Conceptual Service Level Agreement Mechanism to Minimize the SLA Violation with SLA Negotiation Process in Cloud Computing Environment
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Abstract
Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective
Received 16/5/2020
Accepted 31/3/2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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References
Ravindran D. Fog Computing Resource Optimization: A Review on Current Scenarios and Resource Management. Baghdad Sci. J.. 2019;16(2):419-27
Ibrahim AA, Varrette S, Bouvry P. On verifying and assuring the cloud SLA by evaluating the performance of SaaS web services across multi-cloud providers. In2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). 2018 Jun 25 (pp. 69-70). IEEE..
Mubeen S, Asadollah SA, Papadopoulos AV, Ashjaei M, Pei-Breivold H, Behnam M. Management of service level agreements for cloud services in IoT: A systematic mapping study. IEEE Access. 2017 Aug 25;6:30184-207.
Mostafa SA, Gunasekaran SS, Mustapha A, Mohammed MA, Abduallah WM. Modelling an Adjustable Autonomous Multi-agent Internet of Things System for Elderly Smart Home. InInternational Conference on Applied Human Factors and Ergonomics. 2019 Jul 24 (pp. 301-311). Springer, Cham.
De Boer FS, Giachino E, de Gouw S, Hähnle R, Johnsen EB, Laneve C, et al. Analysis of SLA Compliance in the Cloud--An Automated, Model-based Approach. arXiv.org. 2019 Aug 27; EPTCS 302: 1-15.
Casola V, De Benedictis A, Rak M, Villano U. A Security SLA-Driven Moving Target Defense Framework to Secure Cloud Applications. InProceedings of the 5th ACM Workshop on Moving Target Defense. 2018 Jan 15; 48-56.
Alamri O, Abbasi B, Minas JP, Zeephongsekul P. Service level agreements: ready-rate analysis with lump-sum and linear penalty structures. J. Oper. Res. 2018 Jan 2;69(1):142-55.
Abed MM, Younis MF. Developing Load Balancing for IoT-Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms. Baghdad Sci. J. 2019;16(1):130-9.
Mutlag, AA, Khanapi Abd Ghani M, Mohammed MA, Maashi MS, Mohd O, Mostafa SA, Abdulkareem KH, et al. MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management. Sensors. 2020; 20(1853): 1-19.
El-Matary DM, El-Attar NE, Awad WA, Hanafy IM. Automated Negotiation Framework Based on Intelligent Agents for Cloud Computing. In2019 International Conference on Innovative Trends in Computer Engineering (ITCE). 2019 Feb 19; 156-161, IEEE.
Kuo CW, Huang KL, Yang CL. Optimal contract design for cloud computing service with resource service guarantee. J. Oper. Res. 2017 Sep 1;68(9):1030-44.
Sun L, He J, Wang C, Dong H, Ma J, Zhang Y. Survey of Cloud SLA Assurance in Pre-interaction and Post-interaction Start Time Phases. J. Comput. 2019;30(1):23-30.
Kumar A, Bawa S. A comparative review of meta-heuristic approaches to optimize the SLA violation costs for dynamic execution of cloud services. Soft Comput. 2020 Mar;24(6):3909-22.
Awad WA, EL-Attar NE. Adaptive SLA mechanism based on fuzzy system for dynamic cloud environment. Int. J. Comput. Appl. 2019 Nov; 7:1-11.
Labidi T, Mtibaa A, Gaaloul W, Gargouri F. Toward Context-Aware SLA for Cloud Computing. InInternational Conference on Hybrid Intelligent Systems, Springer, Cham. 2016 Nov 21; 350-359..
Papadakis-Vlachopapadopoulos K, González RS, Dimolitsas I, Dechouniotis D, Ferrer AJ, Papavassiliou S. Collaborative SLA and reputation-based trust management in cloud federations. Future Gener. Comp. Sy. 2019 Nov 1;100:498-512.
Paputungan IV, Hani AF, Hassan MF, Asirvadam VS. Real-Time and Proactive SLA Renegotiation for a Cloud-Based System. IEEE Syst. J. 2018 Mar 26;13(1):400-11.
Rajavel R, Iyer K, Maheswar R, Jayarajan P, Udaiyakumar R. Adaptive neuro-fuzzy behavioral learning strategy for effective decision making in the fuzzy-based cloud service negotiation framework. J. Intell. Fuzzy Syst. 2019 Jan 1;36(3):2311-22.
Shojaiemehr B, Rahmani AM, Qader NN. A three-phase process for SLA negotiation of composite cloud services. Comput. Stand. Interfaces. 2019 May 1;64:85-95.
Son S, Jung G, Jun SC. An SLA-based cloud computing that facilitates resource allocation in the distributed data centers of a cloud provider. J. Supercomput.. 2013 May 1;64(2):606-37.
Swagatika S, Rath AK. SLA-aware task allocation with resource optimisation on cloud environment. Int. J. Commun. Netw. Distrib. Syst. 2019;22(2):150-69.
Zhou S, Wu L, Jin C. A privacy-based SLA violation detection model for the security of cloud computing. China Commun. 2017 Oct 16;14(9):155-65.
Mutlag AA, Abd Ghani MK, Arunkumar NA, Mohammed MA, Mohd O. Enabling technologies for fog computing in healthcare IoT systems. F. G. C. S. 2019 Jan 1;90:62-78.
Abdulkareem KH, Mohammed MA, Gunasekaran SS, Al-Mhiqani MN, Mutlag AA, Mostafa SA,et al. A review of Fog computing and machine learning: Concepts, applications, challenges, and open issues. IEEE Access. 2019 Oct 15;7:153123-40.