EXAMIG MATRIX: PROCESS MIGRATION BASED ON MATRIX DEFINITION OF SELECTING DESTINATION IN DISTRIBUTED EXASCALE ENVIRONMENTS
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Volume 1 (1), July 2018, Pages 20-41
Ehsan Mousavi Khaneghah1, Amirhosein Reyhani ShowkatAbad1, Nosratollah Shadnoush2, Nigar Ismayilova3, Reyhaneh Noorabad Ghahroodi1, Elviz Ismayilov4, Mohammad Saeed Nabati Saravani1, Fatemeh Taheri Sarraf1, Ali Soveizi1
1 Department of Computer Engineering, Faculty of Engineering, Shahed University, Tehran, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.
2 Department of Management, Central Branch, Islamic Azad University, Tehran, Iran
3 High Performance Computing Research Advance Center, Department of General and Applied Mathematics, Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.
4 Department of General and Applied Mathematics, Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
In traditional computing system, load balancer, interim selecting the process, determine the destination computing node based on describing Indicators process status. In distributed Exascale computing system, due to the possibility of occurrence of a dynamic and interactive nature in execution time, it is possible. That the chosen destination computing node affected with dynamic and interactive nature so cannot be considered as a destination in process migration. This paper, by changing management approach in process migration. Consider process as an abstract element on the target computing node and calculates the impact of the factors the parameters affecting the process.
Considering the above factors make process migration manager able to create sets of computational node that can be considered as destination computing node.
In the event of a dynamic and interactive nature, in each element of the set, the process migration management, consider the effects of the factors affecting the activity of the process management and then re-weighs the computing element which make the above set. Using this mechanism allow the process migration management in case of dynamic and interactive nature occurrence in destination able to decide about changing on global activity execution so it is not necessary to recall load balancer manager in order to choose destination computing node. These subject louds to decrease execution time of process migration activity in distributed Exascale computing system.
Keywords:
Process Migration, Distributed Exascale Computing Systems, ExaMig Matrix Mechanism, Dynamic and interactive Events
DOI: https://doi.org/10.32010/26166127.2018.1.1.20.41
References
[1]. Ahmed, Khalid, et al. “Resource manager for managing the sharing of resources among multiple workloads in a distributed computing environment.” U.S. Patent No. 9,632,827. 25 Apr. 2017
[2]. Shah, Syed Asif Raza, Amol Hindurao Jaikar, and Seo-Young Noh. “A performance analysis of precopy, postcopy and hybrid live VM migration algorithms in scientific cloud computing environment.” High Performance Computing & Simulation (HPCS), 2015 International Conference on. IEEE, 2015.
[3]. Garcia, G., Octavio, J., Nafarrate A.R. Collaborative agents for distributed load management in cloud data centers using live migration of virtual machines. IEEE transactions on services computing 8(6), 916-929.
[4]. Chen C. (2015) Energy-efficient fault-tolerant data storage and processing in mobile cloud. IEEE Transactions on cloud computing 3(1), 28-41.
[5]. Mousavi Khaneghah, E., Reyhaneh, N.G., Amirhosein, R.S. (2018) Amathematical multi-dimensional mechanism to improve process migration efficiency in peer-to-peer computing environments. Cogent Engineering, 5(1), 1458434.
[6]. Khaneghah, E.M., Amirhosein R.S., Reyhaneh N.G. (2018) Challenges of Process Migration to Support Distributed Exascale Computing Environment. Proceedings of the 7th International Conference on Software and Computer Applications.
[7]. Khaneghah, E.M. (2017) PMamut: runtime flexible resource management framework in scalable distributed system based on nature of request, demand and supply and federalism. U.S. Patent No. 9,613,312.
[8]. Khaneghah, E.M., Mohsen S. (2014) AMRC: an algebraic model for reconfiguration of high performance cluster computing systems at runtime. The Journal of Supercomputing, 67(1): 1-30.
[9]. Sharifi, M., Seyedeh, L.M., Khaneghah, E.M. (2010) A dynamic framework for integrated management of all types of resources in P2P systems. The Journal of Supercomputing, 52(2), 149-170.
