T, Nycz M, Nycz T, Pekergin F. Analytical and numerical means to model transient states in computer networks. In: Kwiecien A, Gaj P, Stera P, editors. Computer networks, CN 2013, Communications in computer and information science, Lecture notes CCIS, vol. 370. Cham: Springer; 2013. pp. 426–35.zbMATHCrossRefGoogle Scholar
  32. 32.
    Czachorski T, Nycz T, Nycz M, Pekergin F. Traffic engineering: erlang and ENGSET models revisited with diffusion approximation. In: Czachorski T, Gelenbe E, Lent R, editors. Information sciences and systems 2014, Polish Acad Sci, Inst Theoret and Appl Informat 2014. 29th annual symposium on computer and information sciences, Krakow, Poland, Oct 27–28, 2014. pp. 249–56.  https://doi.org/10.1007/978-3-319-09465-6_26.Google Scholar
  33. 33.
    Czachorski T, Nycz T, Pekergin F. Priority disciplines—a diffusion approach. In: 23rd international symposium on computer and information sciences, Istanbul, Turkey, Oct 27–29, 2008; 2008. p. 554+.Google Scholar
  34. 34.
    Czachorski T, Nycz T, Pekergin F. Transient states analysis—diffusion approximation as an alternative to Markov models, fluid-flow approximation and simulation. In: ISCC: 2009 IEEE symposium on computers and communications, vols 1 and 2, IEEE symposium on computers and communications ISCC. Sousse, Tunisia, Jul 05–08; 2009. p. 1047+.Google Scholar
  35. 35.
    Czachorski T, Nycz T, Pekergin F. Transient states of priority queues—a diffusion approximation study. In: Geraci P, Logothetis M, editors. AICT: 2009 fifth advanced international conference on telecommunications; 2009. pp. 44–51.  https://doi.org/10.1109/AICT.2009.14.
  36. 36.
    Czachorski T, Nycz T, Pekergin F. Transient states of priority queues—QoS issues in wireless networks via diffusion approximation. In: 2009 1st international conference on wireless communication, vehicular technology, information theory and aerospace and electronic systems technology, vol. 1 and 2; 2009. pp. 426+.Google Scholar
  37. 37.
    Czachorski T, Pastuszka M, Pekergin F. A tool to model network transient states with the use of diffusion approximation. In: Puigjaner R, Savino NN, Serra B, editors. Computer performance evaluation: modelling techniques and tools, LNCS, vol. 1469; 1998. pp. 344–47.CrossRefGoogle Scholar
  38. 38.
    Devillers B, Gündüz D. A general framework for the optimization of energy harvesting communication systems with battery imperfections. J Commun Netw. 2012;14(2):130–9.  https://doi.org/10.1109/JCN.2012.6253061.CrossRefGoogle Scholar
  39. 39.
    Dimitriou I, Alouf S, Jean-Marie A. A markovian queueing system for modeling a smart green base station. In: European workshop on performance engineering. Cham: Springer; 2015. pp. 3–18.CrossRefGoogle Scholar
  40. 40.
    Dobson S, Denazis S, Fernández A, Gaïti D, Gelenbe E, Massacci F, Nixon P, Saffre F, Schmidt N, Zambonelli F. A survey of autonomic communications. ACM Trans Auton Adapt Syst. 2006;1(2):223–59.CrossRefGoogle Scholar
  41. 41.
    Domanska J, Domanski A, Czachorski T. Internet traffic source based on hidden markov model. In: Baladin S, Koucheryavy Y, Hu H, editors. Smart spaces and next generation wired/wireless networking, LNCS, vol. 6869. Cham: Springer; 2011. p. 395–404.CrossRefGoogle Scholar
  42. 42.
    Domańska J, Nowak M, Nowak S, Czachórski T. European cybersecurity research and the seriot project. In: Communications in computer and information science, vol. 935; 2018. pp. 166–73.CrossRefGoogle Scholar
  43. 43.
    Domanski A, Domanska J, Czachorski T, Klamka J, Szygua J, Marek D. Diffusion approximation model of TCP newreno congestion control mechanism. Berlin: Springer; 2019 (this issue).Google Scholar
  44. 44.
