Survey Special Issues

Game theory and evolutionary optimization approaches applied to resource allocation problems in computing environments: A survey

  • Received: 29 July 2021 Accepted: 29 September 2021 Published: 25 October 2021
  • Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they cannot develop and adaptively manage the conditions optimally. To optimize the resource allocation with minimal delay, low energy consumption, minimum computational complexity, high scalability, and better resource utilization efficiency, CC/FC/EC/IoT-based computing architectures should be designed intelligently. Therefore, the objective of this research is a comprehensive survey on resource allocation problems using computational intelligence-based evolutionary optimization and mathematical game theory approaches in different computing environments according to the latest scientific research achievements.

    Citation: Shahab Shamshirband, Javad Hassannataj Joloudari, Sahar Khanjani Shirkharkolaie, Sanaz Mojrian, Fatemeh Rahmani, Seyedakbar Mostafavi, Zulkefli Mansor. Game theory and evolutionary optimization approaches applied to resource allocation problems in computing environments: A survey[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9190-9232. doi: 10.3934/mbe.2021453

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  • Today's intelligent computing environments, including the Internet of Things (IoT), Cloud Computing (CC), Fog Computing (FC), and Edge Computing (EC), allow many organizations worldwide to optimize their resource allocation regarding the quality of service and energy consumption. Due to the acute conditions of utilizing resources by users and the real-time nature of the data, a comprehensive and integrated computing environment has not yet provided a robust and reliable capability for proper resource allocation. Although traditional resource allocation approaches in a low-capacity hardware resource system are efficient for small-scale resource providers, for a complex system in the conditions of dynamic computing resources and fierce competition in obtaining resources, they cannot develop and adaptively manage the conditions optimally. To optimize the resource allocation with minimal delay, low energy consumption, minimum computational complexity, high scalability, and better resource utilization efficiency, CC/FC/EC/IoT-based computing architectures should be designed intelligently. Therefore, the objective of this research is a comprehensive survey on resource allocation problems using computational intelligence-based evolutionary optimization and mathematical game theory approaches in different computing environments according to the latest scientific research achievements.



    Chronic kidney disease (CKD) is a healthcare burden due to the high economic costs it generates for health systems and its high incidence and prevalence. The mortality of CKD has increased in the last 10 years. It is currently the 12th most common cause of death according to the Global Burden of Disease Study in 2015 and also one of the fastest rising major causes of mortality, along with diabetes and dementia [1][3]. CKD is defined as abnormalities of kidney structure or function present for over 3 months with specific implications for health [4]. An expanded definition of CKD includes a glomerular filtration rate of less than 60 mL/min/1.73m2 and a 1-time urine albumin-creatinine ratio of at least 30 mg/g with or without kidney damage, or more markers of kidney failure [4][6]. The clinical progression of the disease is described in 5 stages. In the most advanced stage, kidney replacement therapy is proposed to patients in the form of hemodialysis or peritoneal dialysis [7].

    CKD is associated with many health consequences, including metabolic, endocrine and cardiovascular alterations. It is also strongly associated with pulmonary edema and respiratory muscle dysfunction, leading to a high risk of lung dysfunction in affected patients. The prevalence of lung dysfunction increases in CKD patients from stages 1 to 4 according to the National Health and Nutrition Examination Survey (NHANES) 2007–2012 [8],[9].

    Due to its progressive nature, CKD affects patients in many spheres along the course of the disease including physical, mental and emotional well-being. It changes their daily living and social participation and decreases their perception of quality of life (QOL). Patients with CKD have to change their lifestyles, habits and nutrition and adjust to medical treatments and physical limitations. They experience existential and emotional conflicts, among other health situations and biopsychosocial changes that negatively impact their QOL [7]. In addition, QOL is a marker of disease burden and the assessment of QOL is an important criterion of the effectiveness of many treatments and interventions in health care and a predictor for adverse outcomes [10],[11].

    According to the World Health Organization (WHO) [12] and the American College of Sports Medicine [13], regular exercise training has been proven to help populations maintain healthy levels of quality of life, mitigating health risks. It is also considered safe for adults living with the selected chronic conditions.

    Different exercise modes are very popular in the health and fitness industry at European [14] and global level [15]. Respiratory training has been included in trendy exercise modes and is currently used in a wide range of populations [16],[17]. In fact, breathing training techniques have shown positive effects on health in patients with different conditions such as chronic obstructive pulmonary disease (COPD), asthma, postoperative pulmonary function, and cardiorespiratory function, among others [18].

    However, it is unclear what kind of information is available in the literature about the effects of breathing training on patients with CKD in need of dialysis treatment. For these reasons, a scoping review was conducted to systematically map the research conducted in this area and identify any existing gaps in knowledge.

    The objectives of this study were to examine the characteristics related to the application of breathing training on patients with CKD, and to identify the relevant outcomes and target group for the application of breathing training. Additionally, this scoping review was aimed at developing and confirming our prior inclusion criteria to ensure that the question asked by the subsequent systematic review could be answered by available and relevant evidence.

    This scoping review is reported following the Preferred Reporting Items for Scoping Reviews (PRISMA ScR) guidelines [19] and was registered in the Prospective Register of Systematic Reviews (PROSPERO) with identification number CRD42021288231. Additionally, we followed the method suggested by Arksey and O'Malley [20] as standard steps for the development of scoping reviews.

    We applied the recommended use of the PCC mnemonic (Population, Concept and Context) to guide question development [21]. The inclusion criteria were (1) patients with chronic kidney disease (2) who received breathing training programs and (3) the breathing training intervention had to be compared to a control group that received usual care or no treatment.

