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Reliability of Trust Management Systems in Cloud Computing

Cloud computing is an innovation that conveys administrations like programming, stage, and framework over the web. This computing structure is wide spread and dynamic, which chips away at the compensation per-utilize model and supports virtualization. Distributed computing is expanding quickly among purchasers and has many organizations that offer types of assistance through the web. It gives an adaptable and on-request administration yet at the same time has different security dangers. Its dynamic nature makes it tweaked according to client and supplier’s necessities, subsequently making it an outstanding benefit of distributed computing. However, then again, this additionally makes trust issues and or issues like security, protection, personality, and legitimacy. In this way, the huge test in the cloud climate is selecting a perfect organization. For this, the trust component assumes a critical part, in view of the assessment of QoS and Feedback rating. Nonetheless, different difficulties are as yet present in the trust the board framework for observing and assessing the QoS. This paper talks about the current obstructions present in the trust framework. The objective of this paper is to audit the available trust models. The issues like insufficient trust between the supplier and client have made issues in information sharing likewise tended to here. Besides, it lays the limits and their enhancements to help specialists who mean to investigate this point.

Guest Editorial: Special Section on Parallel and Distributed Computing Techniques for Non-Von Neumann Technologies

Asynchronous rpc interface in distributed computing system, developing an efficient secure query processing algorithm on encrypted databases using data compression.

Abstract Distributed computing includes putting aside the data utilizing outsider storage and being able to get to this information from a place at any time. Due to the advancement of distributed computing and databases, high critical data are put in databases. However, the information is saved in outsourced services like Database as a Service (DaaS), security issues are raised from both server and client-side. Also, query processing on the database by different clients through the time-consuming methods and shared resources environment may cause inefficient data processing and retrieval. Secure and efficient data regaining can be obtained with the help of an efficient data processing algorithm among different clients. This method proposes a well-organized through an Efficient Secure Query Processing Algorithm (ESQPA) for query processing efficiently by utilizing the concepts of data compression before sending the encrypted results from the server to clients. We have addressed security issues through securing the data at the server-side by an encrypted database using CryptDB. Encryption techniques have recently been proposed to present clients with confidentiality in terms of cloud storage. This method allows the queries to be processed using encrypted data without decryption. To analyze the performance of ESQPA, it is compared with the current query processing algorithm in CryptDB. Results have proven the efficiency of storage space is less and it saves up to 63% of its space.

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.

Deep distributed computing to reconstruct extremely large lineage trees

Distributed computing and artificial intelligence, volume 2: special sessions 18th international conference, software engineering, artificial intelligence, networking and parallel/distributed computing, chinese keyword extraction model with distributed computing, on allocation of systematic blocks in coded distributed computing, export citation format, share document.

The evolution of distributed computing systems: from fundamental to new frontiers

• Regular Paper
• Published: 30 January 2021
• Volume 103 , pages 1859–1878, ( 2021 )

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• Dominic Lindsay   ORCID: orcid.org/0000-0002-9354-4183 1 ,
• Sukhpal Singh Gill   ORCID: orcid.org/0000-0002-3913-0369 2 ,
• Daria Smirnova 1 &
• Peter Garraghan 1  

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Distributed systems have been an active field of research for over 60 years, and has played a crucial role in computer science, enabling the invention of the Internet that underpins all facets of modern life. Through technological advancements and their changing role in society, distributed systems have undergone a perpetual evolution, with each change resulting in the formation of a new paradigm. Each new distributed system paradigm—of which modern prominence include cloud computing, Fog computing, and the Internet of Things (IoT)—allows for new forms of commercial and artistic value, yet also ushers in new research challenges that must be addressed in order to realize and enhance their operation. However, it is necessary to precisely identify what factors drive the formation and growth of a paradigm, and how unique are the research challenges within modern distributed systems in comparison to prior generations of systems. The objective of this work is to study and evaluate the key factors that have influenced and driven the evolution of distributed system paradigms, from early mainframes, inception of the global inter-network, and to present contemporary systems such as edge computing, Fog computing and IoT. Our analysis highlights assumptions that have driven distributed systems appear to be changing, including (1) an accelerated fragmentation of paradigms driven by commercial interests and physical limitations imposed by the end of Moore’s law, (2) a transition away from generalized architectures and frameworks towards increasing specialization, and (3) each paradigm architecture results in some form of pivoting between centralization and decentralization coordination. Finally, we discuss present day and future challenges of distributed research pertaining to studying complex phenomena at scale and the role of distributed systems research in the context of climate change.

