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Journal articles on the topic 'Mobile Computing, Software Architecture'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Alreshidi, Ahmad, B. Altamimi, Sultan, and Mehmood. "Software Architecture for Mobile Cloud Computing Systems." Future Internet 11, no. 11 (November 13, 2019): 238. http://dx.doi.org/10.3390/fi11110238.

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Mobile cloud computing (MCC) has recently emerged as a state-of-the-art technology for mobile systems. MCC enables portable and context-aware computation via mobile devices by exploiting virtually unlimited hardware and software resources offered by cloud computing servers. Software architecture helps to abstract the complexities of system design, development, and evolution phases to implement MCC systems effectively and efficiently. This paper aims to identify, taxonomically classify, and systematically map the state of the art on architecting MCC-based software. We have used an evidence-based software engineering (EBSE) approach to conduct a systematic mapping study (SMS) based on 121 qualitatively selected research studies published from 2006 to 2019. The results of the SMS highlight that architectural solutions for MCC systems are mainly focused on supporting (i) software as a service for mobile computing, (ii) off-loading mobile device data to cloud-servers, (iii) internet of things, edge, and fog computing along with various aspects like (iv) security and privacy of mobile device data. The emerging research focuses on the existing and futuristic challenges that relate to MCC-based internet of things (IoTs), mobile-cloud edge systems, along with green and energy-efficient computing. The results of the SMS facilitate knowledge transfer that could benefit researchers and practitioners to understand the role of software architecture to develop the next generation of mobile-cloud systems to support internet-driven computing.

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2

Duchamp, Daniel. "Systems Software for Wireless Mobile Computing." ACM SIGOPS Operating Systems Review 26, no. 2 (April 1992): 10. http://dx.doi.org/10.1145/142111.964563.

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3

Bagchi, Susmit. "The software architecture for designing interactive mobile computing applications." International Journal of Ad Hoc and Ubiquitous Computing 10, no. 4 (2012): 230. http://dx.doi.org/10.1504/ijahuc.2012.049069.

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4

Liuyang, Zhang, J. Vijayashree, J. Jayashree, Ronnie D. Caytiles, and N. Ch S. N. Iyengar. "Architecture for Mobile Cloud Computing via Middle-Ware Technologies." International Journal of Software Engineering and Its Applications 10, no. 12 (December 31, 2016): 249–58. http://dx.doi.org/10.14257/ijseia.2016.10.12.21.

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5

Alkhalil, Adel. "Evolution of existing software to mobile computing platforms: Framework support and case study." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 3 (March 2021): 100–111. http://dx.doi.org/10.21833/ijaas.2021.03.013.

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Mobile computing as ubiquitous and pervasive technology supports portable and context-aware computation. To date, there exist a significant number of traditional computing systems–running on the web and/or workstation-based platforms–that lack features of mobile computing, including but not limited to ubiquity, context-sensing, and high interactivity. Software that executes on these traditional computing systems is referred to as legacy software that can be upgraded to exploit the features of mobile technologies. However, legacy software may contain critical data, logic, and processes that cannot be easily replaced. One of the solutions is to evolve legacy software systems by (a) upgrading their functionality while (b) preserving their data and logic. Recently research and development efforts are focused on modernizing the legacy systems as per the needs of service and cloud-based platforms. However, there does not exist any research that supports a systematic modernization of legacy software as per the requirements of the mobile platforms. We propose a framework named Legacy-to-Mobile as a solution that supports an incremental and process-driven evolution of the legacy software to mobile computing software. The proposed Legacy-to-Mobile framework unifies the concepts of software reverse engineering (recovering software artifacts) and software change (upgrading software artifacts) to support the legacy evolution. The framework follows an incremental approach with four processes that include (i) evolution planning, (ii) architecture modeling, (iii) architecture change, and (iv) software validation of mobile computing software. The framework provides the foundation (as part of futuristic research) to develop a tool prototype that supports automation and user decision support for incremental and process-driven evolution of legacy software to mobile computing platforms.

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6

Ekberg, Jan-Erik. "Mobile Trusted Computing Based on MTM." International Journal of Dependable and Trustworthy Information Systems 1, no. 4 (October 2010): 25–42. http://dx.doi.org/10.4018/jdtis.2010100102.

