پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شمارهها | 438 |
| تعداد مقالات | 5,650 |
| تعداد مشاهده مقاله | 7,681,887 |
| تعداد دریافت فایل اصل مقاله | 6,339,072 |
Energy and Security Awareness Task Scheduling based on Fuzzy System in Cloud Computing | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 12، دوره 52، شماره 1، شهریور 2020، صفحه 129-142 اصل مقاله (2.43 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2020.17354.5180 | ||
| نویسندگان | ||
| najme mansouri* 1؛ Behnam Mohammad Hasani Zade2؛ mohammad masoud javidi3 | ||
| 1Shahid Bahonar University of Kerman | ||
| 2Computer Science Department, Shahid Bahonar University of Kerman | ||
| 3Department of Computer Science ,Shahid Bahonar University,Kerman,Iran | ||
| چکیده | ||
| The increasing popularity of cloud computing environments makes task scheduling as a critical problem and a hot research topic. It is necessary to decrease the energy related costs and enhance the lifespan of high performance computing resources used in cloud data centers. Moreover, the high quality of security service is increasingly critical for security-sensitive applications that work with large-scale data files such as bioinformatics. We propose a new task scheduling algorithm that includes: 1) analyzing task execution time based on the load of data centers; 2) modeling the resource utilization; 3) calculating security cost based on the failure probabilities; 4) evaluating power consumption based on the linear model; and 5) analyzing the closeness centrality of data centers to improve data retrieval time. Finally, it designs a fuzzy inference system with five inputs (i.e., total execution cost, resource utilization cost, security cost, energy consumption, and centrality) in order to assign a merit value to each data center for task execution. Cloud is a dynamic environment and there is not accurate information at every moment. Therefore, fuzzy inference is a good choice for predicting the behavior of the system and scheduling decisions. The simulation results indicate that the proposed algorithm obtains superior performances respectively in waiting time, success rate, energy consumption, and degree of imbalance around 14%, 12%, 15%, 11% on average than other similar methods under high load condition. Consequently, the proposed strategy has potentials to enhance the performance of QoS delivery since it can effectively utilize cloud resources. | ||
| کلیدواژهها | ||
| Cloud computing؛ Task scheduling؛ Security؛ Energy consumption؛ Fuzzy system, Simulation | ||
| مراجع | ||
|
[1] B. Shojaiemehr, A.M. Rahmani, N. Nasih Qader, Cloud computing service negotiation: A systematic review, Computer Standards & Interfaces, vol. 55, pp. 196-206, 2018.
[2] N. Mansouri, M. M. Javidi, B. Mohammad Hasani Zade, Using data mining techniques to improve replica management in cloud environment, Soft Computing, pp. 1-26, 2019.
[3] N. Mansouri, M.M. Javidi, A new prefetching-aware data replication to decrease access latency in cloud environment, Journal of Systems and Software, vol. 144, pp. 197-215, 2018.
[4] B.R. Nadh Singh, B.Raja Srinivasa Reddy, A review on big data mining in cloud computing, Innovations in Computer Science and Engineering, pp. 131-142, 2017.
[5] A. Ragmani, A. El Omri, N.Abghour, K. Moussaid, M. Rida, An intelligent scheduling algorithm for energy efficiency in cloud environment based on artificial bee colony, In: 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech), 2017.
[6] A.R. Arunarani, D. Manjula, V. Sugumaran, Task scheduling techniques in cloud computing: A literature survey, Future Generation Computer Systems, vol. 91, pp. 407-415, 2019.
[7] L. Yuanjun, L. Sisi, T. Diyin, Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment, Robotics and Computer-Integrated Manufacturing, vol. 61, 2020.
[8] N. Mansouri, G.H. Dastghaibyfard, A. Horri, A novel job scheduling algorithm for improving data grid's performance, In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, (2011).
[9] S. Srichandana, T. Ashok Kumar, S. Bibhudatta, Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm, Future Computing and Informatics Journal, vol. 3, pp. 210-230, 2018.
[10] N. Mansouri, Network and data location aware approach for simultaneous job scheduling and data replication in large-scale data grid environments, Frontiers of Computer Science, vol. 8 (3), pp. 391-408, 2014.
[11] M. Roshni Thanka, P. Uma Maheswari, E. Bijolin Edwin, An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment, Cluster Computing, vol. 22, pp.10905–10913, 2019.