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Fog Computing: Concepts, Principles and Related Paradigms

Fog Computing: A Platform for Internet of Things and Analytics

Fog Computing

The first webpage— http://info.cern.ch/hypertext/WWW/TheProject.html

History of Distributed Systems— https://medium.com/microservices-learning/the-evolution-of-distributed-systems-fec4d35beffd

Armbrust M et al (2009) Above the clouds: A Berkeley view of cloud computing. EECS Department, University of California, Berkeley, no. January, pp 1–25, 2009

Lamport L (1978) Time, clocks, and the ordering of events in a distributed system. Commun ACM 21(7):558–565

Article   Google Scholar  

Chow Y-C (1979) Models for dynamic load balancing in a heterogeneous multiple processor system. IEEE Trans Comput 10(5):354–361

Article   MathSciNet   Google Scholar  

Botta A, De Donato W, Persico V, Pescap A (2016) Integration of cloud computing and internet of things: a survey. Future Gen Comput Syst 56:684–700

Yu X, MI Fellow IEEE, Xue Y (2016) Smart grids: a cyber–physical systems perspective. Proc IEEE 104(5):1058–1070

Cisco Systems (2016) Fog computing and the internet of things: extend the cloud to where the things are, p 6. www.Cisco.com

Walker Bruce TG, Popek G, English R, Kline C (1983) The LOCUS distributed operating system. ACM SIGOPS Oper Syst Rev 17:49–70

Birrell AD, Levin R, Schroeder MD, Needham RM (1982) Grapevine: an exercise in distributed computing. Commun. ACM 25(4):260–274

Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz RH, Shenker S, Stoica I (2011) Mesos: a platform for fine-grained resource sharing in the data center. NSDI 11:22–22

Google Scholar  

. Delgado P, Dinu F, Kermarrec A-M, Zwaenepoel W (2015) Hawk: hybrid datacenter scheduling. In: USENIX ATC, 2015, pp 499–510

Peltz C (2003) Web services orchestration and choreography. IEEE Internet Comput 36(10):46–52

Arnautov S et al (2016) SCONE: Secure Linux containers with Intel SGX. In: Proceedings of 12th USENIX symposium on operating systems design and implementation, OSDI 2016, pp 689–703

I. R. Z. Michael Kaufmann, IBM Research Zurich, Karlsruhe Institute of Technology; Kornilios Kourtis (2017) The HCl scheduler: going all-in on heterogeneity. In: 9th {USENIX} workshop on hot topics in cloud computing (HotCloud 17), pp 1–7

Naha RK et al (2018) Fog computing: survey of trends, architectures, requirements, and research directions, vol 6, pp 47980–48009

Li X et al (2018) Holistic virtual machine scheduling in cloud datacenters towards minimizing total energy. IEEE Trans Parallel Distrib Syst 29:1317–1331

Vavilapallih V, Murthyh A, Douglasm C, Konarh M, Evansy R, Gravesy T, Lowey J, Sethh S, Sahah B, Curinom C, O’Malleyh O, Agarwali S, Shahh H, Radiah S, Reed B, Baldeschwieler E (2013) Apache Hadoop YARN. In: SoCC, 2013, pp 1–16

Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J (2016) Borg, omega, and kubernetes. Commun. ACM 59(5):50–57

