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Jamali, Shahram
- A Survey on Data Replication Methods in Content Delivery Networks
Abstract Views :164 |
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Authors
Affiliations
1 Department of Computer Engineering and Information Technology, University of Mohaghegh Ardabili, Ardabil, IR
2 University of Mohaghegh Ardabili, Ardabil, IR
1 Department of Computer Engineering and Information Technology, University of Mohaghegh Ardabili, Ardabil, IR
2 University of Mohaghegh Ardabili, Ardabil, IR
Source
Networking and Communication Engineering, Vol 8, No 1 (2016), Pagination: 26-30Abstract
The Content Delivery Networks (CDN) host the rich and bandwidth consuming content of an organization and process the requests of clients and deliver the content for them. With the extension of these networks and also distributive nature of the resources on them, it is necessary to ensure the desired quality of service for users of these networks. One of the important requirements of maintaining quality of access over the content delivery networks is to utilize an efficient data replication algorithm. This brief review, first explains the necessity of using data replication over the CDN networks and then discusses the challenges exist in this process. The paper then describes the popular replication methods in Content Delivery Networks. The heuristic method as an effective algorithm for data replication is also presented.Keywords
Content Delivery Network, Data Replication, Heuristic Algorithm.
- Improved Grouping Genetic Algorithm for Dynamic Server Consolidation in Virtualized Data Centers
Abstract Views :158 |
PDF Views:3
Authors
Affiliations
1 University of Ardabil, Ardabil, IR
1 University of Ardabil, Ardabil, IR
Source
Networking and Communication Engineering, Vol 7, No 2 (2015), Pagination: 79-84Abstract
The advantages of virtualization have made it a key technology in service management of data centers, but the problem of server consolidation which aims to minimize the number of used physical machines, is still a challenging issue. Server consolidation involves virtual machines (VMs) migration which has a major effect on service performance. Existing consolidation algorithms generate unnecessary migrations while minimizing the number of physical servers hosting these VMs. In this paper, we used our previously proposed improved grouping genetic algorithm with a special technique to avoid unnecessary migration for VMs with steady workload. The testing scenarios show encouraging results regarding overall performance with minimum penalty on the number of used servers.
Keywords
Data Centers Management, Grouping Genetic Algorithm, Server Consolidation, Virtualization.- A Novel Method to Improve the Side Channel Cube Attack on Light-Weight Block Ciphers
Abstract Views :179 |
PDF Views:3
Authors
Affiliations
1 Department of Engineering, University of Mohaghegh, IR
2 Mohaghegh, Ardabili, IR
3 Research Center for Development of Advanced Technology, IR
1 Department of Engineering, University of Mohaghegh, IR
2 Mohaghegh, Ardabili, IR
3 Research Center for Development of Advanced Technology, IR
Source
Networking and Communication Engineering, Vol 6, No 9 (2014), Pagination: 355-362Abstract
Side Channel Cube Attack (SCCA) is a kind of Algebraic Side Channel Attacks (ASCA). In recent years, this kind of attack is implemented on different light weight block ciphers, and it is known as the most powerful attack on these kinds of algorithms. In this paper, we investigate the SCCA on a light-weight block cipher, namely, PRESENT. In addition, we propose novel methods to reduce the data complexity of the SCCA on light-weight block ciphers. Compared to the previous SCCAs, Our proposed SCCA can recover 80 key bits of PRESENT-80 only with 26.13 chosen plaintexts. To the best of our knowledge, this is the most efficient SCCA on PRESENT-80.Keywords
Cube Attack, Light-Weight Block Cipher, Side Channel Cube Attack.- A New Approach to Improve the Performance of Dynamic Distributed Networks
Abstract Views :174 |
PDF Views:2
Authors
Affiliations
1 Autonomic Networking Group, Department of Engineering, University of Mohaghegh, Ardabili, IR
2 Department of Computer Engineering, Mohaghegh Ardabili University, IR