    Doncel J, Fourneau JM. Energy packet networks with multiple energy packet requirements. Probab Eng Inf Sci. 2019;1:4.  https://doi.org/10.1017/S0269964819000226.CrossRefGoogle Scholar
  45. 45.
    Duda A. The effects of checkpointing on program execution time. Inf Process Lett. 1983;16(5):221–9.MathSciNetzbMATHCrossRefGoogle Scholar
  46. 46.
    Duda A. Performance analysis of the checkpoint-rollback-recovery system via diffusion approximation. In: Proceedings of the international workshop on computer performance and reliability. North Holland: Elsevier; 1983. pp. 315–27.Google Scholar
  47. 47.
    Duda A. Transient diffusion approximation for some queueing systems. In: Proceedings of the 1983 ACM SIGMETRICS conference on measurement and modelling of computer systems, ACM; 1983. pp. 118–28.Google Scholar
  48. 48.
    Duda A. Diffusion approximations for time-dependent queueing systems. IEEE J Sel Areas Commun. 1986;4(6):905–18.CrossRefGoogle Scholar
  49. 49.
    Duda A. Approximate performance analysis of parallel systems. In: Proceedings of the international workshop on computer performance and reliability. North Holland: Elsevier; 1987. pp. 315–27.Google Scholar
  50. 50.
    Duda A, Czachorski T. Performance evaluation of fork and join synchronization primitives. Acta Inform. 1987;24(5):525–53.MathSciNetzbMATHCrossRefGoogle Scholar
  51. 51.
    Duda A, Harrus G, Haddad Y, Bernard G. Estimating global time in distributed systems. In: ICDCS 87; 1987. pp. 299–306.Google Scholar
  52. 52.
    Fourneau J, Czachorski T. Transport time distribution for deflection routing on an odd torus. In: Cunha JC, Medeiros PD, editors. EURO-PAR 2005 parallel processing, proceedings, LNCS, vol. 3648. Cham: Springer; 2005. p. 975–83.CrossRefGoogle Scholar
  53. 53.
    Fourneau JM. Modeling green data-centers and jobs balancing with energy packet networks and interrupted Poisson energy arrivals. Berlin: Springer; 2019 (this issue).Google Scholar
  54. 54.
    Fourneau JM, Gelenbe E. G-networks with adders. Future Internet. 2017;9(3):34-ff.CrossRefGoogle Scholar
  55. 55.
    Fourneau JM, Wolter K, Reinecke P, Krauß T, Danilkina A. Multiple class g-networks with restart. In: ACM/SPEC international conference on performance engineering, ICPE’13, Prague, Czech Republic, April 21–24, 2013; 2013. pp. 39–50. http://doi.acm.org/10.1145/2479871.2479880.
  56. 56.
    Francois F, Gelenbe E. Optimizing secure SDN-enabled inter-data centre overlay networks through cognitive routing. In: 2016 IEEE 24th international symposium on modeling, analysis and simulation of computer and telecommunication systems (MASCOTS), IEEE; 2016. pp. 283–88.Google Scholar
  57. 57.
    Gelenbe E. On approximate computer system models. J ACM. 1975;22(2):261–9.MathSciNetzbMATHCrossRefGoogle Scholar
  58. 58.
    Gelenbe E. On the optimum checkpoint interval. J ACM. 1979;26(2):259–70.MathSciNetzbMATHCrossRefGoogle Scholar
  59. 59.
    Gelenbe E. Probabilistic models of computer systems, part ii: diffusion approximations, waiting times and batch arrivals. Acta Inform. 1979;12(4):285–303.MathSciNetzbMATHCrossRefGoogle Scholar
  60. 60.
    Gelenbe E. Multiprocessor performance. New York: Wiley; 1989.zbMATHGoogle Scholar
  61. 61.
    Gelenbe E. Réseaux neuronaux aléatoires stables. Comptes Rendus de l’Académie des Sciences. Série 2, Mécanique, Physique, Chimie, Sciences de l’Univers, Sciences de la Terre 1990;310(3): 177–180Google Scholar
  62. 62.
    Gelenbe E. Product-form queueing networks with negative and positive customers. J Appl Probab. 1991;28(3):656–63.MathSciNetzbMATHCrossRefGoogle Scholar
  63. 63.