Zaharia M, Das T, Li H, Hunter T, Shenker S, Stoica I (2013) Discretized streams: fault-tolerant streaming computation at scale. In: SOSP 2013—proceedings of the 24th ACM symposium on operating systems principles, no. 1, pp 423–438

Karanasos K, Rao S, Curino C, Douglas C, Chaliparambil K, Fumarola GM, Heddaya S, Ramakrishnan R, Sakalanaga S (2015) Mercury: hybrid centralized and distributed scheduling in large shared clusters. In: USENIX ATC, 2015, pp 485–497

Enslow PH (1978) What is a distributed data processing system? Computer 11(1):13–21

Gerard L (1977) Distributed systems—towards a formal approach. In: IFIP Congress, 1977

Algirdas Avižienis LC, Laprie J-C, Randell B (2004) Basic concepts and taxonomy of dependable and secure computing. IEEE Trans Dependable Secur Comput 1(1):11–33

Birrell AD, Nelson BJAY (1984) Implementing remote procedure calls. ACM Trans Comput Syst 2(1):39–59

Thain D, Tannenbaum T, Livny M (2005) Distributed computing in practice: the Condor experience. Concurr Comput Pract Exp 17(2–4):323–356

Lamport L, Shostak R, Pease M (1982) The Byzantine Generals Problem. ACM Trans Program Lang Syst 4(3):382–401

Figde C (1991) Logical time in distributed computing systems. Computer (Long Beach CA) 24:28–33

Friedemann M (1999) Virtual time and global states of distributed systems. SIAM J Comput 28(5):1829–1847

Sunderam VS, Geist GA, Dongarra J, Manchek R (1994) The PVM concurrent computing system: evolution, experiences, and trends. Parallel Comput 20(4):531–545

Gropp W (1998) An introduction to MPI parallel programming with the message passing interface, pp 1–48s

Gummadi PK, Saroiu S, Gribble SD (2002) A measurement study of Napster and Gnutella as examples of peer-to-peer file sharing systems. ACM SIGCOMM Comput Commun Rev 32(1):82–82

Anderson DP, Cobb J, Korpela E, Lebofsky M, Werthimer D (2002) Seti@home an experiment in public-resource computing. Commun ACM 45(11):56–61

Fazio M, Celesti A, Ranjan R, Liu C, Chen L, Villari M (2016) (2016) Open issues in scheduling microservices in the cloud the types of devices that might. IEEE Cloud Comput 3(5):81–88

Foster I, Zhao Y, Raicu I, Lu S (2008) Cloud computing and grid computing 360-degree compared. In: Grid computing environ work GCE 2008, pp 1–10

Mell P, Grance T (2011) The NIST definition of cloud computing recommendations of the National Institute of Standards and Technology. Nist Spec Publ 145:7

Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: issues and challenges. J Grid Comput 14(2):217–264

Baheti R, Gill H (2011) Cyber-physical systems. Impact Control Technol 1:161–166

Karnouskos S (2011) Cyber-physical systems in the SmartGrid. In: 2011 9th international conference on industrial informatics, vol 1 VN-re, 2011

Evans D (2011) The internet of things—how the next evolution of the internet is changing everything. In: CISCO white paper, no. April, pp 1–11

Cerf VG, RE Icahn (1974) A protocol for packet network intercommunication. In: ACM SIGCOMM computer communication review 71 vol 35, number 2, April 2005, pp 71–82

Mockapetris Paul DK (1988) Development of the domain name system. In: SIGCOMM ’88 Symposium, Communication, Architectures and Protocols, 1988

Flynn MJ (1966) Very high-speed computing systems. Proc IEEE 54(12):1901–1909

Singh S, Chana I, Singh M (2017) The journey of QoS-aware autonomic cloud computing. IT Prof 19(2):42–49

Casavant TL, Kuhl JG (1988) A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Trans Soft Eng 14(2):141–154

Compton K, Hauck S (2002) Reconfigurable computing : a survey of systems and software. 34(2):171–210