[10]. Jiang, Y. (2016) A survey of task allocation and load balancing in distributed systems.” IEEE Transactions on Parallel and Distributed Systems, 27(2), 585-599.
[11].Thakor, D., Bankim, P. (2018) Performance Measurement and Evaluation of Pluggable to Scheduler Dynamic Load Balancing Algorithm (P2S_DLB) in Distributed Computing Environment. Advanced Computational and Communication Paradigms, 319-329.
[12].Noshy, M., Abdelhameed, I., Hesham, A. A. (2018) Optimization of live virtual machine migration in cloud computing: A survey and future directions. Journal of Network and Computer Applications.
[13].Lim, D.J., Timothy, R.A., Shott, T. (2015) Technological forecasting of supercomputer development: The March to Exascale computing. Omega 51: 128-135.
[14].Pickartz, S. (2016) “Application migration in HPC—A driver of the exascale era? Proceedings of the International Conference on High Performance Computing & Simulation.
[15].Healy, P. (2016) Single system image: A survey. Journal of Parallel and Distributed Computing 90, 35-51.
[16].Patil, S.S., Arpita N.G. (2017) Dynamic Load Balancing Using Periodically Load Collection with Past Experience Policy on Linux Cluster System. Am. J. Math. Comput. Model 2(2), 60-75.
[17].Khaneghah, E.M. (2011) An efficient live process migration approach for high performance cluster computing systems. Innovative Computing Technology. 362-373.
[18].Varadarajan, S., Ruscio, J. (28 November 2017) Transparent check pointing and process migration in a distributed system. U.S. Patent No. 9,830,095.
[19].Cabello, U. (2014) Fault tolerance in heterogeneous multi-cluster systems through a task migration mechanism. Proceedings of the 11th International Conference on Electrical Engineering, Computing Science and Automatic Control.
[20].Holmbacka, S. (2014) A task migration mechanism for distributed many-core operating systems. The Journal of Supercomputing 68(3), 1141-1162.
[21].Tai, J. (2014) Load balancing for cluster systems under heavy-tailed and temporal dependent workloads. Simulation Modelling Practice and Theory, 44, 63-77.
[22].Qureshi, M.B. (2014) Survey on grid resource allocation mechanisms. Journal of Grid Computing, 12(2), 399-441.
[23].Pooranian, Z. (2015) An efficient meta-heuristic algorithm for grid computing. Journal of Combinatorial Optimization, 30(3), 413-434.
[24].Siar, H., Kourosh, K., Chronopoulos, A.T. (2015) An effective game theoretic static load balancing applied to distributed computing. Cluster Computing, 18(4), 1609-1623.
[25].Liu, Y. (2017) DeMS: A hybrid scheme of task scheduling and load balancing in computing clusters. Journal of Network and Computer Applications 83 (2017): 213- 220.
[26].Kumar, A., Vishnu, V., Krishnakumar, A., Kumar, N. (2018) Efficient performance upsurge in live migration with downturn in the migration time and downtime. Cluster Computing, 1(11).
[27].Dam, S. (2018) An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing. Applied Computational Intelligence and Soft Computing in Engineering. 204-232.
[28].Navimipour, N.J., Milani, F.S. (2015) A comprehensive study of the resource discovery techniques in peer-to-peer networks. Peer-to-Peer Networking and Applications, 8(3), 474-492.
[29].Yevmenkin, Maksim, et al. “Load-balancing cluster.” U.S. Patent No. 8,886,814. 11 Nov. 2014.
[30].Ahmed, T., Singh, Y. (2012) Analytic study of load balancing techniques using tool cloud analyst. International Journal of Engineering Research and Applications, 2(2), 1027-1030.
[31].Kapoor, S., Chetna D. (2015) Cluster based load balancing in cloud computing. Proceedings of the Eighth International Conference on Contemporary Computing (IC3).
[32].Singh, A., Dimple J., Malhotra, M. (2015) Autonomous agent based load balancing algorithm in cloud computing. Procedia Computer Science, 45, 832-841.