1 Autonomic Networking Group, Department of Engineering, University of Mohaghegh, Ardabili, IR
2 Department of Computer Engineering, Mohaghegh Ardabili University, IR
Source
Networking and Communication Engineering, Vol 6, No 7 (2014), Pagination: 301-306Abstract
Efficient placement of VMs in PMs (Physical Machines) in cloud environment improves resources utilization and energy consumption. One of the most important objectives of the VM placement algorithm is determine the optimal location of virtual machines in physical servers So that the minimum number of physical servers to be turned on for enhancing the overall performance of the cloud environment. In this paper, we employ VIKOR method to design a integrated VM placement algorithm, called VIKOR VM Placement (VVMP) which can reduce the number of running PMs and lower the energy consumption. Extensive simulation results in CloudSim environment show that the proposed algorithm outperforms existing algorithms in terms of migration, traffic cost, SLA and energy.Keywords
Energy Consumption, VIKOR, Migration, PM.- A Survey on Load Balancing Methods in Software Defined Network
Abstract Views :187 |
PDF Views:4
Authors
Affiliations
1 Computer Engineering Department, University of Mohaghegh, Ardabili, IR
2 Computer Engineering Department, Azad University, Ardabil, IR
1 Computer Engineering Department, University of Mohaghegh, Ardabili, IR
2 Computer Engineering Department, Azad University, Ardabil, IR
Source
Networking and Communication Engineering, Vol 10, No 1 (2018), Pagination: 1-7Abstract
Software-Defined Networking (SDN) is a novel network architecture which has received much attention in recent years. SDN is an architecture designed for improving and simplifying network management with high flexibility by separating control plane and data plane. Thus, programmability of the network is enhanced which in turn leads to more innovation opportunities. The separating of concerns introduced between the definition of network policies, their implementation in switching hardware, and forwarding of traffic are the main keys to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying of the network management and facilitating network evolution. Since Load Balancing service is essential to service for distributing workload across server farms or data centers, this paper has summarized and categorized Load Balancing methods in SDN. Many load balancing strategies based on SDN have been suggested to improve the performance of the networks. So, despite expressing of proposed methods, this paper also analyzes their advantages and disadvantage and will promote the SDN development and provide a guidance and comprehensive study for other researchers in this field who want to propose a new method of Load Balancing.Keywords
Software Defined Networking, Load Balancing, Open Flow, QoS, Traffic Engineering.References
- Nunes, Bruno Astuto A., et al. "A survey of software-defined networking: Past, present, and future of programmable networks." IEEE Communications Surveys & Tutorials 16.3 (2014): 1617-1634.
- Kreutz, Diego, et al. "Software-defined networking: A comprehensive survey." Proceedings of the IEEE 103.1 (2015): 14-76.
- McKeown, Nick, et al. "OpenFlow: enabling innovation in campus networks." ACM SIGCOMM Computer Communication Review 38.2 (2008): 69-74.
- ONF, “Open networking foundation,” 2014. [Online]. Available: https://www.opennetworking.org/
- Open Networking Foundation, “OpenFlow Switch Specification - Version 1.5.1 (Protocol version 0x06)”, ONF TS-025, March 2015.
- OpenFlow Switch Consortium, OpenFlow Spec, Available: https://www.opennetworking.org/images/stories/downloads/sdnresources/onf-specifications/openflow/ openflow-spec-v1.5.0.pdf, 2015.
- Lara, Adrian, Anisha Kolasani, and Byrav Ramamurthy. "Network innovation using openflow: A survey." IEEE communications surveys & tutorials 16.1 (2014): 493-512.
- Curtis, Andrew R., Wonho Kim, and Praveen Yalagandula. "Mahout: Low-overhead datacenter traffic management using end-host-based elephant detection." INFOCOM, 2011 Proceedings IEEE. IEEE, 2011.
- Chen, Wenbo, et al. "Dynamic server cluster load balancing in virtualization environment with openflow." International Journal of Distributed Sensor Networks 11.7 (2015): 531538N.