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Trusted computing (TC) denotes a set of security-related hardware and software mechanisms that make a computing device work in a consistent manner, even in the presence of external attacks. For personal computers, TC typically is interpreted to be a software architecture designed around the trusted platform module (TPM), a hardware chip residing on the motherboard and implemented according to the specifications of the Trusted Computing Group (Trusted Computing Group, 2008A). In embedded devices, the state-of-the art in terms of hardware security and operating systems is significantly different from what is present on personal computers. So to stimulate the take-up of TCG technology on handsets as well, the recently approved mobile trusted module (MTM) specification (Trusted Computing Group, 2008B) defines new interfaces and adaptation options that match the requirements of the handset business ecosystem, as well as the hardware in use in the embedded domain. This chapter provides an overview of a few hardware security architectures (in handsets) to introduce the reader to the problem domain. The main focus of the text is in introducing the MTM specification – by first presenting its main functional concepts, and then by adapting it to one of the hardware architectures first described, essentially presenting a plausible practical deployment. The author also presents a brief security analysis of the MTM component, and a few novel ideas regarding how the (mobile) trusted module can be extended, and be made more versatile.

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7

Rößling, Guido, Henning Bär, Christoph Trompler, and Chin-Man Choi. "Mobile computing in education." ACM SIGCSE Bulletin 35, no. 3 (September 2003): 227. http://dx.doi.org/10.1145/961290.961583.

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8

Faheem, Muhammad, Tahar Kechadi, and Nhien An Le-Khac. "The State of the Art Forensic Techniques in Mobile Cloud Environment." International Journal of Digital Crime and Forensics 7, no. 2 (April 2015): 1–19. http://dx.doi.org/10.4018/ijdcf.2015040101.

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Smartphones have become popular in recent days due to the accessibility of a wide range of applications. These sophisticated applications demand more computing resources in a resource constraint smartphone. Cloud computing is the motivating factor for the progress of these applications. The emerging mobile cloud computing introduces a new architecture to offload smartphone and utilize cloud computing technology to solve resource requirements. The popularity of mobile cloud computing is an opportunity for misuse and unlawful activities. Therefore, it is a challenging platform for digital forensic investigations due to the non-availability of methodologies, tools and techniques. The aim of this work is to analyze the forensic tools and methodologies for crime investigation in a mobile cloud platform as it poses challenges in proving the evidence.

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9

Lu, Chao-ze, Guo-sun Zeng, and Ying-jie Xie. "Bigraph specification of software architecture and evolution analysis in mobile computing environment." Future Generation Computer Systems 108 (July 2020): 662–76. http://dx.doi.org/10.1016/j.future.2020.02.008.

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10

Huertas Celdrán, Alberto, Kallol Krishna Karmakar, Félix Gómez Mármol, and Vijay Varadharajan. "Detecting and mitigating cyberattacks using software defined networks for integrated clinical environments." Peer-to-Peer Networking and Applications 14, no. 5 (February 10, 2021): 2719–34. http://dx.doi.org/10.1007/s12083-021-01082-w.

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AbstractThe evolution of integrated clinical environments (ICE) and the future generations of mobile networks brings to reality the hospitals of the future and their innovative clinical scenarios. The mobile edge computing paradigm together with network function virtualization techniques and the software-defined networking paradigm enable self-management, adaptability, and security of medical devices and data management processes making up clinical environments. However, the logical centralized approach of the SDN control plane and its protocols introduce new vulnerabilities which affect the security of the network infrastructure and the patients’ safety. The paper at hand proposes an SDN/NFV-based architecture for the mobile edge computing infrastructure to detect and mitigate cybersecurity attacks exploiting SDN vulnerabilities of ICE in real time and on-demand. A motivating example and experiments presented in this paper demonstrate the feasibility of of the proposed architecture in a realistic clinical scenario.