    We conducted a broad search of the literature for indexed articles on electronic databases MEDLINE/PubMed, Web of Science and Scopus from their inception to March 2022. The search strategy was designed using the following steps: (1) examining relevant key terms used in existing systematic reviews to develop our keywords, (2) a thorough search for terms in the MeSH Database, (3) and expert guidance by a specialist. The strategy was adapted to index across other databases. We screened the references of relevant reviews to screen for additional studies that could potentially be included in this scoping review. The full search strategy is shown in Appendix A.

    All the searched citations were stored in the Mendeley Desktop 1.19.4 reference manager application. Duplicated studies retrieved from electronic searches were removed. Two independent researchers screened the titles and abstracts of articles found in the searches (A.I.R., A.H.C.). Studies appearing to meet the inclusion criteria and those with insufficient data to make a clear decision were selected for evaluation of the full manuscript to determine their eligibility. Disagreement was solved by a third researcher (C.V.).

    We charted key items of information obtained from the primary research reports reviewed. Data extraction was performed by one of the researchers through a custom-designed data extraction form created in Excel (Microsoft Corporation, Redmond, WA), using ‘data charting form’. This form included information on the study population, the type of intervention and the outcome measures employed. We recorded information as follows:

    • Author(s), year of publication
    • Design
    • Pathology treatment status
    • Study populations
    • Intervention type, and comparator (if any); duration of the intervention
    • Outcome measures
    • Important results

    Two authors independently assessed the methodological quality and the risk of bias of individual studies. We used the Downs and Black Checklist [22] to assess methodological quality. This assessment method includes 27 items in five subscales (study quality, external validity, study bias, confounding and selection bias, and study power). It classifies the quality of studies as follows: excellent when scoring 26 or more points, good between 20 and 25 points, fair between 15 and 19, and poor when the score is 14 or less. Due to its high validity and reliability, this scale is one of the most suitable scales for use in research reviews [23],[24].

    The risk of bias was assessed with the Cochrane risk-of-bias tool for randomized control trials [25]. The items of this tool classify the risk of bias as high when the methodological procedure is not described, unclear if the description is unclear, and low when the procedure is described in detail. A study is considered to have good quality when all criteria are met and fair quality when one criterion is not met or two criteria are unclear, and there is no known important limitation that could invalidate the results. It is considered to have poor quality when two or more criteria are listed as having high or unclear risk of bias, or when one criterion is not met or two criteria are unclear and there are important limitations that could invalidate the results [26].

    Figure 1.  Flowchart.

    Our search strategy identified 796 potentially eligible articles from MEDLINE/PubMed, Web of Science and Scopus databases. After removal of duplicates and studies with animals, 182 titles and abstracts were screened for potentially relevant articles. Fourteen studies were selected for full-text evaluation. Finally, 4 papers were included in the scoping review [27][30]. Details of the study selection procedure are listed in Figure 1.

    The results of the methodological quality of studies included are shown in Table 1 The risk of bias in all these studies ranged from 23 to 26 points. Only the study by Tsai et al. [27] had a good quality score, and the studies by Huang et al. [29] and Kharbteng et al. [28] had a poor quality score.

    A total of 206 subjects with CKD were assessed in the studies included, and 60.5% were male. The experimental groups included 92 patients aged between 52 and 66 years, and the control group included 86 patients aged between 51 and 61 years. Of the studies included, two were conducted in Taiwan [27],[29] and one was conducted in India [28]. The kidney disease stage of the patients was heterogeneous. One study included patients with CKD who received hemodialysis in two or three three-hour sessions weekly for more than three months [27], two studies [28],[30] included patients without kidney replacement therapy (KRT) who had a clinically stable course for the last month and an estimated glomerular filtration rate (GFR) between 14 and 45 ml/min/1.73m2 [27] and one study included patients with kidney failure undergoing hemodialysis treatment three times per week for at least three months [29].

    Details about applied interventions and obtained results are reported in Table 2. Breathing training programs were applied heterogeneously, that is, isolated or combined; three studies [27],[28],[30] applied isolated breathing training, and one study [29] combined breathing training and leg exercises.

    The components of the usual care in the control groups were also heterogeneous. Tsai et al. [27] assigned patients in the control group to a waiting list and after the post-test measurements were completed, the control group received breathing training for four weeks. Kharbteng et al. [28] did not specify the usual care components. Huang et al. [29] described usual care including routine medications, medical treatment, and guidance regarding diet, daily activity and water restrictions.

    The duration and timing of training also varied among studies. Tsai et al. [27] and Kaneko et al. [30] designed a four-week intervention program with breathing exercises, twice weekly for a total of eight sessions with no specifications of the timing related to treatment. The intervention used by Kharbteng et al. [28] consisted of 5-minute sessions three times a day for 4 weeks. Participants in the study by Huang et al. [29] underwent a 12-week intervention three times per week performed two hours after hemodialysis was initiated.

    Outcome measures

    Quality of life was the main outcome measure, and it was measured with different instruments in the studies analyzed. One study [27] assessed health-related quality of life using the Medical Outcome Studies 36-Short Form Health Survey (SF-36). Another study [28] measured quality of life with the Kidney Disease and Quality of Life questionnaire (KDQOL™-36). Finally, a study [29] used the Chinese version of the World Health Organization quality of life assessment brief to reflect quality of life and general health status.

    There were also other outcomes measured in the studies included in this scoping review such as depression measured with the Beck Depression Inventory-II (BDI-II) and self-reported sleep quality assessed using the Pittsburg Sleep Quality Index (PSQI) [27], as well as heart rate variability and fatigue assessed with the hemodialysis-related fatigue scale [29],[30]. The study by Kaneko et al. [30] also assessed blood pressure, respiratory rate, skin temperature, and skin blood flow.

    The analyzed studies showed significant improvements in quality of life after treatment intervention. Tsai et al. [27] reported that the intervention group had scores significantly higher than the control group for both the role-emotional subscale and the mental component summary of the 36-Item Short-Form Health Survey (SF-36). In their study, Kharbteng et al. [28] found a significant difference in mean scores in the intervention group for the KDQOL™-36 for the subscales effects of kidney disease, SF-12 physical functioning or physical health component, and SF-12 mental functioning or mental health composite. Huang et al. [29] found significant changes in quality of life after the intervention in the experimental group.