[12] F. Abazari, M. Analoui, H. Takabi, S. Fu, MOWS: Multi-objective workflow scheduling in cloud computing based on heuristic algorithm, Simulation Modelling Practice and Theory, vol. 93, pp. 119-132, 2018.
[13] N. Mansouri, M.M. Javidi, A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers, The Journal of Supercomputing, vol.74 (10), pp. 5349-5372, 2018.
[14] A. Wilczyński, J. Kołodziej, Modelling and simulation of security-aware task scheduling in cloud computing based on Blockchain technology, Simulation Modelling Practice and Theory, vol. 99, 2020.
[15] N. Mansouri, M. M. Javidi, Cost-based job scheduling strategy in cloud computing environments, Distributed and Parallel Databases, 2019.
[16] D. Fernández-Cerero, A. Jakóbik, D. Grzonka, J. Kołodziej, A. Fernández-Montes, Security supportive energy-aware scheduling and energy policies for cloud environments, Journal of Parallel and Distributed Computing, vol. 119, pp. 191-202, 2018.
[17] H. Yoshikazu Shishido, J. Cezar Estrella, C.F. Motta Toledo, M. Silva Arantes, Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds, Computers and Electrical Engineering, vol. 69, pp. 378-394, 2018.
[18] Z. Li, J. Ge, H. Yang, L. Huang, H. Hu, H. Hu, B. Luo, A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds, Future Generation Computer Systems, vol. 65, pp. 140-52, 2016.
[19] L. Ismail, H. Materwala, EATSVM: Energy-aware task scheduling on cloud virtual machines, Procedia Computer Science, vol. 135, pp. 248-258, 2018.
[20] Y.C. Lee and A. Y. Zomaya, Energy efficient utilization of resources in cloud computing systems, Journal of Supercomputing, vol. 60, pp. 268-280, 2012.
[21] H. Zhang, Research on job security scheduling strategy in cloud computing model, International Conference on Intelligent Transportation, Big Data & Smart City, pp. 649-652, 2015.
[22] X. Liu, Y. Zhou, A self-adaptive layered sleep-based method for security dynamic scheduling in cloud storage, 4th International Conference on Information Science and Control Engineering, pp. 99-103, 2017.
[23] Y. Lou, T. Zhang, J. Yan, K. Li, Y. Jiang, H. Wang, J. Cheng, Dynamic scheduling strategy for testing task in cloud computing, Sixth International Conference on Computational Intelligence and Communication Networks, pp. 633-636, 2014.
[24] N. Mansouri, M. M. Javidi, Cost-based job scheduling strategy in cloud computing environments, Distributed and Parallel Databases, pp. 1-36, 2019.
[25] R. Achar, P. Santhi Thilagam, D. Shwetha, H. Pooja, R. Andrea, Optimal scheduling of computational task in cloud using virtual machine tree, Third International Conference on Emerging Applications of Information Technology (EAIT), pp. 143-146, 2012.
[26] J. Kołodziej, F. Xhafa, Meeting security and user behavior requirements in grid scheduling, Simulation Modelling Practice and Theory, vol. 19, pp. 213-226, 2011.
[27] A. Beloglazov, J. Abawajy, R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, Future Generation Computer Systems, vol. 28, pp. 755-768, 2012.
[28] M. Ali, K. Bilal, S.U. Khan, B. Veeravalli, K. Li, A.Y. Zomaya, DROPS: division and replication of data in cloud for optimal performance and security, IEEE Transactions on Cloud Computing, vol. 6, pp. 305-315, 2018.
[29] N. Mansouri, M.M. Javidi, A hybrid data replication strategy with fuzzy-based deletion for heterogeneous cloud data centers, Journal of Supercomputing, vol. 74 (10), pp. 5349–5372, 2018.
[30] R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F. De Rose, R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software – Practice and Experience, vol. 41, pp. 23-50, 2011.
[31] N. Mansouri, Adaptive data replication strategy in cloud computing for performance improvement, Frontiers of Computer Science, vol. 10 (5), pp. 925-935, 2016.
[31] N. Mansouri, B. Mohammad Hasani Zade, M.M. Javidi, Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory, Computers & Industrial Engineering, vol. 130, pp. 597-633, 2019. | ||
|
آمار تعداد مشاهده مقاله: 567 تعداد دریافت فایل اصل مقاله: 387 |
||