Amdahl GM (1967) Validity of the single processor approach to achieving large scale computing capabilities. In: AFIPS spring joint computer conference, pp 1–4

Lindsay D, Gill SS, Garraghan P (2019) PRISM: an experiment framework for straggler analytics in containerized clusters. In: Proceedings of the 5th international workshop on container technologies and container clouds, pp 13–18

Yu J, Buyya R A taxonomy of workflow management systems for grid computing, pp 1–31

Foster I, Kesselman C, Tuecke S (2001) The anatomy of the grid. Hand Clin 17(4):525–532

Sterling T, Becker DJ, Savarase D, Dorband JE, Ranawake UA, Packer CV (1995) BEOWULF: a parallel workstation for scientific computation. In: Proceedings of the 24th international conference on parallel processing, pp 2–5

Gill SS, Ouyang X, Garraghan P (2020) Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centres. J Supercomput 50:10050–10089

Singh S, Chana I (2015) QoS-aware autonomic resource management in cloud computing: a systematic review. 48(3)

Leiner BM et al (2000s) Internet society (ISOC) all about the internet : a brief history of the internet internet society (ISOC) all about the internet : a brief history of the internet, pp 1–18

Gill SS et al (2019) Transformative effects of IoT, blockchain and artificial intelligence on cloud computing: evolution, vision, trends and open challenges. Internet Things 8:100118

Whitmore A, Agarwal A, Da Xu L (2015) The internet of things—a survey of topics and trends. no. March 2014, pp 261–274

Gill SS, Garraghan P, Buyya R (2019) ROUTER: Fog enabled cloud based intelligent resource management approach for smart home IoT devices. J Syst Softw 154:125–138

Brogi A, Forti S, Guerrero C, Lera I (2019) How to place your apps in the fog—state of the art and open challenges

Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge Computing: Vision and Challenges. IEEE Internet Things J. 3(5):637–646

Waldrop M (2016) The chips are down for Moore’s law. Nature 530:144

Verma A, Pedrosa L, Korupolu M, Oppenheimer D, Tune E, Wilkes J (2015) Large-scale cluster management at google with Borg. In: Proceedings of the tenth European conference on computer systems, EuroSys ’15. ACM, New York, pp 18:1–18:17

Gog I, Schwarzkopf M, Gleave A, Watson RMN, Hand S (201) Firmament: fast, centralized cluster scheduling at scale. In: Proceedings of 12th USENIX symposium on operating systems design and implementation, 2016, pp 99–115

Ousterhout K, Wendell P, Zaharia M, Stoica I (2013) Sparrow: distributed, low latency scheduling. In: Proceedings of the 24th ACM symposium on operating systems principles, 2013, pp 69–84

Blair G (2018) Complex distributed systems: the need for fresh perspectives. In: IEEE ICDCS, pp 1410–1421

Xiao W et al (2018) Gandiva, introspective cluster scheduling for deep learning. In: OSDI, 2018

Gill SS, Shaghaghi A (2020) Security-aware autonomic allocation of cloud resources: a model, research trends, and future directions. J Organ End User Comput (JOEUC) 32(3):15–22

Garraghan P et al (2018) Emergent failures: rethinking cloud reliability at scale. IEEE Cloud Comput 5:12–21

Gao J (2014) Machine learning applications for data center optimization. Google White Paper, 2014

Liao X (2018) Moving from Exascale to Zettascale computing: challenges and techniques. Front Inf Technol Electron Eng 19:1236–1244

Van Heddeghem W, Lambert S, Lannoo B, Colle D, Pickavet M, Demeester P (2014) Trends in worldwide ICT electricity consumption from 2007 to 2012. Comput Commun 50:64–76

Gossart C (2014) Rebound effects and ICT: a review of the literature. In: ICT innovations for sustainability, pp 435–448

IPCC (2018) Global warming of 1.5 °C. Intergovernmental Panel on Climate Change, 2018