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11

Liu, Jianghua, Jinhua Ma, Wei Wu, Xiaofeng Chen, Xinyi Huang, and Li Xu. "Protecting Mobile Health Records in Cloud Computing." ACM Transactions on Embedded Computing Systems 16, no. 2 (April 14, 2017): 1–20. http://dx.doi.org/10.1145/2983625.

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12

Payvar, Saman, Maxime Pelcat, and Timo D. Hämäläinen. "A model of architecture for estimating GPU processing performance and power." Design Automation for Embedded Systems 25, no. 1 (January 16, 2021): 43–63. http://dx.doi.org/10.1007/s10617-020-09244-4.

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AbstractEfficient usage of heterogeneous computing architectures requires distribution of the workload on available processing elements. Traditionally, the mapping is based on information acquired from application profiling and utilized in architecture exploration. To reduce the amount of manual work required, statistical application modeling and architecture modeling can be combined with exploration heuristics. While the application modeling side of the problem has been studied extensively, architecture modeling has received less attention. Linear System Level Architecture (LSLA) is a Model of Architecture that aims at separating the architectural concerns from algorithmic ones when predicting performance. This work builds on the LSLA model and introduces non-linear semantics, specifically to support GPU performance and power modeling, by modeling also the degree of parallelism. The model is evaluated with three signal processing applications with various workload distributions on a desktop GPU and mobile GPU. The measured average fidelity of the new model is 93% for performance, and 84% for power, which can fit design space exploration purposes.

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13

Ren, Jinke, Yinghui He, Guan Huang, Guanding Yu, Yunlong Cai, and Zhaoyang Zhang. "An Edge-Computing Based Architecture for Mobile Augmented Reality." IEEE Network 33, no. 4 (July 2019): 162–69. http://dx.doi.org/10.1109/mnet.2018.1800132.

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14

Finochietto, Mariano, Gabriel M. Eggly, Rodrigo Santos, Javier Orozco, Sergio F. Ochoa, and Roc Meseguer. "A Role-Based Software Architecture to Support Mobile Service Computing in IoT Scenarios." Sensors 19, no. 21 (November 5, 2019): 4801. http://dx.doi.org/10.3390/s19214801.

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The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.

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15

Khan, Asmat Ullah, and Susmit Bagchi. "Software architecture and algorithm for reliable RPC for geo-distributed mobile computing systems." Future Generation Computer Systems 86 (September 2018): 185–98. http://dx.doi.org/10.1016/j.future.2018.04.023.

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16

Kin Yeung Wong. "Cell Phones as Mobile Computing Devices." IT Professional 12, no. 3 (May 2010): 40–45. http://dx.doi.org/10.1109/mitp.2010.46.

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17

Thimm, Heiko. "Cloud-Based Collaborative Decision Making." International Journal of Decision Support System Technology 4, no. 4 (October 2012): 39–59. http://dx.doi.org/10.4018/jdsst.2012100103.

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The complexity of many decision problems of today’s globalized world requires new innovative solutions that are built upon proven decision support technology and also recent advancements in the area of information and communication technology (ICT) such as Cloud Computing and Mobile Communication. A combination of the cost-effective Cloud Computing approach with extended group decision support system technology bears several interesting unprecedented opportunities for the development of such solutions. These opportunities include ubiquitous accessibility to decision support software and, thus, the possibility to flexibly involve remote experts in group decision processes, guided access to background information, and facilitation support to direct group decision processes. The architects of such future solutions are challenged by numerous requirements that need to be considered and reflected in an integrated architectural approach. This article presents a thorough analysis of major design considerations for software solutions for collaborative decision making from a broad range of perspectives especially including the business process management perspective and the Cloud Computing perspective. The proposed architectural approach of the GRUPO-MOD system demonstrates how one can address the requirements in one integrated system architecture that supports different deployment options of Cloud Computing. A refinement of the high-level system architecture into a corresponding implementation architecture that builds on widely adopted standards such as OSGi and industry proven technology such as the Eclipse platform is also given in the article.

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18

Fei, Yunsi, Lin Zhong, and Niraj K. Jha. "An energy-aware framework for dynamic software management in mobile computing systems." ACM Transactions on Embedded Computing Systems 7, no. 3 (April 2008): 1–31. http://dx.doi.org/10.1145/1347375.1347380.

