Trends in heterogeneous computing in 2020

Trends in heterogeneous computing in 2020

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  Trends in HeterogeneousComputing in 2020 International Journal of Distributed and Parallel systems (IJDPS) ISSN : 0976 - 9757 [Online] ; 2229 - 3957 [Print] http://airccse.org/journal/ijdps/ijdps.html
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  Real-Time Adaptive Energy-SchedulingAlgorithm for Virtualized Cloud Computing D B Srinivas1 , H K Krishnappa2 , Rajan M A3 , Sujay N. Hegde4 1,4 Nitte Meenakshi Institute of Technology, Bangalore, India 2 R V College of Engineering, Bangalore, India 3 TCS Research and Innovation, Bangalore, India ABSTRACT Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches. KEYWORDS Virtual Machine (VM); Virtualized Data Centers (VDCs); Quality of Service (QoS); Dynamic Voltage and Frequency Scaling (DVFS); Real Time Adaptive Energy-Scheduling (RTAES). Full Text : http://aircconline.com/ijdps/V10N1/10119ijdps01.pdf
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  REFERENCES [1] Lizhe Wang,Gregor von Laszewski, Jai Dayal, Thomas R. Furlani, Thermal aware workload scheduling with backfilling for green data centers, in: IPCCC, 2009, pp. 289–296. [2] William Forrest, How to cut data centre carbon emissions? Website, December 2008. [3] Yongpan Liu et al. “Thermal vs energy optimization for dvfs-enabled processors in embedded systems”. In: Quality Electronic Design, 2007. ISQED’07. 8th International Symposium on. IEEE. 2007, pp. 204–209. [4] J.-J. Chen and C.-F. Kuo, ``Energy-efficient scheduling for real-time systems on dynamic voltage scaling (DVS) platforms,'' in Proc. IEEE RTCSA, Aug. 2007, pp. 28-38. [5] V. Devadas and H. Aydin, ``Coordinated power management of periodic real-time tasks on chip multiprocessors,'' in Proc. Green Comput. Conf., 2010, pp. 61-72. [6] Arabnejad, H., Barbosa, J.G.: A budget constrained scheduling algorithm for workflow applications. J. Grid Comput. 12(4), 15 (2014) [7] Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in amazon ec2. Cluster Comput. 17(2), 169–189 (2014) [8] Q. Wu, F. Ishikawa, Q. Zhu, Y. Xia and J. Wen, "Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds," in IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 12, pp. 3401-3412, Dec. 2017. [9] S. Chinprasertsuk and S. Gertphol, "Power model for virtual machine in cloud computing," 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), Chon Buri, 2014, pp. 140- 145. [10] T. AlEnawy et al., ``Energy-aware task allocation for rate monotonic scheduling,'' in Proc. IEEE RTAS, Apr. 2005, pp. 213-223. [11] C.-Y. Yang, J.-J. Chen, T.-W. Kuo, and L. Thiele, ``An approximation scheme for energy- efficient scheduling of real-time tasks in heterogeneous multiprocessor systems,'' in Proc. DATE, 2009, pp. 694-699. [12] E. Seo, J. Jeong, S. Park, and J. Lee, ``Energy efcient scheduling of realtime tasks on multicore processors,'' IEEE Trans. Parallel Distrib. Syst., vol. 19, no. 11, pp. 1540-1552, Nov. 2008. [13] C. Xian and Y.-H. Lu, ``Dynamic voltage scaling for multitasking real-time systems with uncertain execution time,'' in Proc. ACM GLSVLSI, 2006, pp. 392-397. [14] C. Xian, Y.-H. Lu, and Z. Li, ``Energy-aware scheduling for real-time multiprocessor systems with uncertain task execution time,'' in Proc. ACM DAC, 2007, pp. 664-669. [15] Baliga, J, Ayre, R. W., Hinton, K., and Tucker, R. 2011. Green cloud computing: Balancing energy in processing, storage, and transport. Proceedings of the IEEE, 2011. 99(1):149–167. [16] Canali, C. and Lancellotti, R. Parameter Tuning for Scalable Multi-Resource Server Consolidation in Cloud Systems. Communications Software and Systems, 2016. [17] Urgaonkar, R., Kozat, U. C., Igarashi, K., and Neely, M. J. Dynamic resource allocation and power management in virtualized data centers. In NOMS, 2010, pp 479–486. IEEE. [18] A. Alsarhan, A. Itradat, A. Y. Al-Dubai, A. Y. Zomaya and G. Min, "Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments," in IEEE Transactions on Parallel and Distributed Systems, vol. 29, no. 1, pp. 31-42, Jan. 1 2018.