    Gelenbe E. G-networks with signals and batch removal. Probab Eng Inf Sci. 1993;7(3):335–42.MathSciNetCrossRefGoogle Scholar
  64. 64.
    Gelenbe E. Learning in the recurrent random neural network. Neural Comput. 1993;5(1):154–64.CrossRefGoogle Scholar
  65. 65.
    Gelenbe E. A diffusion model for packet travel time in a random multihop medium. ACM Trans Sens Netw. 2007;3(2):10.CrossRefGoogle Scholar
  66. 66.
    Gelenbe E. Steps toward self-aware networks. Commun ACM. 2009;52(7):66–75.CrossRefGoogle Scholar
  67. 67.
    Gelenbe E. Search in unknown random environments. Phys Rev E. 2010;82(6):061112.CrossRefGoogle Scholar
  68. 68.
    Gelenbe E. Energy packet networks: adaptive energy management for the cloud. In: Proceedings of the 2nd international workshop on cloud computing platforms, ACM, NY, USA; 2012. pp. 1:1–1:5.  https://doi.org/10.1145/2168697.2168698.
  69. 69.
    Gelenbe E. Energy packet networks: Ict based energy allocation and storage. In: Rodrigues JJPC, Zhou L, Chen M, Kailas A, editors. Green communications and networking—first international conference, GreeNets 2011, Colmar, France, October 5–7, 2011, Revised Selected Papers, Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol. 51. Cham: Springer; 2012.Google Scholar
  70. 70.
    Gelenbe E. Adaptive management of energy packets. In: IEEE 38th annual computer software and applications conference, COMPSAC workshops 2014, Vasteras, Sweden, July 21–25, 2014; 2014. pp. 1–6.Google Scholar
  71. 71.
    Gelenbe E. Synchronising energy harvesting and data packets in a wireless sensor. Energies. 2015;8(1):356–69.CrossRefGoogle Scholar
  72. 72.
    Gelenbe E, Abdelrahman OH. An energy packet network model for mobile networks with energy harvesting. Nonlinear Theory Appl IEICE. 2018;9(3):1–15.Google Scholar
  73. 73.
    Gelenbe E, Campegiani P, Czachórski T, Katsikas SK, Komnios I, Romano L, Tzovaras D. Security in computer and information sciences. In: Recent cybersecurity research in Europe: proceedings of the 2018 ISCIS security workshop, Imperial College London. Lecture notes CCIS No. 821. Springer; 2018. pp. 26–7.Google Scholar
  74. 74.
    Gelenbe E, Campegiani P, Czachórski T, Katsikas SK, Komnios I, Romano L, Tzovaras D, editors. Security in computer and information sciences—first international ISCIS security workshop 2018, Euro-CYBERSEC 2018, London, UK, February 26–27, 2018, Revised selected papers, Communications in computer and information science, vol. 821. Cham: Springer; 2018.  https://doi.org/10.1007/978-3-319-95189-8.Google Scholar
  75. 75.
    Gelenbe E, Caseau Y. The impact of information technology on energy consumption and carbon emissions. Ubiquity. 2015;2015(June):1.CrossRefGoogle Scholar
  76. 76.
    Gelenbe E, Ceran ET. Energy packet networks with energy harvesting. IEEE Access. 2016;4:1321–31.CrossRefGoogle Scholar
  77. 77.
    Gelenbe E, Derochette D. Performance of rollback recovery systems under intermittent failures. Commun ACM. 1978;21(6):493–9.MathSciNetzbMATHCrossRefGoogle Scholar
  78. 78.
    Gelenbe E, Domanska J, Czàchorski T, Drosou A, Tzovaras D. Security for internet of things: the seriot project. In: 2018 international symposium on networks, computers and communications (ISNCC), IEEE, 19–21 June 2018, Rome, Italy; 2018. pp. 1–5.Google Scholar
  79. 79.
    Gelenbe E, Finkel D, Tripathi SK. On the availability of a distributed computer system with failing components. ACM SIGMETRICS ’85, Proceedings of the 1985 ACM SIGMETRICS conference on measurement and modeling of computer systems, Austin, Texas, USA, August 26–29, 1985 13(2); 1985. pp. 6–13.Google Scholar
  80. 80.