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19

Rodger, James, Parag Pendharkar, and Mehdi Khosrow-Pour. "Mobile Computing at the Department of Defense." Journal of Database Management 12, no. 2 (April 2001): 36–48. http://dx.doi.org/10.4018/jdm.2001040104.

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20

Lim, Sung-Hwa, and J. H. Kim. "Real-time broadcast algorithm for mobile computing." Journal of Systems and Software 69, no. 1-2 (January 2004): 173–81. http://dx.doi.org/10.1016/s0164-1212(03)00083-9.

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21

Huang, Kuo-Hsuan, Yu-Fang Chung, Chia-Hui Liu, Feipei Lai, and Tzer-Shyong Chen. "Efficient migration for mobile computing in distributed networks." Computer Standards & Interfaces 31, no. 1 (January 2009): 40–47. http://dx.doi.org/10.1016/j.csi.2007.10.011.

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22

Hsu, Chien-Lung, and Yu-Li Lin. "Improved migration for mobile computing in distributed networks." Computer Standards & Interfaces 36, no. 3 (March 2014): 577–84. http://dx.doi.org/10.1016/j.csi.2013.10.006.

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23

Jiao, Zhenzhen, Baoxian Zhang, Li Zhang, Min Liu, Wei Gong, and Cheng Li. "A Blockchain-Based Computing Architecture for Mobile Ad Hoc Cloud." IEEE Network 34, no. 4 (July 2020): 140–49. http://dx.doi.org/10.1109/mnet.001.1800064.

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24

Sharma, Shivi, and Hemraj Saini. "Efficient Solution for Load Balancing in Fog Computing Utilizing Artificial Bee Colony." International Journal of Ambient Computing and Intelligence 10, no. 4 (October 2019): 60–77. http://dx.doi.org/10.4018/ijaci.2019100104.

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Fog computing is a set of mobile cloudlets which can fulfil the demand of the user who is already considered a mobile job in this architecture. The main aim of Fog computing is to provide the user with an optimal solution which is quick and cost-efficient. This article focuses on a load balancing mechanism for cloudlets along with keeping the cost-effectiveness as an optimal selection parameter. This article utilizes the Artificial Bee Colony (ABC) in order to prioritize the user demand using a fitness function. This work evaluates quality of service (QoS) parameters such as schedule length runtime (SLR), schedule length vm ratio (SLVMR), energy consumed (EC) and energy consumption ratio (ECR) and shows the effectiveness of proposed work.

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25

Adamczyk, Piotr, Kevin Hamilton, Alan Chamberlain, Steve Benford, Nick Tandavanitj, Amanda Oldroyd, Kate Hartman, et al. "Urban Computing and Mobile Devices." IEEE Distributed Systems Online 8, no. 7 (July 2007): 2. http://dx.doi.org/10.1109/mdso.2007.46.

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26

Chen, Min, Yulei Wu, and Athanasios V. Vasilakos. "Advances in Mobile Cloud Computing." Mobile Networks and Applications 19, no. 2 (March 21, 2014): 131–32. http://dx.doi.org/10.1007/s11036-014-0503-1.

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27

Vahdat-Nejad, Hamed, Mohammad Sadeq Navabi, and Hosein Khosravi-Mahmouei. "A Context-Aware Museum-Guide System Based on Cloud Computing." International Journal of Cloud Applications and Computing 8, no. 4 (October 2018): 1–19. http://dx.doi.org/10.4018/ijcac.2018100101.

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This paper proposes a context-aware museum-guide system, which is able to present information on exhibits in accordance with visitor needs. Other features of the system include planning and directing a visit as well as providing locations, facilities and additional services. The architecture is designed based on the attribute-driven design method. It consists of two components including mobile and server components. The server component is located in the cloud and is regarded as the backup for the mobile component. The software architecture analysis method is employed to evaluate the proposed architecture. To this end, some scenarios have been codified for system functionalities by consulting museum experts. The proposed architecture satisfies the scenarios by resolving the issues arisen in each one. The CIF-based questionnaire method indicates a high usability score for the proposed system. Finally, comparison of the capabilities of the proposed system with related research reveals that it benefits from a comprehensive design.