    Additionally, the study by Tsai et al. [27] showed a significant decrease in depressive symptoms after treatment but no changes in sleep quality. Huang et al. [29] found significant decreases in fatigue but no significant changes in heart rate variability. The study by Kaneko et al. [30] also showed significant differences in diastolic BP, respiratory rate, skin temperature, HF, and the LF/HF ratio, after applying the breathing intervention. The details of interventions and obtained results are reported in Table 2.

    Table 1.  Characteristics of studies.
    Study (year) Design Pathology treatment status Sample (% male) Sample Age Years ± SD Quality of assessment Downs and Black (risk of bias)
    Huang et al. (2021) [29] RCT KF In hemodialysis
    3 times / week
    At least 3 months
    EG: n = 40 (72.5%)
    CG: n = 43 (65.1%)
    Total: n = 83 (68.67%)
    EG: 53.70 ± 10.04
    CG: 61.19 ± 10.19
    24 (Poor quality)
    Kharbteng et al. (2020) [28] RCT CKD without KRT
    Clinically stable course
    for at least 1 month
    EG: n = 30 (50%)
    CG: n = 30 (70%)
    Total: n = 60 (60%)
    EG: 52.06 ± 6.97
    CG: 51.83 ± 10.27
    23 (Poor quality)
    Tsai et al. (2015) [27] RCT CKD In hemodialysis
    2/3 times / week
    3 hours / season
    At least 3 months
    EG: n = 32 (50%)
    CG: n = 25 (48%)
    Total: n = 57 (49.12%)
    EG: 64.94 ± 9.51
    CG: 61.08 ± 11.18
    26 (Good quality)
    Kaneko et al (2021) [30] Pilot quasi-experimental study CKD without KRT
    in a stable condition
    EG: n = 6 (100%)
    CG: -
    EG: 66.0 ± 9.4
    CG: -
    -

    *Note: RCT – Randomized controlled trial; KF – Kidney failure; CKD – Chronic kidney disease; KRT – Kidney replacement therapy; EG – Experimental group; CG – Control group; SD – Standard deviation.

     | Show Table
    DownLoad: CSV
    Table 2.  Characteristics of interventions.
    Study (year) Timing of intervention Interventions Outcomes Main results
    Huang et al. (2021) [29] During hemodialysis sessions (3 hours)
    12 weeks
    3 times/week
    EG
    Usual care
    Breathing-based low-intensity leg exercise program
    leg lifts + quadriceps femoris contraction +
    knee flexion + five abdominal breaths
    15 min/exercise section

    CG
    Usual care: routine medication, medical treatment and
    guidance (diet + daily activity + water restrictions)
    - QOL
    WHOQOL_BREF
    - Heart rate variability
    Low-frequency power is associated with the clinical response to sympathetic and parasympathetic activity and high-frequency power, which is an index of parasympathetic activity.
    - Fatigue
    The hemodialysis-related fatigue scale.
    ↑ WHOQOL *
    ↓ Fatigue*
    LF X
    HF X
    Kharbteng et al. (2020) [28] At home

    4 weeks
    7 times / week
    3 times / day
    EG
    Breathing training program (alternate nostril
    breathing or anulom-vilom)
    4-7-8 breathing exercises and breath counting
    5 min/session (15 min/day)
    CG
    Usual care
    - QOL
    KDQOL-36
    ↑KDQOL™-36*
    Tsai et al. (2015) [27] NR
    (at the dialysis center)

    4 weeks
    2 times / week
    EG
    Audio device-guided breathing training
    1st session:
    • 10 min individualized breathing coaching
    • Listening to prerecorded instructions on breathing technique
    • 20 min practiced breathing + prerecorded voice guide

    7 following sessions:
    • 30 min listening to prerecorded voice guide and music + practicing breathing

    CG
    Waiting list
    After the posttest measurements were
    completed, patients received four weeks of
    breathing training
    - QOL
     SF-36
    - Depression
     BDI-II
    - Sleep quality
     PSQI
    ↓ BDI-II *
    PSQI X
    ↑Role-emotional subscale and mental component summary of QoL FS-36*
    Kaneko et al (2021) [30] NR

    Around 4 weeks
    2 times / day
    EG
    Six abdominal breaths per minute for 15 minutes
    Subjects repeatedly inhaled for 3 seconds
    through the nose and exhaled for 6 seconds
    through the mouth.
    CG
    No control group
    -Heart rate
    -Blood pressure
    -Respiratory rate
    -Skin temperature
    -Skin blood flow
    -Heart rate variability: LF, HF, ratio of LF and HF power
    HR X
    Systolic BP X
    ↓ Diastolic BP*
    ↓ Respiratory rate*
    ↑Skin temperature*
    Skin blood flow X
    LF X
    ↑ HF*
    ↓ LF/HF ratio*

    *Note: EG – Experimental group; CG – Control group; NR – Not reported; QOL – Quality of life; WHOQOL_BREF – World Health Organization quality of life-brief version; LF – Low-frequency power; HF – High-frequency power; KDQOL-36 – Kidney Disease and Quality of Life questionnaire; SF-36 – Medical Outcome Studies 36-Item Short Form Health Survey; BDI-II – Beck Depression Inventory II; PSQI – Pittsburgh Sleep Quality Index; BP – Blood pressure; *: Statistically significant; ↑: Increment; ↓: Decrement; X: No statistically relevant variations.

     | Show Table
    DownLoad: CSV

    To our knowledge, this is the first scoping review to evaluate the effects of breathing training on patients with CKD treated by dialysis. The small number of included studies and the publication years indicate the novelty and limited research to date. Even with the heterogeneity of the studies included, our findings suggest that breathing training alone or combined with leg exercises has positive effects on quality of life in CKD patients without KRT or hemodialysis treatment.