Chandra A, Weissman J, Heintz B (2013) Decentralized edge clouds. IEEE Internet Computing 17(5):70–73

Ferrer AJ, Manuel Marquès J, Jorba J (2019) Towards the decentralised cloud: survey on approaches and challenges for mobile, ad hoc, and edge computing. ACM Comput Surv 51(6):1–36

Khan MA, Algarni F, Quasim MT (2020) Decentralised internet of things. In: Decentralised internet of things. Springer, Cham, pp 3–20

Psaras I (2018) Decentralised edge-computing and IoT through distributed trust. In: Proceedings of the 16th annual international conference on mobile systems, applications, and services, pp 505–507

Alqahtani A, Solaiman E, Patel P, Dustdar S, Ranjan R (2019) Service level agreement specification for end-to-end IoT application ecosystems. Softw Pract Exp 49(12):1689–1711

Xiao W, Bhardwaj R, Ramjee R, Sivathanu M, Kwatra N, Han Z, Patel P, Peng X, Zhao H, Zhang Q, Yang F, Zhou L (2018) Gandiva: introspective cluster scheduling for deep learning. In: Proceedings of the 13th USENIX conference on operating systems design and implementation (OSDI’18). USENIX Association, USA, pp 595–610

Gill SS, Garraghan P, Stankovski V, Casale G, Thulasiram RK, Ghosh SK, Ramamohanarao K, Buyya R (2019) Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge. J Syst Softw 155:104–129

Yang R, Hu C, Sun X, Garraghan P, Wo T, Wen Z, Peng H, Xu J, Li C (2020) Performance-aware speculative resource oversubscription for large-scale clusters. IEEE Trans Parallel Distrib Syst 31(7):1499–1517

Ma K, Li X, Chen W, Zhang C, Wang X (2012) GreenGPU: a holistic approach to energy efficiency in GPU-CPU heterogeneous architectures. In: Proceedings of international conference on parallel processing, pp 48–57

Gill SS, Tuli S, Toosi AN, Cuadrado F, Garraghan P, Bahsoon R, Lutfiyya H et al (2020) ThermoSim: deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments. J Syst Softw 164:110596

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Acknowledgements

This work is supported by the UK Engineering and Physical Sciences Research Council (EP/P031617/1).

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Lindsay, D., Gill, S.S., Smirnova, D. et al. The evolution of distributed computing systems: from fundamental to new frontiers. Computing 103 , 1859–1878 (2021). https://doi.org/10.1007/s00607-020-00900-y

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Received : 06 February 2020

Accepted : 28 December 2020

Published : 30 January 2021

Issue Date : August 2021

DOI : https://doi.org/10.1007/s00607-020-00900-y

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Dear Colleagues,

Over the last few decades, trends in the computing industry have been towards distributed, low-cost, and high-volume units. Therefore, this Special Issue is dedicated to distributed systems, whose components are located on different networked computers and which communicate and coordinate their actions by passing messages to one another. Currently, there is a wide spectrum of types of distributed systems varying from SOA-based systems to massively multiplayer online games and peer-to-peer applications.

The control of distributed systems is a well-known challenge which requires complex computational software referred to as distributed computing. Therefore, authors should demonstrate new methods allowing to increase distributed system performance, for instance, by rebalancing resource loads and thereby avoiding networking failures caused by node overstrain.

Particularly welcome will be works that validate, at the experimental level, improved networking performance by managing resource loads and hence preventing system failures. Since such systems are generally required to operate across the Internet and different administrative domains, new algorithms fulfilling these scalability requirements without loss of performance will be a valuable contribution to the Special Issue.

We invite authors interested in the proposed topics to contribute to this Special Issue by publishing their results of research related, but not limited, to the following topics: multiprocessing, multicomputing, cybersecurity for distributed systems applications, programming paradigms for distributed systems, and load balancing algorithms.