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28

Liu, Zhen. "High-performance computing in mobile services." ACM SIGMETRICS Performance Evaluation Review 40, no. 1 (June 7, 2012): 3–4. http://dx.doi.org/10.1145/2318857.2254759.

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29

Satoh, Ichiro. "Reusable mobile agents for cluster computing." International Journal of High Performance Computing and Networking 2, no. 2/3/4 (2004): 77. http://dx.doi.org/10.1504/ijhpcn.2004.008894.

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30

Chen, Ching-Han, and Mu-Che Wu. "CellS: A Cell-Inspired Efficient Software Framework for AI-Enabled Application on Resources-Constrained Mobile System." Electronics 10, no. 5 (February 28, 2021): 568. http://dx.doi.org/10.3390/electronics10050568.

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Today’s mobile processors generally have multiple cores and sufficient hardware resources to support AI-enabled software operation. However, very few AI applications make full use of the computing performance of mobile multiprocessors. This is because the typical software development is sequential, and the degree of parallelism of the program is very low. In the increasingly complex AI-driven and software development projects with natural human–computer interaction, this will undoubtedly cause a waste of mobile computing resources that are originally limited. This paper proposes an intelligent system software framework, CellS, to improve smart software development on multicore mobile processor systems. This software framework mimics the cell system. In this framework, each cell can autonomously aware changes in the environment (input) and reaction (output) and may change the behavior of other cells. Smart software can be regarded as a large number of cells interacting with each other. Software developed based on the CellS framework has a high degree of scalability and flexibility and can more fully use multicore computing resources to achieve higher computing efficiency.

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31

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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32

Ellis, Carla Schlatter. "Controlling Energy Demand in Mobile Computing Systems." Synthesis Lectures on Mobile and Pervasive Computing 2, no. 1 (January 2007): 1–89. http://dx.doi.org/10.2200/s00089ed1v01y200704mpc002.

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33

Li, Guo-Hui, and Hong-Ya Wang. "A novel min-process checkpointing scheme for mobile computing systems." Journal of Systems Architecture 51, no. 1 (January 2005): 45–61. http://dx.doi.org/10.1016/j.sysarc.2004.07.001.

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34

Li, Keqin. "Heuristic Computation Offloading Algorithms for Mobile Users in Fog Computing." ACM Transactions on Embedded Computing Systems 20, no. 2 (January 4, 2021): 1–28. http://dx.doi.org/10.1145/3426852.

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35

Mikkonen, Tommi, and Antero Taivalsaari. "Cloud computing and its impact on mobile software development: Two roads diverged." Journal of Systems and Software 86, no. 9 (September 2013): 2318–20. http://dx.doi.org/10.1016/j.jss.2013.01.063.

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36

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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37

Meng, Zhaozong. "Investigation of a hierarchical context-aware architecture for rule-based customisation of mobile computing service." Journal of Ambient Intelligence and Smart Environments 6, no. 6 (2014): 739–40. http://dx.doi.org/10.3233/ais-140280.

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38

PARK, E. "Cooperative Reconfiguration of Software Components for Power-Aware Mobile Computing." IEICE Transactions on Information and Systems E89-D, no. 2 (February 1, 2006): 498–507. http://dx.doi.org/10.1093/ietisy/e89-d.2.498.

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39

Liu, Zongkai, Xiaoqiang Yang, and Jinxing Shen. "Optimization of multitask parallel mobile edge computing strategy based on deep learning architecture." Design Automation for Embedded Systems 24, no. 3 (July 12, 2019): 129–43. http://dx.doi.org/10.1007/s10617-019-09222-5.

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40

Zhao, Xianlong, Kexin Yang, Qimei Chen, Duo Peng, Hao Jiang, Xianze Xu, and Xinzhuo Shuang. "Deep learning based mobile data offloading in mobile edge computing systems." Future Generation Computer Systems 99 (October 2019): 346–55. http://dx.doi.org/10.1016/j.future.2019.04.039.