- Handigol, S. Seetharaman, M. Flajslik, N. Mckeown, R. Johari, “Plug-n-Server:load-balacing Web traffic using OpenFlow,” In ACM Sigcomm Demo,(2009): pp. 268- 270.
- R. Wang, D. Butnariu, J. Rexford, “OpenFlow-based server load balancing gone wild,” Usenix Conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services. USENIX Association, (2011): pp. 12-12.
- C. Hopps, “Analysis of an Equal-Cost Multi-Path Algorithm,” RFC Editor, 2000.
- Mohammad Al-Fares, Sivasankar Radhakrishnan, Barath Raghavan, Nelson Huang, Amin Vahdat, “Hedera:dynamic flow scheduling for data center networks,” in 7th USENIX Symposium on Networked Systems Design and Implementation, 2010.
- M. Alizadeh, T. Edsall, S. Dharmapurikar, et al., “CONGA: distributed congestion-aware load balancing for datacenters,” ACM Conference on SIGCOMM. ACM, (2014): pp. 503-514.
- Li, Yu, and Deng Pan. "OpenFlow based load balancing for Fat-Tree networks with multipath support." Proc. 12th IEEE International Conference on Communications (ICC’13), Budapest, Hungary. 2013.
- F. Carpio, A. Engelmann, A. Jukan, “DiffFlow: Differentiating Short and Long Flows for Load Balancing in Data Center Networks,” 2016.
- R. Gandhi, H. H. Liu, Y. C. Hu, et al., “Duet: cloud scale load balancing with hardware and software,” ACM, 2014.
- J. Li, X.. Chang, Y. Ren, Z. Zhang, G. Wang, “An Effective Path Load Balancing Mechanism Based on SDN,” IEEE, International Conference on Trust, Security and Privacy in Computing and Communications. IEEE, (2014): pp. 527-533.
- M. Yu, J. Rexford, M. J.Freedman, J. Wang, “Scalable Flow-based Networking with DIFANE,” ACM SIGCOMM Computer Communication Review,(2010), vol. 41, pp. 351-362.
- T. Koponen, M. Casado, N. Gude, et al, “ Onix : a distributed control platform for large-scal production networks,” In OSDI , (2010): pp. 1-6.
- Tootoonchian, Amin, and Yashar Ganjali. "Hyperflow: A distributed control plane for openflow." Proceedings of the 2010 internet network management conference on Research on enterprise networking. 2010.
- A. Dixit, H. Fang, S. Mukherjee, T.V. Lakshman, R.R. Kompella, “ElastiCon: an elastic distributed sdn controller,” Proceedings of the tenth ACM/IEEE symposium on Architectures for networking and communications systems.ACM, (2014): pp. 17-28.s
- A. Krishnamurthy, S. P. Chandrabose, A. Gember-Jacobson, “Pratyaastha: an efficient elastic distributed SDN control plane,” The Workshop on Hot Topics in Software Defined NETWORKING. ACM, (2014): pp. 133-138.
- Y. P. Yu , H. Qin, “Research on load balancing strategy of controller in OpenFlow network,” Network Security Technology and Application, 2015, pp. 6-7. Network Security Technology and Application China,(2015): pp. 6-7.
- F. Y. Chen, B. Q. Wang, B. W. Wang, Z. M. Wang, "Progressive auction based switch migration mechanism in software define network", Journal of Computer Applications, vol. 35,(2015): pp. 2118-2123.
- Y. Hu, W. Wang, X. Gong, X. Que, S. Cheng, “BalanceFlow: Controller load balancing for OpenFlow networks”[C]// Cloud Computing and Intelligent Systems. IEEE, (2012), vol. 2, pp. 780-785.
- Lin, P. P., et al. "MSDN: a mechanism for scalable intra-domain control plane in SDN." Journal of Chinese Computer Systems 34.9 (2013): 1969-1974.
- Lin, Pingping, Jun Bi, and Hongyu Hu. "Asic: an architecture for scalable intra-domain control in openflow." Proceedings of the 7th International Conference on Future Internet Technologies. ACM, 2012.