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Introductory Chapter: The Newest Research in Parallel and Distributed Computing

Submitted: 14 September 2016 Published: 19 July 2017

DOI: 10.5772/intechopen.69201

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Recent Progress in Parallel and Distributed Computing

Edited by Wen-Jyi Hwang

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Wen-jyi hwang *.

• Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan

*Address all correspondence to: [email protected]

The parallel and distributed computing is concerned with concurrent use of multiple compute resources to enhance the performance of a distributed and/or computationally intensive application. The compute resources may be a single computer or a number of computers connected by a network. A computer in the system may contain single-core, multi-core and/or many-core processors. The design and implementation of a parallel and distributed system may involve the development, utilization and integration of techniques in computer network, software and hardware. With the advent of networking and computer technology, parallel and distributed computing systems have been widely employed for solving problems in engineering, management, natural sciences and social sciences.

There are six chapters in this book. From Chapters 2 to 6, a wide range of studies in new applications, algorithms, architectures, networks, software implementations and evaluations of this growing field are covered. These studies may be useful to scientists and engineers from various fields of specialization who need the techniques of distributed and parallel computing in their work.

The second chapter of this book considers the applications of distributed computing for social networks. The chapter entitled “A Study on the Node Influential Capability in Social Networks by Incorporating Trust Metrics” by Tong-Ming Lim and Hock Yeow Yap provides useful distributed computing models for the evaluation of node influential capacity in social networks. Two algorithms are presented in this study: Trust-enabled Generic Algorithm Diffusion Model (T-GADM) and Domain-Specified Trust-enabled Generic Algorithm Diffusion Model (DST-GADM). Experimental results confirm the hypothesis that social trust plays an important role in influential propagation. Moreover, it is able to increase the rate of success in influencing other social nodes in a social network.

Another application presented in this book is the smart grid for power engineering. The chapter entitled “A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids” by Ignacio Aravena, Anthony Papavasiliou and Alex Papalexopoulos analyzes the distributed system for the management of the short-term operations of power systems. They propose optimization algorithms for both the levels of the distribution grid and high voltage grids. Numerical results are also included for illustrating the effectiveness of the algorithms.

This book also contains a chapter covering the programming aspect of parallel and distributed computing. For the study of parallel programming, the general processing units (GPUs) are considered. GPUs have received attention for parallel computing because their many-core capability offers a significant speedup over traditional general purpose processors. In the chapter entitled “GPU Computing Taxonomy” by Abdelrahman Ahmed Mohamed Osman, a new classification mechanism is proposed to facilitate the employment of GPU for solving the single program multiple data problems. Based on the number of hosts and the number of devices, the GPU computing can be separated into four classes. Examples are included to illustrate the features of each class. Efficient coding techniques are also provided.

The final two chapters focus on the software aspects of the distributed and parallel computing. Software tools for the wikinomics-oriented development of scientific applications are presented in the chapter entitled “Distributed Software Development Tools for Distributed Scientific Applications” by Vaidas Giedrimas, Anatoly Petrenko and Leonidas Sakalauskas. The applications are based on service-oriented architectures. Flexibilities are provided so that codes and components deployed can be reused and transformed into a service. Some prototypes are given to demonstrate the effectiveness of the proposed tools.

The chapter entitled “DANP-Evaluation of AHP-DSS” by Wolfgang Ossadnik, Benjamin Föcke and Ralf H. Kaspar evaluates the Analytic Hierarchy Process (AHP)-supporting software for the use of adequate Decision Support Systems (DSS) for the management science. The corresponding software selection, evaluation criteria, evaluation framework, assessments and evaluation results are provided in detail. Issues concerning the evaluation assisted by parallel and distributed computing are also addressed.

These chapters offer comprehensive coverage of parallel and distributed computing from engineering and science perspectives. They may be helpful to further stimulate and promote the research and development in this rapid growing area. It is also hoped that newcomers or researchers from other areas of disciplines desiring to learn more about the parallel and distributed computing will find this book useful.

© 2017 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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