    Even the analysed studies used different approaches to breathing training program design and choice of technique; all of them included abdominal breathing, a breathing exercise that seemed to have positive effects on quality of life. The four included studies [27][30] followed similar coaching method for teaching their breathing training programs to participants in the experimental group. The experimental group in all the studies [27][30] received a coaching training demonstration by the researchers. In addition, to enhance the intervention performance, one study used pre-recorded instructions to guide each session [27]; in another two studies, the experimental group was guided with a video provided to each participant in the experimental group with the purpose of either standardizing the program and correcting the practice [29] or practicing the exercises at home [28]. This methodology could also have ensured adherence to treatment.

    Even though the duration of the interventions and of the entire protocols were heterogeneous among the studies, the evidence in this scoping review suggests that a breathing training intervention as short as a total of 8 sessions in 4 weeks has positive benefits in some areas of the quality of life in CKD patients treated by hemodialysis [27],[30]. In this regard, other exercise types have demonstrated similar improvement in CKD quality of life and functional status, with greater values of TAC,CAT,GSH and GSH/GSSG after the exercise program [31].

    Given that no special equipment was required, after coaching, respiratory training could be performed from home without taking much time, with good benefits as reported by Kharbteng et al. [28] This matches the findings of Lu et al. [32], which concluded that home-based breathing exercises have beneficial effects on chronic obstructive pulmonary disease.

    The study by Tsai et al. [27] had self-reported depressive symptoms as primary outcome and the health-related quality of life and self-reported sleep quality as secondary outcomes. The latter outcome showed no statistically relevant variations, but the breathing program had positive and statistically relevant changes in the other two outcomes. Similarly, the study by Levendoglu et al. [33] showed a significant reduction of depression levels and the mental component scale of CKD patients after applying a twelve-week exercise program.

    Other studies support our findings with breathing training as a promising intervention to improve health outcomes and quality of life in various pathologies such as heart failure [34] and chronic obstructive pulmonary disease [35].

    The strength of our study is that it is the first to review the effects of breathing training on CKD patients. Additionally, it includes research published about the topic to date.

    This scoping review has several limitations. Our analysis included a small number of studies; nevertheless, previous reviews have been conducted with a similar number of studies [36]. Additionally, the interventions of the studies included were not homogeneous, making it difficult to categorize the results.

    In conclusion, a breathing training intervention for at least 4 weeks, including diaphragmatic breathing exercises, was able to improve the quality of life of patients with CKD during hemodialysis treatment.

    These findings could improve the daily clinical practice of CKD healthcare professionals and the daily physical activity of CKD patients. It is a coaching training protocol that does not require extra equipment and could be used in the future as a non-invasive low-cost intervention for patients with CKD for improving their performance status and quality of life.

    This scoping review was undertaken as a precursor to future systematic reviews that confirm the results shown here. In this regard, we performed a preliminary mapping of published literature that could be taken as a base for clinical practice. In addition, it is necessary to conduct future randomized controlled trials using different breathing training programs in the various CKD stages.