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  [19] P. Arroba,J. M. Moya, J. L. Ayala and R. Buyya, "DVFS-Aware Consolidation for Energy-Efficient Clouds," 2015 International Conference on Parallel Architecture and Compilation (PACT), San Francisco, CA, 2015, pp. 494- 495. [20] W. Chawarut and L. Woraphon, "Energy-aware and real-time service management in cloud computing," 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, 2013, pp. 1-5. [21] H. Faragardi, A. Rajabi, K. Sandström and T. Nolte, "EAICA: An energy-aware resource provisioning algorithm for Real-Time Cloud services," 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), Berlin, 2016, pp.1-10. [22] Z. Bagheri and K. Zamanifar, "Enhancing energy efficiency in resource allocation for real-time cloud services," Telecommunications (IST), 2014 7th International Symposium on, Tehran, 2014, pp. 701-706. [23] Zheng, W., Sakellariou, R.: Budget-deadline constrained workflow planning for admission control. J. Grid Comput. 11(4), 105–119, 2013. [24] T. Knauth and C. Fetzer, “Energy-aware scheduling for infrastructure clouds,” in Cloud Computing Technology and Science (CloudCom), 2012,IEEE 4th International Conference on. IEEE, 2012, pp.58–65. [25] S. K. Garg, C. S. Yeo, and R. Buyya, “Green cloud framework for improving carbon efficiency of clouds,” in Euro-Par 2011 Parallel Processing. Springer, 2011, pp. 491–502. [26] Z. Zhu, G. Zhang, M. Li and X. Liu, "Evolutionary Multi-Objective Workflow Scheduling in Cloud," in IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 5, pp. 1344-1357, May 1 2016. [27] D.B Srinivas, Puneeth R.P, Rajan M.A, Sanjay. H.A, An integrated framework to measure the energy consumption of a parallel application on a computational grid, International Journal of Computer Science and Network Security, VOL.16 No.7, pp. 94-98, 2016,
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  Advanced Diffusion Approachto Dynamic Load- Balancing for Cloud Storage Eman Daraghmi1 and Yousef-Awwad Daraghmi2 1 Department of Applied Computing, Palestine Technical University Kadoori (PTUK), Tulkarm, Palestine 2 Department of Computer Systems Engineering, Palestine Technical University Kadoori (PTUK), Tulkarm, Palestine ABSTRACT Load-balancing techniques have become a critical function in cloud storage systems that consist of complex heterogeneous networks of nodes with different capacities. However, the convergence rate of any load-balancing algorithm as well as its performance deteriorated as the number of nodes in the system, the diameter of the network and the communication overhead increased. Therefore, this paper presents an approach aims at scaling the system out not up - in other words, allowing the system to be expanded by adding more nodes without the need to increase the power of each node while at the same time increasing the overall performance of the system. Also, our proposal aims at improving the performance by not only considering the parameters that will affect the algorithm performance but also simplifying the structure of the network that will execute the algorithm. Our proposal was evaluated through mathematical analysis as well as computer simulations, and it was compared with the centralized approach and the original diffusion technique. Results show that our solution outperforms them in terms of throughput and response time. Finally, we proved that our proposal converges to the state of equilibrium where the loads in all in-domain nodes are the same since each node receives an amount of load proportional to its capacity. Therefore, we conclude that this approach would have an advantage of being fair, simple and no node is privileged. KEYWORDS Load balancing, cloud storage, Heterogeneous, Simulation, Task assignment Full Text : http://aircconline.com/ijdps/V10N3/10319ijdps01.pdf