    Gelenbe E, Finkel D, Tripathi SK. Availability of a distributed computer system with failures. Acta Inform. 1986;23(6):643–55.zbMATHCrossRefGoogle Scholar
  81. 81.
    Gelenbe E, Gesbert D, Gunduz D, Külah H, Uysal-Biyikoglu E. Energy harvesting communication networks: Optimization and demonstration (the e-crops project). In: 2013 24th Tyrrhenian international workshop on digital communications-Green ICT (TIWDC), IEEE; 2013. pp. 1–6.Google Scholar
  82. 82.
    Gelenbe E, Hernández M. Optimum checkpoints with age dependent failures. Acta Inform. 1990;27(6):519–31.MathSciNetzbMATHCrossRefGoogle Scholar
  83. 83.
    Gelenbe E, Lent R, editors. Computer and information sciences III—27th international symposium on computer and information sciences, Paris, France, October 3–4, 2012. Cham: Springer; 2013.  https://doi.org/10.1007/978-1-4471-4594-3.Google Scholar
  84. 84.
    Gelenbe E, Lent R. Energy-qos trade-offs in mobile service selection. Future Internet. 2013;5(2):128–39.CrossRefGoogle Scholar
  85. 85.
    Gelenbe E, Lent R, Sakellari G, editors. Computer and information sciences II—26th international symposium on computer and information sciences, London, UK, 26–28 September 2011. Cham: Springer; 2011.  https://doi.org/10.1007/978-1-4471-2155-8.Google Scholar
  86. 86.
    Gelenbe E, Lent R, Sakellari G, Sacan A, Toroslu IH, Yazici A, editors. Computer and information sciences—Proceedings of the 25th international symposium on computer and information sciences, London, UK, September 22–24, 2010, Lecture notes in electrical engineering, vol. 62. Cham: Springer; 2010.  https://doi.org/10.1007/978-90-481-9794-1.Google Scholar
  87. 87.
    Gelenbe E, Liu Z. Performance analysis approximations for parallel processing in multiprocessor systems. In: Parallal processing, Proceedings of the IFIP WG 10.3 conference on. Elsevier Science; 1988.Google Scholar
  88. 88.
    Gelenbe E, Mang X, Feng Y. A diffusion cell loss estimate for ATM with multiclass bursty traffic. In: Kuvatsos D, editors International workshop on performance modelling and evaluation of ATM networks, July 2–6, 1995, Ilkley, UK. Cham: Springer; 1995. pp. 233–248.CrossRefGoogle Scholar
  89. 89.
    Gelenbe E, Mang X, Önvural R. Diffusion based statistical call admission control in ATM. Perform Eval. 1996;27:411–36.zbMATHCrossRefGoogle Scholar
  90. 90.
    Gelenbe E, Mang X, Onvural R. Bandwidth allocation and call admission control in high-speed networks. IEEE Commun Mag. 1997;35(5):122–9.CrossRefGoogle Scholar
  91. 91.
    Gelenbe E, Mitrani I. Analysis and synthesis of computer systems. Singapore: World Scientific; 2010.zbMATHCrossRefGoogle Scholar
  92. 92.
    Gelenbe E, Morfopoulou C. Routing and g-networks to optimise energy and quality of service in packet networks. In: Hatziargyriou N, Dimeas A, Tomtsi T, Weidlich A, editors. International conference on energy-efficient computing and networking. E-Energy 2010, Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, vol. 54. Berlin: Springer; 2010. pp. 163–73.Google Scholar
  93. 93.
    Gelenbe E, Morfopoulou C. A framework for energy-aware routing in packet networks. Comput J. 2011;54(6):850–9.CrossRefGoogle Scholar
  94. 94.
    Gelenbe E, Pujolle G. The behaviour of a single queue in a general queueing network. Acta Inform. 1976;7(2):123–36.MathSciNetzbMATHCrossRefGoogle Scholar
  95. 95.
    Gelenbe E, Pujolle G. Introduction aux Réseaux de Files d’Sttente. Eyrolles: Edition Hommes et Techniques; 1982.zbMATHGoogle Scholar
  96. 96.
    Gelenbe E, Pujolle G. Introduction to queueing networks. Chichester: Wiley; 1998.zbMATHGoogle Scholar
  97. 97.