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41

Aminzadeh, Nazanin, Zohreh Sanaei, and Siti Hafizah Ab Hamid. "Mobile storage augmentation in mobile cloud computing: Taxonomy, approaches, and open issues." Simulation Modelling Practice and Theory 50 (January 2015): 96–108. http://dx.doi.org/10.1016/j.simpat.2014.05.009.

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42

Gao, Honghao, and Yuyu Yin. "Editorial: ACM/Springer Mobile Networks & Applications - Special Issue on Mobile Computing and Software Engineering." Mobile Networks and Applications 25, no. 2 (December 12, 2019): 672–73. http://dx.doi.org/10.1007/s11036-019-01451-z.

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43

Bai, Yuebin, and Yanwen Ju. "MOVE: A mobile personalized virtual computing environment." Future Generation Computer Systems 28, no. 6 (June 2012): 890–99. http://dx.doi.org/10.1016/j.future.2010.12.009.

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44

Deboosere, L., P. Simoens, J. De Wachter, B. Vankeirsbilck, F. De Turck, B. Dhoedt, and P. Demeester. "Grid design for mobile thin client computing." Future Generation Computer Systems 27, no. 6 (June 2011): 681–93. http://dx.doi.org/10.1016/j.future.2010.12.010.

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45

Xiao, Surong, Chubo Liu, Kenli Li, and Keqin Li. "System delay optimization for Mobile Edge Computing." Future Generation Computer Systems 109 (August 2020): 17–28. http://dx.doi.org/10.1016/j.future.2020.03.028.

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Chen, Xu, Zhi Zhou, Weigang Wu, Di Wu, and Junshan Zhang. "Socially-Motivated Cooperative Mobile Edge Computing." IEEE Network 32, no. 6 (November 2018): 177–83. http://dx.doi.org/10.1109/mnet.2018.1700354.

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47

Yang, Xiaoxian, and Ying Li. "Editorial: Urban Computing in Mobile Environment." Mobile Networks and Applications 25, no. 4 (May 11, 2020): 1193–94. http://dx.doi.org/10.1007/s11036-020-01533-3.

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48

Pan, Wenwen, Bei Liu, and Zhiliang Song. "Edge Computing-Induced Caching Strategy for National Traditional Sports Video Resources by Considering Unusual Items." International Journal of Distributed Systems and Technologies 12, no. 2 (April 2021): 1–12. http://dx.doi.org/10.4018/ijdst.2021040101.

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In order to promote the development of national traditional sports to carry forward the spirit and culture of a country or nation, this paper designs a system for national traditional sports video distribution with the help of software-defined network and mobile edge computing technologies. Thus, the popular national traditional sports resources can be cached in mobile edge computing servers, which can reduce the delay time from cloud center directly. In order to improve the hit rate of the cached videos, the ant colony-stimulated annealing is used as the caching strategy. The experimental results show that the ant colony-stimulated annealing caching strategy can increase the hit rate of the contents in mobile edge computing servers as well as decrease the delay time of the request videos. The ant colony-stimulated annealing caching strategy performs better than previous caching strategies for updating contents in mobile edge computing servers.

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49

Shen, Hang, Guangwei Bai, Yujia Hu, and Tianjing Wang. "P2TA: Privacy-preserving task allocation for edge computing enhanced mobile crowdsensing." Journal of Systems Architecture 97 (August 2019): 130–41. http://dx.doi.org/10.1016/j.sysarc.2019.01.005.

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50

Alakbarov, Rashid G. "Challenges of Mobile Devices’ Resources and in Communication Channels and their Solutions." International Journal of Computer Network and Information Security 13, no. 1 (February 8, 2021): 39–46. http://dx.doi.org/10.5815/ijcnis.2021.01.04.

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The article is dedicated to the development of cloudlet based mobile cloud computing (MCC) to address the restrictions that occur in the resources of mobile devices (energy consumption, computing and memory resources, etc.) and the delays occurring in communication channels. The architecture offered in the article more efficiently ensures the demand of mobile devices for computing and storage and removes the latency that occur in the network. At the same time, the tasks related to energy saving and eliminating delays in communication channels by solving the problems that require complex computing and memory resources in the cloudlets located nearby the user were outlined in the article.

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