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  REFERENCES [1] H.-C. Hsiao,H.-Y. Chung, H. Shen and Y.-C. Chao, "Load Rebalancing for Distributed File Systems in Clouds," IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 5, pp. 951-962, 2013. [2] E. Y. Daraghmi and S. M. Yuan, "In-domain neighborhood approach to heterogeneous dynamic load balancing in real world network," in 14'th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT'13), Taipei, Taiwn, 2013. [3] C. P. A. a. P. Berenbrink., "Distributed selfish load balancing with weights and speeds.," in The 2012 ACM symposium on Principles of distributed computing, New York, USA, 2012. [4] J. Bahi, R. Couturier and F. Vernier, "Synchronous Distributed Load Balancing on Totally Dynamic Networks," in Parallel and Distributed Processing Symposium, 2007. [5] E. Luque, A. Ripoll and A. C. a. T. Margalef, "A distributed diffusion method for dynamic load balancing on parallel computers," in Euromicro Workshop on Parallel and Distributed Processing, 1995. [6] P.Neelakantan, "Decentralized Load Balancing In Heterogeneous Systems Using Diffusion Approach," International Journal of Distributed and Parallel systems (IJDPS), vol. 3, no. 1, pp. 229 -239, 2012. [7] C.-C. Hui and S. Chanson, "A hydro-dynamic approach to heterogeneous dynamic load balancing in a network of computers," in Proceedings of the 1996 International Conference on Parallel Processing Software., 1996. [8] G. Cybenko, "Dynamic load balancing for distributed memory multiprocessors," Journal of Parallel and Distributed Computing, vol. 7, no. 2, pp. 279-301, 1989. [9] J. E. Boillat, "Load balancing and Poisson equation in a graph," Concurrency: Practice and Experience, vol. 2, no. 4, pp. 289-313, 1990. [10] "Google File System," [Online]. Available: http://en.wikipedia.org/wiki/Google_File_System. [11] R. N. Calheiros, R. Ranjan, A. Beloglazo, C. A. F. D. Rose and R. Buyya, "CloudSim: a toolkit for modeling and simulation of cloud," SOFTWARE – PRACTICE AND EXPERIENCE, vol. 41, no. 1,pp. 23-50, 2010. [12] R. M. I. Stoica, D. Liben-Nowell, D. R. Karger, M. F. Kaashoek, F. Dabek and H. Balakrishnan, "Chord: a Scalable Peer-to-Peer Lookup Protocol for Internet Applications," in Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols, San Diego, California, USA, 2001.
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  Design and Analysisof Secure Smart Home for Elderly People Mayada Elsaid, Sara Altuwaijri, Nouf Aljammaz and Anees Ara Computer Science Department, College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia ABSTRACT Internet of Things (IoT) technology is used to enhance the safety of the elderly living in smart home environments and to help their caregivers. The daily behaviour of the elderly people is collected using IoT sensors and then evaluated to detect any abnormal behaviour. This research paper analyzes the smart home based anomaly detection system from a security perspective, to answer the question whether it is reliable and secure enough to leave elderly people alone in their smart homes. In this direction comparative analysis of literature is done to identify the potential security breaches on all layers of an IoT device. Further, this paper proposes a secure smart home model, built using Cisco Packet Tracer to simulate a network of IoT devices in a smart home environment. Consequently, a list of security countermeasures is proposed to protect the IoT devices from the identified attacks. . KEYWORDS Cyber physical systems, anomaly detection system, intrusion detection system, secure smart home, IoT. Full Text : http://aircconline.com/ijdps/V10N6/10619ijdps01.pdf
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  REFERENCES [1] Aran, O.,Sanchez-Cortes, D., Do, M. T., & Gatica-Perez, D. (2016, October). "Anomaly detection in elderly daily behavior in ambient sensing environments". In International Workshop on Human Behavior Understanding (pp. 51-67). Springer, Cham. [2] Paudel, R., Dunn, K., Eberle, W., & Chaung, D. (2018, May). "Cognitive Health Prediction on the Elderly Using Sensor Data in Smart Homes". In The Thirty-First International Flairs Conference. [3] Hoque, E., Dickerson, R. F., Preum, S. M., Hanson, M., Barth, A., & Stankovic, J. A. (2015, June). "Holmes: A comprehensive anomaly detection system for daily in-home activities". In 2015 International Conference on Distributed Computing in Sensor Systems (pp. 