    Gelenbe E, Sevcik K. Analysis of update synchronization for multiple copy data-bases. In: 3rd Berkeley workshop on distributed data and computer networks, Lawrence Berkeley Laboratory, Berkeley, California, August 29–31; 1978. pp. 69–90.Google Scholar
  98. 98.
    Gelenbe E, Silvestri S. Reducing power consumption in wired networks. In: 24th international symposium on computer and information sciences (ISCIS 2009), 14–16 September 2009, Turkish Republic of North Cyprus, IEEE; 2009. pp. 292–97.Google Scholar
  99. 99.
    Gelenbe E, Zhang Y. Matching energy and job flows for system performance optimization. Submitted for Publication 2019.Google Scholar
  100. 100.
    Gelenbe E, Zhang Y. Performance optimization with energy packets. IEEE Syst J. 2019;1:1–11.  https://doi.org/10.1109/JSYST.2019.2912013 (Early Access).CrossRefGoogle Scholar
  101. 101.
    Ghosal D, Yeh G, Shukla S, Farrens M, Wu J. Model driven joint optimization of power and latency guarantee in datacenter applications. Berlin: Springer; 2019 (this issue).Google Scholar
  102. 102.
    Gonzalez G, Atmaca T, Czachorski T. An energy saving solution in integrated access networks. In: Gaj P, Kwiecien A, Stera P, editors. Computer networks, CN 2015, Communications in computer and information science. 22nd international conference on computer networks (CN), Brunow, Poland, June 16–19, 2015, vol. 522. Cham: Springer; 2015. pp. 222–31.  https://doi.org/10.1007/978-3-319-19419-6_21.CrossRefGoogle Scholar
  103. 103.
    Gorawski M, Grochla K. Performance tests of smart city IoT data repositories for universal linear infrastructure data and graph databases. Berlin: Springer; 2019 (this issue).Google Scholar
  104. 104.
    Gurakan B, Kaya O, Ulukus S. Energy and data cooperative multiple access channel with intermittent data arrivals. IEEE Trans Wirel Commun. 2018;17(3):2016–28.  https://doi.org/10.1109/TWC.2017.2787744.CrossRefGoogle Scholar
  105. 105.
    Gurakan B, Ozel O, Yang J, Ulukus S. Energy cooperation in energy harvesting communications. IEEE Trans Commun. 2013;61(12):4884–98.CrossRefGoogle Scholar
  106. 106.
    Guruacharya S, Hossain E. Self-sustainability of energy harvesting systems: concept, analysis, and design. IEEE Trans Green Commun Netw. 2018;2(1):175–92.CrossRefGoogle Scholar
  107. 107.
    Harrison PG, Pitel E. Response time distributions in tandem g-networks. J Appl Probab. 1995;32(1):224–46.  https://doi.org/10.2307/3214932.MathSciNetCrossRefzbMATHGoogle Scholar
  108. 108.
    Ho CK, Zhang R. Optimal energy allocation for wireless communications with energy harvesting constraints. IEEE Trans Signal Process. 2012;60(9):4808–18.  https://doi.org/10.1109/TSP.2012.2199984.MathSciNetCrossRefzbMATHGoogle Scholar
  109. 109.
    Ilik B, Koyuncuoglu A, Şardan Sukas O, Külah H. Thin film piezoelectric acoustic transducer for fully implantable cochlear implants. Sens Actuators A. 2018;280:38–46.CrossRefGoogle Scholar
  110. 110.
    Jomier G, Cellary W. The database version approach. Netw Inf Syst J. 2000;3(1):177–214.Google Scholar
  111. 111.
    Kadioglu YM, Gelenbe E. Product form solution for cascade networks with intermittent energy. IEEE Syst J. 2019;.  https://doi.org/10.1109/JSYST.2018.2854838 (Early Access).CrossRefGoogle Scholar
  112. 112.
    Kansal A, Hsu J, Zahedi S, Srivastava MB. Power management in energy harvesting sensor networks. ACM Trans Embed Comput Syst. 2007;6:4.  https://doi.org/10.1145/1274858.1274870.CrossRefGoogle Scholar
  113. 113.
    Kopats D, Matalytski M. About diffusion approximation of open queueing network with limited number of customer and time-dependent service parameters. Berlin: Springer; 2019 (this issue).Google Scholar
  114. 114.
