ارائه رویکرد روش‌شناسی مبتنی بر قرارداد سرویس جهت نظارت بر کیفیت خدمت در سیستم‌های ابری

نوع مقاله : مقاله پژوهشی

نویسنده

گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد خدابنده، زنجان، ایران

چکیده

امروزه سطح عظیم اشتراک منابع رایانشی و پویائی بارهای کاری در محیط‌های ابری، ضرورت تدوین رویکردی جامع در جهت نظارت بر کیفیت خدمت را ایجاب می­‌نماید. درحال حاضر فقدان بکارگیری دیدگاهی جامع، متدولوژیک و چندمنظوره درجهت پیوند دیدگاه‌های قبلی، موجب ایجاد فضای آشوب در این حوزه تحقیقاتی و درک ناقص از مسئله گشته است. بنابراین در مقایسه با تحقیقات قبلی، هدف این پژوهش گام برداشتن درجهت جبران نواقص مذکور می­‌باشد و مهم­ترین دستاورد آن ارائه دیدگاه متدولوژیک نظارت بر کیفیت خدمت مبتنی بر قرارداد سطح سرویس در سیستم های ابری می باشد. دیدگاه پیشنهادی برخلاف راه‌­حل های موجود، مستقل از عملگرهای محیطی، اهداف کیفی را به­ صورت یکپارچه و خودکار نظارت می‌­نماید تا موثّر واقع گردد. نتایج  ارزیابی ها مبیّن سودمندی و برتری دیدگاه متدولوژیک پیشنهادی نسبت به دیدگاه‌های موجود و مدیریت چندمنظوره کیفیت خدمت در مراکز داده ابری می‌­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Service Contract-Aware Quality Supervisory Methodology in Cloud Systems

نویسنده [English]

  • Nafiseh Fareghzadeh
Department of Computer Science, Khodabandeh Branch, Islamic Azad University, Zanjan, Iran 
چکیده [English]


 Supervising the service quality and aligning performance objectives in cloud service centers facilitates the effective delivery of the requirements and related operational goals. Nowadays, with huge level of resource sharing and dynamic workloads in data centers, there is a need for comprehensive approaches for service quality supervisory in clouds. Previous research has considered this issue from different aspects and at present, there is a lack of multi-objective and methodologic supervisory approach to connect major previous approaches. Therefore, compared with related work, the purpose of this research is to take steps to compensate the mentioned deficiencies and the most important achievement is a novel service contact aware and quality supervisory methodology in cloud data centers. Proposed methodology, unlike existing solutions, is independent from the management strategy and environmental operators and supervises the goal quality metrics in cloud ecosystem. The empirical results indicate the usefulness and superiority of the proposed methodology in identifying quality bottlenecks and supervising the service quality targets. 

کلیدواژه‌ها [English]

  • Service Quality Supervisory
  • cloud computing
  • methodology
  • Service Level Agreement
  • Data Center 
 