[33]. Jena, S.R., Ahmad Z. (2013) Response time minimization of different load balancing algorithms in cloud computing environment. International Journal of Computer Applications 69 (17).
[34].Chien, N.K., Nguyen H.S., Ho D. L. Load balancing algorithm based on estimating finish time of services in cloud computing. 18th International Conference on Advanced Communication Technology (ICACT).
[35].Werstein, P., Hailing, S., Huang, Z. (2006) Load balancing in a cluster computer. Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies.
[36].Suri, P.K., Singh, M. (2010) An efficient decentralized load balancing algorithm for grid. Proceedings of the 2nd International Advance Computing Conference.
[37].Zarrin, J., Rui, L.A, Barraca, J.P. (2018) Resource discovery for distributed computing systems: A comprehensive survey. Journal of Parallel and Distributed Computing, 113, 127-166.
[38].Krauter, K., Buyya, R., Maheswaran, M. (2002) A taxonomy and survey of grid resource management systems for distributed computing. Software: Practice and Experience 32(2), 135-164.
[39].Kovvur, R.M.R. (2010) Adaptive resource discovery models and resource selection in grids. Proceedings of the 1st international conference on parallel distributed and grid computing.
[40].Iamnitchi, A., Foster, I. (2001) On fully decentralized resource discovery in grid environments.” International Workshop on Grid Computing. Berlin: Springer.
[41].Iamnitchi, A., Foster, I. (2004) A peer-to-peer approach to resource location in grid environments. Grid resource management. 413-429.
[42].Torkestani, J.A. (2012) A distributed resource discovery algorithm for P2P grids. Journal of Network and Computer Applications, 35(6), 2028-2036.
[43].Asghari, S., Navimipour, N.J. (2018) Resource discovery in the peer to peer networks using an inverted ant colony optimization algorithm. Peer-to-Peer Networking and Applications, 1-14.
[44].Ghebleh, R., Ghaffari, A. (2017) A Multi-criteria Method for Resource Discovery in Distributed Systems Using Deductive Fuzzy System. International Journal of Fuzzy Systems, 19(6), 1829-1839.
[45].Govindarajan, K., Kumar, V.S., Somasundaram, T.S. (2017) A distributed cloud resource management framework for High-Performance Computing (HPC) applications. Proceedings of the Eighth International Conference on Advanced Computing.
[46]. Sandhya, S., Revathi, S., Cauvery, N.K. (2016) Performance Analysis and Comparative Study of Process Migration Using Genetic Algorithm. International Journal of Science, Engineering and Technology Research, 5(11), 3179-3183.
[47].Duolikun, D. (2015) Energy-aware Migration and Replication of Processes in a Cluster. Proceedings of the 10th International Conference on Broadband and Wireless Computing, Communication and Applications.
[48].Patel, M., Chaudhary, P., Garg, S. (2018) Improved pre-copy algorithm using statistical prediction and compression model for efficient live memory migration. International Journal of High Performance Computing and Networking, 11(1), 55-65.
[49].Bloch, T., Sridaran, R., Prashanth, C. S. R. (2018) Understanding Live Migration Techniques Intended for Resource Interference Minimization in Virtualized Cloud Environment. Big Data Analytics. 487-497.
[50].Zhang, F. A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues. IEEE Communications Surveys & Tutorials 20(2), 1206-1243.
[51].Goga, K. (2018) Performance of WRF Cloud Resolving Simulations with Data Assimilation on Public Cloud and HPC Environments. Conference on Complex, Intelligent, and Software Intensive Systems.
[52].Kartsios, S. (2017) The Role of Heat Extinction Depth Concept to Fire Behavior: An Application to WRF-SFIRE Model. Perspectives on Atmospheric Sciences. 137-142.
[53].Mirtaheri, S.L. (2013) Four-dimensional model for describing the status of peers in peer-to-peer distributed systems. Turkish Journal of Electrical Engineering & Computer Sciences, 21(6), 1646-1664.