    [1] C. Mouradian, D. Naboulsi, S. Yangui, R. H. Glitho, M. J. Morrow, P. A. Polakos, A comprehensive survey on fog computing: State-of-the-art and research challenges, IEEE Commun. Surv. Tutorials, 20 (2017), 416–464.
    [2] N. Abbas, Y. Zhang, A. Taherkordi, T. Skeie, Mobile edge computing: A survey, IEEE Int. Things, 5 (2017), 450–465.
    [3] W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, A. Ahmed, Edge computing: A survey, Future Gener. Comput. Syst., 97 (2019), 219–235. doi: 10.1016/j.future.2019.02.050
    [4] P. Mach, Z. Becvar, Mobile edge computing: A survey on architecture and computation offloading, IEEE Commun. Surv. Tutorials, 19 (2017), 1628–1656. doi: 10.1109/COMST.2017.2682318
    [5] S. Agarwal, S. Yadav, A. K. Yadav, An efficient architecture and algorithm for resource provisioning in fog computing, Int. J. Inf. Eng. Electron. Bus., 8 (2016), 48.
    [6] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Gener. Comput. Syst., 25 (2009), 599–616. doi: 10.1016/j.future.2008.12.001
    [7] N. R. Mohan, E. B. Raj, Resource allocation techniques in cloud computing-research challenges for applications, in 2012 fourth international conference on computational intelligence and communication networks, IEEE, (2012), 556–560.
    [8] D. Ergu, G. Kou, Y. Peng, Y. Shi, Y. Shi, The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment, J. Supercomput., 64 (2013), 835–848. doi: 10.1007/s11227-011-0625-1
    [9] P. Mell, T. Grance, The NIST definition of cloud computing, 2011.
    [10] F. Shahid, H. Ashraf, A. Ghani, S. A. K. Ghayyur, S. Shamshirband, E. Salwana, PSDS-proficient security over distributed storage: A method for data transmission in cloud, IEEE Access, 8 (2020), 118285–118298. doi: 10.1109/ACCESS.2020.3004433
    [11] A. Shawish, M. Salama, Cloud computing: paradigms and technologies, in Inter-cooperative collective intelligence: Techniques and applications: Springer, Berlin, Heidelberg, (2014), 39–67.
    [12] I. Foster, C. Kesselman, J. M. Nick, S. Tuecke, Grid services for distributed system integration, Computer, 35 (2002), 37–46.
    [13] S. J. Baek, S. M. Park, S. H. Yang, E. H. Song, Y. S. Jeong, Efficient server virtualization using grid service infrastructure, J. Inf. Process. Syst., 6 (2010), 553–562. doi: 10.3745/JIPS.2010.6.4.553
    [14] A. Jula, E. Sundararajan, Z. Othman, Cloud computing service composition: A systematic literature review, Expert Syst. Appl., 41 (2014), 3809–3824. doi: 10.1016/j.eswa.2013.12.017
    [15] H. R. Faragardi, A. Rajabi, R. Shojaee, T. Nolte, Towards energy-aware resource scheduling to maximize reliability in cloud computing systems, in 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, IEEE, (2013), 1469–1479.
    [16] P. Samimi, Y. Teimouri, M. Mukhtar, A combinatorial double auction resource allocation model in cloud computing, Inf. Sci., 357 (2016), 201–216. doi: 10.1016/j.ins.2014.02.008
    [17] L. Ni, J. Zhang, C. Jiang, C. Yan, K. Yu, Resource allocation strategy in fog computing based on priced timed petri nets, IEEE Int. Things, 4 (2017), 1216–1228. doi: 10.1109/JIOT.2017.2709814
    [18] X. Zhao, S. S. Band, S. Elnaffar, M. Sookhak, A. Mosavi, E. Salwana, The implementation of border gateway protocol using software-defined networks: A systematic literature review, IEEE Access, 2021.
    [19] M. Aazam, E. N. Huh, Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT, in 2015 IEEE 29th International Conference on Advanced Information Networking and Applications, IEEE, (2015), 687–694.
    [20] O. Skarlat, S. Schulte, M. Borkowski, P. Leitner, Resource provisioning for IoT services in the fog, in 2016 IEEE 9th international conference on service-oriented computing and applications (SOCA), IEEE, (2016), 32–39.
    [21] F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in Proceedings of the first edition of the MCC workshop on Mobile cloud computing, (2012), 13–16.
    [22] P. G. V. Naranjo, Z. Pooranian, M. Shojafar, M. Conti, R. Buyya, FOCAN: A Fog-supported smart city network architecture for management of applications in the Internet of Everything environments, J Parallel Distr. Comput., 132 (2019), 274–283. doi: 10.1016/j.jpdc.2018.07.003
    [23] B. Varghese, N. Wang, D. S. Nikolopoulos, R. Buyya, Feasibility of fog computing, in Handbook of Integration of Cloud Computing, Cyber Physical Systems and Internet of Things, Springer, (2020), 127–146.
    [24] X. Li, Y. Liu, H. Ji, H. Zhang, V. C. Leung, Optimizing resources allocation for fog computing-based Internet of Things networks, IEEE Access, 7 (2019), 64907–64922. doi: 10.1109/ACCESS.2019.2917557
    [25] I. Stojmenovic, S. Wen, The fog computing paradigm: Scenarios and security issues, in 2014 federated conference on computer science and information systems, IEEE, (2014), 1–8.
    [26] A. Singh, Y. Viniotis, Resource allocation for IoT applications in cloud environments, in 2017 International Conference on Computing, Networking and Communications (ICNC), IEEE, (2017), 719–723.
    [27] M. H. Homaei, E. Salwana, S. Shamshirband, An enhanced distributed data aggregation method in the Internet of Things, Sensors, 19 (2019), 3173. doi: 10.3390/s19143173
    [28] Y. Gu, Z. Chang, M. Pan, L. Song, Z. Han, Joint radio and computational resource allocation in IoT fog computing, IEEE Trans. Veh. Technol., 67 (2018), 7475–7484. doi: 10.1109/TVT.2018.2820838
    [29] S. F. Abedin, M. G. R. Alam, S. A. Kazmi, N. H. Tran, D. Niyato, C. S. Hong, Resource allocation for ultra-reliable and enhanced mobile broadband IoT applications in fog network, IEEE Trans. Commun., 67 (2018), 489–502.