    Ku M, Li W, Chen Y, Liu KJR. Advances in energy harvesting communications: past, present, and future challenges. IEEE Commun Surv Tutor. 2016;18(2):1384–412.  https://doi.org/10.1109/COMST.2015.2497324.CrossRefGoogle Scholar
  115. 115.
    Külah H, Najafi K. Energy scavenging from low-frequency vibrations by using frequency up-conversion for wireless sensor applications. IEEE Sens J. 2008;8(3):261–8.CrossRefGoogle Scholar
  116. 116.
    Laalaoua R, Czachorski T, Atmaca T. Markovian model of RED mechanism. In: Buyya R, Mohay G, Roe P, editors. First IEEE/ACM international symposium on cluster computing and the grid, proceedings, IEEE 2001, Brisbane, Australia, May 15–18, 2001. pp. 610–617.  https://doi.org/10.1109/CCGRID.2001.923250.
  117. 117.
    Laalaoua R, Jedrus S, Atmaca T, Czachorski T. Diffusion model of RED control mechanism. In: Lorenz P, editors. Networking—ICN 2001, PT I, proceedings, Lecture notes in computer science, 1st international conference on networking, Colmar, France, Jul 09–13, 2001, vol. 2093. Heidelberg:Springer; 2001. pp. 107–16.Google Scholar
  118. 118.
    Liu Q, Mak T, Luo J, Luk W, Yakovlev A. Power adaptive computing system design in energy harvesting environment. In: Embedded computer systems, 2011 international conference on, 18–21 July 2011, Samos, Greece, IEEE; 2011. pp. 33–40.  https://doi.org/10.1109/SAMOS.2011.6045442.
  119. 119.
    Lo Piano S, Mayumi K. Toward an integrated assessment of the performance of photovoltaic systems for electricity generation. Appl Energy. 2017;186(2):167–74.  https://doi.org/10.1016/j.apenergy.2016.05.102.CrossRefGoogle Scholar
  120. 120.
    Marin A. Product-form in g-networks. Probab Eng Inf Sci. 2016;30(3):345–60.  https://doi.org/10.1017/S0269964816000048.MathSciNetCrossRefzbMATHGoogle Scholar
  121. 121.
    Marin GA, Mang X, Gelenbe E, Onvural RO. Statistical call admission control. 2001. US Patent 6222824Google Scholar
  122. 122.
    Niedzielewski K, Marchwiany M, Piliszek R, Michalewicz M, Rudnicki W. Multidimensional feature selection and high performance parallex. Berlin: Springer; 2019 (this issue).Google Scholar
  123. 123.
    Niu X, Korniss G, Szymanski B. Supervised learning of the global risk network activation from media event reports. Berlin: Springer; 2019 (this issue).Google Scholar
  124. 124.
    Nowak M, Domańska J, Nowak S. Cognitive routing for improvement of IoT security. In: 2019 IEEE international conference on fog computing (ICFC 2019). IEEE Computer Society, Prague, Czech Republic; 2019.Google Scholar
  125. 125.
    Nowak S, Nowak M, Domańska J, Czachórski T. Cognitive packet networks for the secure internet of things. In: Global Internet of Things summit (GIoTS). Aarhus, Denmark; 2019.Google Scholar
  126. 126.
    Orgerie AC, Amersho BL, Haudebourg T, Quinson M, Rifai M, Pacheco DL, Lefèvre L. Simulation toolbox for studying energy consumption in wired networks. In: Embedded computer systems, 2011 international conference on, Nov 26–30 2017, Tokyo, Japan, IEEE; 2017. pp. 1–5.Google Scholar
  127. 127.
    Ozel O, Tutuncuoglu K, Yang J, Ulukus S, Yener A. Transmission with energy harvesting nodes in fading wireless channels: optimal policies. IEEE J Sel Areas Commun. 2011;29(8):1732–43.CrossRefGoogle Scholar
  128. 128.
    Phi TC, Muzy A, Reynaud-Bouret P. Event-scheduling algorithms with Kalikov decomposition for simulating potentially infinite neuronal networks. Berlin: Springer; 2019 (this issue).Google Scholar
  129. 129.