- F. Cimorelli, F. D. Priscoli, A. Pietrabissa, et al., “A distributed load balancing algorithm for the control plane in software defined networking,” Mediterranean Conference on Control and Automation, (2016): pp. 1033-1040.
- S. Hassas Yeganeh, Y. Ganjali, “ Kandoo: a framework for efficient and scalable offloading of control applications,” The Workshop on Hot Topics in Software Defined Networks. ACM,(2012): pp. 19-24.
- Yao, Haipeng, et al. "A multicontroller load balancing approach in software-defined wireless networks." International Journal of Distributed Sensor Networks 11.10 (2015): 454159.
- Basta, Arsany, et al. "Towards a dynamic SDN virtualization layer: Control path migration protocol." Network and Service Management (CNSM), 2015 11th International Conference on. IEEE, 2015.
- R. Sherwood, G. Gibb, K. K. Yap, et al. “Can the production network be the testbed?,” OSDI (2010), vol. 10, pp. 1-6.
- M. Koerner, O. Kao, “Multiple service load-balancing with OpenFlow,” IEEE, International Conference on High PERFORMANCE Switching and Routing. IEEE, (2012): pp. 210-214
- G. Iannaccone, C. Chuah, S. Bhattacharyya, C. Diot, “Feasibility of IP restoration in a tier-1 backbone”, IEEE Network 18 (2004).
- Cao, Zhiruo, Zheng Wang, and Ellen Zegura. "Performance of hashing-based schemes for internet load balancing." INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. Vol. 1. IEEE, 2000.
- Bharti, Sourabh, and Kiran Kumar Pattanaik. "Dynamic distributed flow scheduling with load balancing for data center networks." Procedia Computer Science 19 (2013): 124-130.
- A Survey on Load Balancing Methods in Software Defined Network
Abstract Views :243 |
PDF Views:2
Authors
Affiliations
1 Computer Engineering Department, Azad University, Ardabil, IR
2 Computer Engineering Department, University of Mohaghegh Ardabili, IR
1 Computer Engineering Department, Azad University, Ardabil, IR
2 Computer Engineering Department, University of Mohaghegh Ardabili, IR
Source
Networking and Communication Engineering, Vol 10, No 2 (2018), Pagination: 21-27Abstract
Software-Defined Networking (SDN) is a novel network architecture which has received much attention in recent years. SDN is an architecture designed for improving and simplifying network management with high flexibility by separating control plane and data plane. Thus, programmability of the network is enhanced which in turn leads to more innovation opportunities. The separating of concerns introduced between the definition of network policies, their implementation in switching hardware, and forwarding of traffic are the main keys to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying of the network management and facilitating network evolution. Since Load Balancing service is essential to service for distributing workload across server farms or data centers, this paper has summarized and categorized Load Balancing methods in SDN. Many load balancing strategies based on SDN have been suggested to improve the performance of the networks. So, despite expressing of proposed methods, this paper also analyzes their advantages and disadvantage and will promote the SDN development and provide a guidance and comprehensive study for other researchers in this field who want to propose a new method of Load Balancing.
Keywords
Software Defined Networking, Load Balancing, OpenFlow, QoS, Traffic Engineering.References
- Nunes, Bruno Astuto A., et al. "A survey of software-defined networking: Past, present, and future of programmable networks." IEEE Communications Surveys & Tutorials 16.3 (2014): 1617-1634.
- Kreutz, Diego, et al. "Software-defined networking: A comprehensive survey." Proceedings of the IEEE 103.1 (2015): 14-76.
- McKeown, Nick, et al. "OpenFlow: enabling innovation in campus networks." ACM SIGCOMM Computer Communication Review 38.2 (2008): 69-74.
- ONF, “Open networking foundation,” 2014. [Online]. Available: https://www.opennetworking.org/
- Open Networking Foundation, “OpenFlow Switch Specification - Version 1.5.1 (Protocol version 0x06)”, ONF TS-025, March 2015.