    [30] X. Xu, S. Fu, Q. Cai, W. Tian, W. Liu, W. Dou, et al., Dynamic resource allocation for load balancing in fog environment, Wirel. Commun. Mob. Comput., 2018 (2018).
    [31] W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu, Edge computing: Vision and challenges, IEEE Int. Things, 3 (2016), 637–646. doi: 10.1109/JIOT.2016.2579198
    [32] B. Frankovič, I. Budinská, Advantages and disadvantages of heuristic and multi agents approaches to the solution of scheduling problem, IFAC Proc. Vol., 33 (2000), 367–372.
    [33] M. H. Mohamaddiah, A. Abdullah, S. Subramaniam, M. Hussin, A survey on resource allocation and monitoring in cloud computing, Int. J. Mach. Learn. Comput., 4 (2014), 31–38.
    [34] H. Rafique, M. A. Shah, S. U. Islam, T. Maqsood, S. Khan, C. Maple, A novel bio-inspired hybrid algorithm (NBIHA) for efficient resource management in fog computing, IEEE Access, 7 (2019), 115760–115773. doi: 10.1109/ACCESS.2019.2924958
    [35] Y. Jie, C. Guo, K. K. R. Choo, C. Z. Liu, M. Li, Game-theoretic resource allocation for fog-based industrial internet of things environment, IEEE Int. Things J., 7 (2020), 3041–3052. doi: 10.1109/JIOT.2020.2964590
    [36] R. Gibbons, A primer in game theory, 1992.
    [37] J. Moura, D. Hutchison, Game theory for multi-access edge computing: Survey, use cases, and future trends, IEEE Commun. Surv. Tutorials, 21 (2018), 260–288.
    [38] A. Yousafzai, A. Gani, R. M. Noor, M. Sookhak, H. Talebian, M. Shiraz, et al., Cloud resource allocation schemes: review, taxonomy, and opportunities, Knowl. Inf. Syst., 50 (2017), 347–381. doi: 10.1007/s10115-016-0951-y
    [39] M. Ghobaei-Arani, A. Souri, A. A. Rahmanian, Resource management approaches in fog computing: A comprehensive review, J Grid Comput., 18 (2020), 1–42. doi: 10.1007/s10723-019-09491-1
    [40] A. Hameed, A. Khoshkbarforoushha, R. Ranjan, P. P. Jayaraman, J. Kolodziej, P. Balaji, et al., A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems, Computing, 98 (2016), 751–774. doi: 10.1007/s00607-014-0407-8
    [41] A. Beloglazov, R. Buyya, Y. C. Lee, A. Zomaya, A taxonomy and survey of energy-efficient data centers and cloud computing systems, in Advances in computers, Elsevier, (2011), 47–111.
    [42] J. Shuja, K. Bilal, S. A. Madani, M. Othman, R. Ranjan, P. Balaji, et al., Survey of techniques and architectures for designing energy-efficient data centers, IEEE Syst. J., 10 (2014), 507–519.
    [43] G. Aceto, A. Botta, W. De Donato, A. Pescapè, Cloud monitoring: A survey, Comput. Network, 57 (2013), 2093–2115. doi: 10.1016/j.comnet.2013.04.001
    [44] B. Jennings, R. Stadler, Resource management in clouds: Survey and research challenges, J. Network Syst. Manag., 23 (2015), 567–619. doi: 10.1007/s10922-014-9307-7
    [45] A. Goyal, S. Dadizadeh, A survey on cloud computing, Univ. B. C. Tech. Rep. CS, 508 (2009), 55–58.
    [46] H. Hussain, S. U. R. Malik, A. Hameed, S. U. Khan, G. Bickler, N. Min-Allah, et al., A survey on resource allocation in high performance distributed computing systems, Parallel Comput., 39 (2013), 709–736. doi: 10.1016/j.parco.2013.09.009
    [47] L. Huang, H. S. Chen, T. T. Hu, Survey on resource allocation policy and job scheduling algorithms of cloud computing1, J. Softw., 8 (2013), 480–487.
    [48] R. W. Ahmad, A. Gani, S. H. A. Hamid, M. Shiraz, F. Xia, S. A. Madani, Virtual machine migration in cloud data centers: a review, taxonomy, and open research issues, J. Supercomput., 71 (2015), 2473–2515. doi: 10.1007/s11227-015-1400-5
    [49] R. W. Ahmad, A. Gani, S. H. A. Hamid, M. Shiraz, A. Yousafzai, F. Xia, A survey on virtual machine migration and server consolidation frameworks for cloud data centers, J. Network Comput. Appl., 52 (2015), 11–25. doi: 10.1016/j.jnca.2015.02.002
    [50] V. Vinothina, R. Sridaran, P. Ganapathi, A survey on resource allocation strategies in cloud computing, Int. J. Adv. Comput. Sci. Appl., 3 (2012), 97–104. doi: 10.5121/acij.2012.3511
    [51] V. Anuradha, D. Sumathi, A survey on resource allocation strategies in cloud computing, in International Conference on Information Communication and Embedded Systems (ICICES2014), IEEE, (2014), 1–7.
    [52] E. Castaneda, A. Silva, A. Gameiro, M. Kountouris, An overview on resource allocation techniques for multi-user MIMO systems, IEEE Commun. Surv. Tutorials, 19 (2016), 239–284.
    [53] S. S. Manvi, G. K. Shyam, Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey, J. Network Comput. Appl., 41 (2014), 424–440. doi: 10.1016/j.jnca.2013.10.004
    [54] R. Su, D. Zhang, R. Venkatesan, Z. Gong, C. Li, F. Ding, et al., Resource allocation for network slicing in 5G telecommunication networks: A survey of principles and models, IEEE Network, 33 (2019), 172–179. doi: 10.1109/MNET.2019.1900024
    [55] F. Saeik, M. Avgeris, D. Spatharakis, N. Santi, D. Dechouniotis, J. Violos, et al. S. Papavassiliou, Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions, Comput. Network, 195 (2021), 108177. doi: 10.1016/j.comnet.2021.108177
    [56] L. Song, D. Niyato, Z. Han, E. Hossain, Game-theoretic resource allocation methods for device-to-device communication, IEEE Wirel. Commun., 21 (2014), 136–144.
    [57] H. Zhang, Y. Zhang, Y. Gu, D. Niyato, Z. Han, A hierarchical game framework for resource management in fog computing, IEEE Commun. Mag., 55 (2017), 52–57.
    [58] J. Klaimi, S. M. Senouci, M. A. Messous, Theoretical game approach for mobile users resource management in a vehicular fog computing environment, in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), IEEE, (2018), 452–457.
    [59] H. Zhang, Y. Xiao, S. Bu, D. Niyato, F. R. Yu, Z. Han, Computing resource allocation in three-tier IoT fog networks: A joint optimization approach combining Stackelberg game and matching, IEEE Int. Things, 4 (2017), 1204–1215. doi: 10.1109/JIOT.2017.2688925