    Rataj A. Random neural networks with hierarchical committees for improved routing in wireless mesh networks with interference. Berlin: Springer; 2019 (this issue).Google Scholar
  130. 130.
    Siavvas M, Gelenbe E. Optimum checkpoints for programs with loops. Simul Model Pract Theory. 2019;97:101951.CrossRefGoogle Scholar
  131. 131.
    Szczesniak I, Mukherjee B, Czachorski T. Approximate analytical performance evaluation of synchronous bufferless optical packet-switched networks. J Opt Commun Netw. 2011;3(10):806–15.  https://doi.org/10.1364/JOCN.3.000806.CrossRefGoogle Scholar
  132. 132.
    Takahashi R, Takuno T, Hikihara T. Estimation of power packet transfer properties on indoor power line channel. Energies. 2012;5(7):2141–9.CrossRefGoogle Scholar
  133. 133.
    Takahashi R, Tashiro K, Hikihara T. Router for power packet distribution network: design and experimental verification. IEEE Trans Smart Grid. 2015;6(2):618–26.  https://doi.org/10.1109/TSG.2014.2384491.CrossRefGoogle Scholar
  134. 134.
    Tripathi S, Duda A. Time-dependent analysis of queueing systems. Inf Syst Oper Res. 1986;24(3):199–220.zbMATHGoogle Scholar
  135. 135.
    Tripathi SK, Finkel D, Gelenbe E. Load sharing in distributed systems with failures. Acta Inform. 1988;25(6):677–89.MathSciNetCrossRefGoogle Scholar
  136. 136.
    Ulukus S, Yener A, Erkip E, Simeone O, Zorzi M, Grover P, Huang K. Energy harvesting wireless communications: a review of recent advances. IEEE J Sel Areas Commun. 2015;33(3):360–81.  https://doi.org/10.1109/JSAC.2015.2391531.CrossRefGoogle Scholar
  137. 137.
    Wang L, Gelenbe E. An implementation of voice over ip in the cognitive packet network. Cham: Springer; 2014. p. 33–40.Google Scholar
  138. 138.
    Wang L, Gelenbe E. Demonstrating voice over an autonomic network. In: 2015 IEEE international conference on autonomic computing, IEEE; 2015. pp. 139–40.Google Scholar
  139. 139.
    Wang L, Gelenbe E. Adaptive dispatching of tasks in the cloud. IEEE Trans Cloud Comput. 2018;6(1):33–45.CrossRefGoogle Scholar
  140. 140.
    Wȩgrzyn S, Gille JC, Vidal P. Developmental systems: at the crossroads of system theory, computer science, and genetic engineering. New York: Springer; 1990.CrossRefGoogle Scholar
  141. 141.
    Weglarz J, Blazewicz J, Cellary W, Slowinski R. Algorithm 520: an automatic revised simplex method for constrained resource network scheduling [h]. ACM Trans Math Softw. 1977;3(3):295–300.zbMATHCrossRefGoogle Scholar
  142. 142.
    Wegrzyn S. Les Bases de L’Automatique Industrielle. Paris: Dunod; 1965.Google Scholar
  143. 143.
    Yang J, Ulukus S. Optimal packet scheduling in a multiple access channel with energy harvesting transmitters. J Commun Netw. 2012;14(2):140–50.  https://doi.org/10.1109/JCN.2012.6253062.CrossRefGoogle Scholar
  144. 144.
    Yang J, Ulukus S. Optimal packet scheduling in an energy harvesting communication system. IEEE Trans Commun. 2012;60(1):220–30.  https://doi.org/10.1109/TCOMM.2011.112811.100349.CrossRefGoogle Scholar
  145. 145.
    Zhang Y. Optimal energy distribution with energy packet networks. Probab Eng Inf Sci. 2019;1:1–17.  https://doi.org/10.1017/S0269964818000566.CrossRefGoogle Scholar
  146. 146.
    Zielinski J, Nowosielski J. Finet’s law as a special case of the generalised Murray’s law. Berlin: Springer; 2019 (this issue).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd 2019

Authors and Affiliations

  1. 1.Institute of Theoretical and Applied InformaticsPolish Academy of Sciences (PAN)GliwicePoland
  2. 2.Laboratoire I3SUniversité Côte d’AzurNice Sophia-AntipolisFrance

Personalised recommendations