[1]    Ghahramani, M., Zhou, M., & Hon, C.T. (2017).  Toward cloud computing QOS architectrure: analysis of cloud systems and cloud services. IEEE/CAA J. Autom. Sin., 4(1), pp. 5-17.
[2]     Potluri, S., & Rao, K.S. (2017). Quality of service based task scheduling algorithms in cloud computing. Int. J. Electr. Comput. Eng. (IJECE), 7(2), pp. 1088-1095.
[3]    Freitas, A. L. P., &  Freitas Neto, M. M.  (2017). Assessing the service quality in Software-as-a-Service from the customer’s perspective: a methodological approach and case of use, J Production, vol. 27. http://dx.doi.org/10.1590/0103-6513.20170020
[4]    Iranpour, E., & Sharifian, S.  (2018). A distributed load balancing and admission control algorithm based on fuzzy type-2 and game theory for large scale SaaS architecture.   J Future Generation Computer Systems, Vol. 86, pp. 81-98. https://doi.org/10.1016/j.future.2018.03.045
[5]    Krebs, R., Loesch, M. & Kounev, S. (2014). Platform-as-a-Service Architecture for Performance Isolated Multi-Tenant Applications in Cloud Computing (CLOUD). IEEE 7th International Conference on, pp. 914-921.
[6]    Sampaio, A. M.  & Barbosa, J. G. (2016). Chapter Three- Energy-Efficient and SLA-Based Resource Management in Cloud Data Centers, J Advances in Computers, Vol. 100, pp. 103-159.https://doi.org/10.1016/bs.adcom.2015.11.002
[7]    Almeida, J., Almeida, V., Ardagna, D., Cunha, I., Francalanci, C. & Trubian, M. (2010). Joint admission control and resource allocation in virtualized servers. J Parallel and Distributed Computing, Vol. 70, pp. 344-362.
[8]    Gao, Y., Guan, H., Qi, Z., Song, T., Huan, F. & Liu, L. (2014). Service level agreement based energy-efficient resource management in cloud data centers. J Computers & Electrical Engineering, Vol. 40, pp. 1621-1633.
[9]    Patros, P., MacKay, S. A., Kent, K. B. & Dawson, M. (2016). Investigating resource interference and scaling on multi-tenant PaaS clouds. In Proceedings of the 26th Annual International Conference on Computer Science and Software Engineering, pp. 166-177.
[10]  Ramanathan, R. & Latha, B. (2018). Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment. J Cluster Comput, pp. 1–11.https://doi.org/10.1007/s10586-018-2234-8
[11]   Xen NUMA roadmap, 2015.[Online] Available: http://t.cn/RoiaLQP.
[12]   Ren, J., Qi, Y., Dai, Y., Xuan, Y. & Shi, Y. (2017). A lightweight nested-virtualization VMM for hosting high performance computing on cloud. J Systems and Software, Vol. 124, pp. 137-152.https://doi.org/10.1016/j.jss.2016.11.001
[13]  Suresh, S. & Sakthivel, S.(2017). A novel performance constrained power management framework for cloud computing using an adaptive node scaling approach. J Computers& Electrical Engineering, Vol. 60, pp. 30-44.
[14] García,   A., García, I., Blanquer Espert, I. & Hernández García, V. (2014). SLA-driven dynamic cloud resource management. J Future Gener. Comput. Syst, vol.31, pp.1-11.
[15] Calheiros,   R.N., Ranjan, R., Beloglazov, A., DeRose, C.A.& Buyya, R. (2011). CloudSim:a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. J Softw Pract Exp, Vol. 41, pp. 23-50.
[16] Beloglazov, A., & Buyya, R. (2012). Optimal Online Deterministic Algorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers. J Concurrency and Computation: Practice and Experience, Vol. 24, pp. 1397-1420.https://doi.org/10.1002/cpe.1867.
[17]  Madhu , B. R. ,  Manjunatha, A.S. , Chandra , P., & Murthy, C. (2016). A Comparative Study of Algorithms For Efficient Dynamic Consolidation of Virtual Machines In Cloud.  J Applied Engineering Research, Vol. 11, no. 6, pp .4597-4600.
[18]  Sundarraj, B. (2015). A Stochastic Model to Investigate Data Center Performance and QoS in IaaS Cloud Computing Systems. International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, no. 3.
[19] Farahnakian , F. (2015). Using ant colony system to consolidate VMs for green cloud computing, J IEEE Trans. Services Comput, Vol. 8, no. 2, pp. 187–198. https://doi.org/10.1109/TSC.2014.2382555
[20] Wang, Z., Tang, X., & Luo, X. (2011). Policy-Based SLA-Aware Cloud Service Provision Framework. In Proceedings of the Seventh International Conference on Semantics Knowledge and Grid, pp. 114-121.
[21]  Zhang, L., Zhuang, Y., & Zhu, W. (2013). A Balanced Scheduling Algorithm for Virtual Cloud Resources based on Dynamic Power-Aware, Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference.  
[22]  Kanani, B. & Maniyar, B. (2015). Review on Max-min Task Scheduling Algorithm for Cloud Computing. J Emerging Technologies and Innovative Research, Vol. 2, pp. 781-784.
[23] Gaurav, G. et al. (2014). A simulation of priority based earliest deadline first scheduling for cloud computing system. Networks & Soft Computing (ICNSC), First International Conference on. IEEE.https://doi.org/10.1109/CNSC.2014.6906659
[24]  Rimal, B.P., & Maier, M. (2017). Workflow Scheduling in Multi-Tenant Cloud Computing Environments. IEEE Trans Parallel Distrib Syst , Vol. 28, no.1, pp.290-304.https://doi.org/10.1109/TPDS.2016.2556668