- OpenFlow Switch Consortium, OpenFlow Spec, Available: https://www.opennetworking.org/images/stories/downloads/sdnresources/onf-specifications/openflow/ openflow-spec-v1.5.0.pdf, 2015.
- Lara, Adrian, Anisha Kolasani, and Byrav Ramamurthy. "Network innovation using openflow: A survey." IEEE communications surveys & tutorials 16.1 (2014): 493-512.
- Curtis, Andrew R., Wonho Kim, and Praveen Yalagandula. "Mahout: Low-overhead datacenter traffic management using end-host-based elephant detection." INFOCOM, 2011 Proceedings IEEE. IEEE, 2011.
- Chen, Wenbo, et al. "Dynamic server cluster load balancing in virtualization environment with openflow." International Journal of Distributed Sensor Networks 11.7 (2015): 531538N.
- Handigol, S. Seetharaman, M. Flajslik, N. Mckeown, R. Johari, “Plug-n-Server:load-balacing Web traffic using OpenFlow,” In ACM Sigcomm Demo,(2009): pp. 268- 270.
- R. Wang, D. Butnariu, J. Rexford, “OpenFlow-based server load balancing gone wild,” Usenix Conference on Hot Topics in Management of Internet, Cloud, and Enterprise Networks and Services. USENIX Association, (2011): pp. 12-12.
- C. Hopps, “Analysis of an Equal-Cost Multi-Path Algorithm,” RFC Editor, 2000.
- Mohammad Al-Fares, Sivasankar Radhakrishnan, Barath Raghavan, Nelson Huang, Amin Vahdat, “Hedera:dynamic flow scheduling for data center networks,” in 7th USENIX Symposium on Networked Systems Design and Implementation, 2010.
- M. Alizadeh, T. Edsall, S. Dharmapurikar, et al., “CONGA: distributed congestion-aware load balancing for datacenters,” ACM Conference on SIGCOMM. ACM, (2014): pp. 503-514.
- Li, Yu, and Deng Pan. "OpenFlow based load balancing for Fat-Tree networks with multipath support." Proc. 12th IEEE International Conference on Communications (ICC’13), Budapest, Hungary. 2013.
- F. Carpio, A. Engelmann, A. Jukan, “DiffFlow: Differentiating Short and Long Flows for Load Balancing in Data Center Networks,” 2016.
- R. Gandhi, H. H. Liu, Y. C. Hu, et al., “Duet: cloud scale load balancing with hardware and software,” ACM, 2014.
- J. Li, X.. Chang, Y. Ren, Z. Zhang, G. Wang, “An Effective Path Load Balancing Mechanism Based on SDN,” IEEE, International Conference on Trust, Security and Privacy in Computing and Communications. IEEE, (2014): pp. 527-533.
- M. Yu, J. Rexford, M. J.Freedman, J. Wang, “Scalable Flow-based Networking with DIFANE,” ACM SIGCOMM Computer Communication Review,(2010), vol. 41, pp. 351-362.
- T. Koponen, M. Casado, N. Gude, et al, “ Onix : a distributed control platform for large-scal production networks,” In OSDI , (2010): pp. 1-6.
- Tootoonchian, Amin, and Yashar Ganjali. "Hyperflow: A distributed control plane for openflow." Proceedings of the 2010 internet network management conference on Research on enterprise networking. 2010.
- A. Dixit, H. Fang, S. Mukherjee, T.V. Lakshman, R.R. Kompella, “ElastiCon: an elastic distributed sdn controller,” Proceedings of the tenth ACM/IEEE symposium on Architectures for networking and communications systems.ACM, (2014): pp. 17-28.s
- A. Krishnamurthy, S. P. Chandrabose, A. Gember-Jacobson, “Pratyaastha: an efficient elastic distributed SDN control plane,” The Workshop on Hot Topics in Software Defined NETWORKING. ACM, (2014): pp. 133-138.