    [60] H. Munir, S. A. Hassan, H. Pervaiz, Q. Ni, A game theoretical network-assisted user-centric design for resource allocation in 5G heterogeneous networks, in 2016 IEEE 83rd vehicular technology conference (VTC Spring), IEEE, (2016), 1–5.
    [61] Y. Chen, Z. Li, B. Yang, K. Nai, K. Li, A Stackelberg game approach to multiple resources allocation and pricing in mobile edge computing, Future Gener. Comput. Syst., 108 (2020), 273–287. doi: 10.1016/j.future.2020.02.045
    [62] L. Liang, G. Feng, Y. Jia, Game-theoretic hierarchical resource allocation for heterogeneous relay networks, IEEE Trans. Veh. Technol., 64 (2014), 1480–1492.
    [63] A. Nezarat, G. Dastghaibifard, Efficient nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction, PloS one, 10 (2015), e0138424. doi: 10.1371/journal.pone.0138424
    [64] A. Nezarat, G. Dastghaibifard, A game theoretic method for resource allocation in scientific cloud, Int. J. Cloud Appl. Comput. (IJCAC), 6 (2016), 15–41.
    [65] B. Yang, Z. Li, S. Chen, T. Wang, K. Li, Stackelberg game approach for energy-aware resource allocation in data centers, IEEE Trans. Parallel Distr. Syst., 27 (2016), 3646–3658. doi: 10.1109/TPDS.2016.2537809
    [66] J. Huang, Y. Zhao, K. Sohraby, Resource allocation for intercell device-to-device communication underlaying cellular network: A game-theoretic approach, in 2014 23rd international conference on computer communication and networks (ICCCN), IEEE, (2014), 1–8.
    [67] D. Niyato, E. Hossain, A game-theoretic approach to competitive spectrum sharing in cognitive radio networks, in 2007 IEEE Wireless Communications and Networking Conference, IEEE, (2007), 16–20.
    [68] J. Huang, Y. Yin, Q. Duan, H. Yan, A game-theoretic analysis on context-aware resource allocation for device-to-device communications in cloud-centric internet of things, in 2015 3rd International Conference on Future Internet of Things and Cloud, IEEE, (2015), 80–86.
    [69] W. Wei, X. Fan, H. Song, X. Fan, J. Yang, Imperfect information dynamic stackelberg game based resource allocation using hidden Markov for cloud computing, IEEE Trans. Serv. Comput., 11 (2016), 78–89.
    [70] H. Zhang, J. Du, J. Cheng, K. Long, V. C. Leung, Incomplete CSI based resource optimization in SWIPT enabled heterogeneous networks: A non-cooperative game theoretic approach, IEEE Trans. Wirel. Commun., 17 (2017), 1882–1892.
    [71] J. Zhang, W. Xia, F. Yan, L. Shen, Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing, IEEE Access, 6 (2018), 19324–19337. doi: 10.1109/ACCESS.2018.2819690
    [72] X. Chen, L. Jiao, W. Li, X. Fu, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE ACM Trans. Network, 24 (2015), 2795–2808.
    [73] S. Guo, B. Xiao, Y. Yang, Y. Yang, Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing, in IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE, (2016), 1–9.
    [74] G. S. Li, Y. Zhang, M. L. Wang, J. H. Wu, Q. Y. Lin, X. F. Sheng, Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing, Complexity, 2020 (2020).
    [75] P. S. Pillai, S. Rao, Resource allocation in cloud computing using the uncertainty principle of game theory, IEEE Syst. J., 10 (2016), 637–648. doi: 10.1109/JSYST.2014.2314861
    [76] X. Xu, H. Yu, A game theory approach to fair and efficient resource allocation in cloud computing, Math. Probl. Eng., 2014 (2014).
    [77] Z. Zhou, P. Liu, J. Feng, Y. Zhang, S. Mumtaz, J. Rodriguez, Computation resource allocation and task assignment optimization in vehicular fog computing: A contract-matching approach, IEEE Trans. Veh. Technol., 68 (2019), 3113–3125. doi: 10.1109/TVT.2019.2894851
    [78] K. Wang, Z. Ding, D. K. So, G. K. Karagiannidis, Stackelberg game of energy consumption and latency in MEC systems With NOMA, IEEE Trans. Commun., 69 (2021), 2191–2206. doi: 10.1109/TCOMM.2021.3049356
    [79] S. G. Domanal, R. M. R. Guddeti, R. Buyya, A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment, IEEE Trans. Serv. Comput., 13 (2017), 3–15.
    [80] X. S. Yang, Nature-inspired Optimization Algorithms, Academic Press, 2020.
    [81] A. Arram, M. Ayob, A. Sulaiman, Hybrid bird mating optimizer with single-based algorithms for combinatorial optimization problems, IEEE Access, 9 (2021), 115972–115989. doi: 10.1109/ACCESS.2021.3096125
    [82] N. S. Jaddi, S. Abdullah, A novel auction-based optimization algorithm and its application in rough set feature selection, IEEE Access, 9 (2021), 106501–106514. doi: 10.1109/ACCESS.2021.3098808
    [83] Z. J. Lee, S. F. Su, C. Y. Lee, Y. S. Hung, A heuristic genetic algorithm for solving resource allocation problems, Knowl. Inf. Syst., 5 (2003), 503–511. doi: 10.1007/s10115-003-0082-0
    [84] Z. J. Lee, C. Y. Lee, A hybrid search algorithm with heuristics for resource allocation problem, Inf. Sci., 173 (2005), 155–167. doi: 10.1016/j.ins.2004.07.010
    [85] Y. Liu, J. E. Fieldsend, G. Min, A framework of fog computing: Architecture, challenges, and optimization, IEEE Access, 5 (2017), 25445–25454. doi: 10.1109/ACCESS.2017.2766923
    [86] M. Kim, I. Y. Ko, An efficient resource allocation approach based on a genetic algorithm for composite services in IoT environments, in 2015 IEEE International Conference on Web Services, IEEE, (2015), 543–550.
    [87] L. Chimakurthi, Power efficient resource allocation for clouds using ant colony framework, preprint, arXiv: 1102.2608.
    [88] B. Han, J. Lianghai, H. D. Schotten, Slice as an evolutionary service: Genetic optimization for inter–slice resource management in 5G networks, IEEE Access, 6 (2018), 33137–33147. doi: 10.1109/ACCESS.2018.2846543