40-51). IEEE. [4] Hsu, Y. L., Chou, P. H., Chang, H. C., Lin, S. L., Yang, S. C., Su, H. Y., ... & Kuo, Y. C. (2017). "Design and implementation of a smart home system using multisensor data fusion technology". Sensors, 17(7), 1631. [5] Pal, D., Triyason, T., & Funikul, S. (2017, December). Smart homes and quality of life for the elderly: A systematic review. In 2017 IEEE International Symposium on Multimedia (ISM) (pp. 413-419). IEEE. [6] Lê, Q., Nguyen, H. B., & Barnett, T. (2012). "Smart homes for older people: Positive aging in a digital world". Future internet, 4(2), 607-617. [7] Boyanov, L., & Minchev, Z. (2014). "Cyber Security Challenges in Smart Homes". Volume 38: Cyber Security and Resiliency Policy Framework,IOS Press Ebooks [8] Paudel, R., Eberle, W., & Holder, L. B (2018). "Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Approach". In: Proceedings of the 2018 International Conference on Data Science, pp. 163–169. CSREA . [9] Rao, T. A. "Security Challenges Facing IoT Layers and its Protective Measures". International Journal of Computer Applications, 975, 8887. [10] Lin, H., & Bergmann, N. (2016). "IoT privacy and security challenges for smart home environments". Information, 7(3), 44. [11] Ara, A., Al-Rodhaan, M., Tian, Y., & Al-Dhelaan, A. (2015). "A secure service provisioning framework for cyber physical cloud computing systems". arXiv preprint arXiv:1611.00374. [12] Mtshali, P., & Khubisa, F. (2019, March). "A Smart Home Appliance Control System for Physically Disabled People". In 2019 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-5). IEEE. [13] Karimi, K., & Krit, S. (2019, July). "Smart home-Smartphone Systems: Threats, Security Requirements and Open research Challenges". In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE) (pp. 1-5). IEEE. [14] Adiono, T., Tandiawan, B., & Fuada, S. (2018). "Device protocol design for security on internet of things based smart home". International Journal of Online Engineering (iJOE), 14(07), 161-170. [15] Sultana, S. N., Ramu, G., & Reddy, B. E. (2014). "Cloud-based development of smart and connected data in healthcare application". International Journal of Distributed and Parallel Systems, 5(6), 1.
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  AUTHORS BIOGRAPHY Mayada Elsaidis a senior Software Engineering ( Cyber Security) student in Prince Sultan University. She is an IEEE member and chair in IEEE-Student Chapter. Her areas of interest and research are cyber physical systems security and IoT. She has presented security and business process management papers in local conferences and research forums. Sara Altuwaijri is a bachelor student, majoring in Software Engineering (Cyber Security) at Prince Sultan University. She’s currently leading two clubs in the university by arranging events and bootcamps that are related to many topics of computer science. She’s the president of Edtech club and the Vice Chair if IEEE chapter at PSU. She received nano degrees in Artificial Intelligence and Self-driving cars. Her research interests include security & privacy in IoT and cyber physical systems. Nouf Aljammaz is a senior Computer Science (Cyber Security) student focused on improving facility security through diligent approach and sense of personal responsibility. Resilient individual trained in security. Her research interest includes security domains related to risks management, optimization techniques relates to asset protection and threat minimizations. Anees Ara received her BSc in Computer Science and MSc in Mathematics with Computer Science from Osmania University, India in 2005 and 2007 respectively. She has received her PhD degree from King Saud University and she is currently working as Assistant Professor at College of Computer and Information Sciences, Prince Sultan University, Kingdom of Saudi Arabia. She is an active member of Security Engineering Lab, Prince Sultan University, KSA and IEEE, computing society. In addition, she is an active reviewer of international journals. Her research interests are broadly divided into privacy and security, which are related to cloud computing, cryptograph, smart environment, cyber physical systems and big data.