- Y. P. Yu , H. Qin, “Research on load balancing strategy of controller in OpenFlow network,” Network Security Technology and Application, 2015, pp. 6-7. Network Security Technology and Application China,(2015): pp. 6-7.
- F. Y. Chen, B. Q. Wang, B. W. Wang, Z. M. Wang, "Progressive auction based switch migration mechanism in software define network", Journal of Computer Applications, vol. 35,(2015): pp. 2118-2123.
- Y. Hu, W. Wang, X. Gong, X. Que, S. Cheng, “BalanceFlow: Controller load balancing for OpenFlow networks”[C]// Cloud Computing and Intelligent Systems. IEEE, (2012), vol. 2, pp. 780-785.
- Lin, P. P., et al. "MSDN: a mechanism for scalable intra-domain control plane in SDN." Journal of Chinese Computer Systems 34.9 (2013): 1969-1974.
- Lin, Pingping, Jun Bi, and Hongyu Hu. "Asic: an architecture for scalable intra-domain control in openflow." Proceedings of the 7th International Conference on Future Internet Technologies. ACM, 2012.
- F. Cimorelli, F. D. Priscoli, A. Pietrabissa, et al., “A distributed load balancing algorithm for the control plane in software defined networking,” Mediterranean Conference on Control and Automation, (2016): pp. 1033-1040.
- S. Hassas Yeganeh, Y. Ganjali, “ Kandoo: a framework for efficient and scalable offloading of control applications,” The Workshop on Hot Topics in Software Defined Networks. ACM,(2012): pp. 19-24.
- Yao, Haipeng, et al. "A multicontroller load balancing approach in software-defined wireless networks." International Journal of Distributed Sensor Networks 11.10 (2015): 454159.
- Basta, Arsany, et al. "Towards a dynamic SDN virtualization layer: Control path migration protocol." Network and Service Management (CNSM), 2015 11th International Conference on. IEEE, 2015.
- R. Sherwood, G. Gibb, K. K. Yap, et al. “Can the production network be the testbed?,” OSDI (2010), vol. 10, pp. 1-6.
- M. Koerner, O. Kao, “Multiple service load-balancing with OpenFlow,” IEEE, International Conference on High PERFORMANCE Switching and Routing. IEEE, (2012): pp. 210-214
- G. Iannaccone, C. Chuah, S. Bhattacharyya, C. Diot, “Feasibility of IP restoration in a tier-1 backbone”, IEEE Network 18 (2004).
- Cao, Zhiruo, Zheng Wang, and Ellen Zegura. "Performance of hashing-based schemes for internet load balancing." INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. Vol. 1. IEEE, 2000.
- Bharti, Sourabh, and Kiran Kumar Pattanaik. "Dynamic distributed flow scheduling with load balancing for data center networks." Procedia Computer Science 19 (2013): 124-130.
- Distributed Data Storage in the IoT: A Performance and Reliability Approach
Abstract Views :418 |
PDF Views:0
Authors
Affiliations
1 Science and Research Branch, Islamic Azad University, Ardabil, IR
2 Department of Computer Engineering, University of Mohaghegh Ardabili, Ardabil, IR
1 Science and Research Branch, Islamic Azad University, Ardabil, IR
2 Department of Computer Engineering, University of Mohaghegh Ardabili, Ardabil, IR
Source
Networking and Communication Engineering, Vol 12, No 2 (2020), Pagination: 17-23Abstract
Internet of things (IoT) includes a lot of key technologies; In this emerging field, wireless sensors have a key role to play in sensing and collecting measures on the surrounding environment. In the deployment of large scale observation systems in remote areas, when there is not a permanent connection with the Internet, the network requires distributed storage techniques for increasing amount of data storage which decreases probability of data loss. Unlike conventional networked data storage, distributed storage is constrained by the limited resources of the sensors. In this article, we will discuss strategies for, distributed data storage method by using PSO and K-means clustering mechanism organized with the binary decision tree C4.5 in the IoT area.