    [89] J. Tang, D. K. So, E. Alsusa, K. A. Hamdi, A. Shojaeifard, Resource allocation for energy efficiency optimization in heterogeneous networks, IEEE J. Sel. Area Commun., 33 (2015), 2104–2117. doi: 10.1109/JSAC.2015.2435351
    [90] Y. Liu, S. L. Zhao, X. K. Du, S. Q. Li, Optimization of resource allocation in construction using genetic algorithms, in 2005 International Conference on Machine Learning and Cybernetics, IEEE, 6 (2005), 3428–3432.
    [91] J. Zhang, W. Xia, Z. Cheng, Q. Zou, B. Huang, F. Shen, et al., An evolutionary game for joint wireless and cloud resource allocation in mobile edge computing, in: ; 2017. IEEE. pp. 1–6.
    [92] X. L. Zheng, L. Wang. A Pareto based fruit fly optimization algorithm for task scheduling and resource allocation in cloud computing environment, in 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), IEEE, (2016), 3393–3400.
    [93] R. M. Guddeti, R. Buyya, A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment, IEEE Trans. Serv. Comput., 2017.
    [94] E. Arianyan, D. Maleki, A. Yari, I. Arianyan, Efficient resource allocation in cloud data centers through genetic algorithm, in 6th International Symposium on Telecommunications (IST), IEEE, (2012), 566–570.
    [95] A. Beloglazov, J. Abawajy, R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, Future Gener. Comput. Syst., 28 (2012), 755–768. doi: 10.1016/j.future.2011.04.017
    [96] Z. Cao, J. Lin, C. Wan, Y. Song, Y. Zhang, X. Wang, Optimal cloud computing resource allocation for demand side management in smart grid, IEEE Trans. Smart Grid, 8 (2016), 1943–1955.
    [97] S. H. da Mata, P. R. Guardieiro, A genetic algorithm based approach for resource allocation in LTE uplink, in 2014 International Telecommunications Symposium (ITS), IEEE, (2014), 1–5.
    [98] E. Hachicha, K. Yongsiriwit, M. Sellami, W. Gaaloul, Genetic-based configurable cloud resource allocation in QoS-aware business process development, in 2017 IEEE International Conference on Web Services (ICWS), IEEE, (2017), 836–839.
    [99] K. Ma, A. Bagula, C. Nyirenda, O. Ajayi, An iot-based fog computing model, Sensors, 19 (2019), 2783.
    [100] L. Ngqakaza, A. Bagula, Least path interference beaconing protocol (libp): A frugal routing protocol for the internet-of-things, in International Conference on Wired/Wireless Internet Communications, Springer, (2014), 148–161.
    [101] A. Bagula, D. Djenouri, E. Karbab, Ubiquitous sensor network management: The least interference beaconing model, in 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE, (2013), 2352–2356.
    [102] A. B. Bagula, D. Djenouri, E. Karbab, On the relevance of using interference and service differentiation routing in the internet-of-things, Int. Things, Smart Spaces Next Gener. Networking, Springer, (2013), 25–35.
    [103] R. Kumar, A. Kumar, A. Sharma, A bio-inspired approach for power and performance aware resource allocation in clouds, in MATEC Web of Conferences, EDP Sciences, 57 (2016), 02008.
    [104] J. J. Rao, K. V. Cornelio, An optimized resource allocation approach for data-Intensive workloads using topology-Aware resource allocation, in 2012 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), IEEE, (2012), 1–4.
    [105] G. Lee, N. Tolia, P. Ranganathan, R. H. Katz, Topology-aware resource allocation for data-intensive workloads, in Proceedings of the first ACM asia-pacific workshop on Workshop on systems, (2010), 1–6.
    [106] S. B. Akintoye, A. Bagula, Improving quality-of-service in cloud/fog computing through efficient resource allocation, Sensors, 19 (2019), 1267. doi: 10.3390/s19061267
    [107] C. W. Tsai, SEIRA: An effective algorithm for IoT resource allocation problem, Comput. Commun., 119 (2018), 156–166. doi: 10.1016/j.comcom.2017.10.006
    [108] C. W. Tsai, An effective WSN deployment algorithm via search economics, Comput. Network, 101 (2016), 178–191. doi: 10.1016/j.comnet.2016.01.005
    [109] J. MacQueen, Some methods for classification and analysis of multivariate observations, in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA, 1 (1967), 281–297.
    [110] A. K. Sangaiah, A. A. R. Hosseinabadi, M. B. Shareh, S. Y. Bozorgi Rad, A. Zolfagharian, N. Chilamkurti, IoT resource allocation and optimization based on heuristic algorithm, Sensors, 20 (2020), 539. doi: 10.3390/s20020539
    [111] S. Mirjalili, A. Lewis, The whale optimization algorithm, Adv. Eng. Software, 95 (2016), 51–67. doi: 10.1016/j.advengsoft.2016.01.008
    [112] S. K. Chaharsooghi, A. H. M. Kermani, An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP), Appl. Math. Comput., 200 (2008), 167–177.
    [113] M. Dorigo, Optimization, Learning and Natural Algorithms, PhD Thesis, Politecnico di Milano, 1992.
    [114] Y. Choi, Y. Lim, Optimization approach for resource allocation on cloud computing for iot, Int. J. Distrib. Sens. Networks, 12 (2016), 3479247. doi: 10.1155/2016/3479247
    [115] J. Yan, W. Pu, S. Zhou, H. Liu, M. S. Greco, Optimal resource allocation for asynchronous multiple targets tracking in heterogeneous radar networks, IEEE Trans. Signal Process., 68 (2020), 4055–4068. doi: 10.1109/TSP.2020.3007313
    [116] K. Karthiban, J. S. Raj, An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm, Soft Comput., (2020), 1–10.
    [117] H. Ye, G. Y. Li, B. H. F. Juang, Deep reinforcement learning based resource allocation for V2V communications, IEEE Trans. Veh. Technol., 68 (2019), 3163–3173. doi: 10.1109/TVT.2019.2897134
    [118] F. Hussain, S. A. Hassan, R. Hussain, E. Hossain, Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges, IEEE Commun. Surv. Tutorials, 22 (2020), 1251–1275. doi: 10.1109/COMST.2020.2964534
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