A Multi-Objective Dynamic Scheduling Approach for IoT Task Offloading on Amazon EC2 Spot Instances

Computer and Knowledge Engineering

مقاله 1، دوره 3، شماره 1 - شماره پیاپی 5، اسفند 2020، صفحه 1-10   

نوع مقاله: Internet of Thing (IoT)-Yaghmaee

شناسه دیجیتال (DOI): 10.22067/cke.2020.63251.0

نویسندگان

Arash Deldari^{* 1}؛ Ghasem Akbarian²؛ Mostafa Sabzekar³

¹Department of Computer Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran.

²Department of Computer Engineering, Imamreza International University, Mashhad, Iran.

³Department of Computer Engineering, Birjand University of Technology, Birjand, Iran.

چکیده

: In the recent years, the Internet of things (IoT) applications have affected many aspects of human life due to the continuous innovation in hardware, software, and communication technologies along with the growth of the connected devices. The enormous amounts of data generated by these devices must be stored and then processed for analysis and decision-making. One promising solution for this purpose is cloud computing. However, offloading tasks to the cloud imposes costs that must be reduced by intelligent techniques and optimization algorithms. Fortunately, considering cloud computing instances with dynamic pricing referred to as spot instances can significantly reduce the processing costs. Although these instances offer a considerable cost reduction compared to on-demand instances, they can be evicted by the cloud providers and require special scheduling techniques. In this paper, we propose a dynamic scheduling method for IoT task offloading on Amazon EC2 spot instances. The proposed method considers both the predicted execution time of the task and the specified deadline that can be mapped on spot instances. The empirical results denote that the proposed method leads to a considerable reduction in the execution costs, while it simultaneously increases the number of successful tasks executed before the deadline and decreases task turnaround time.

کلیدواژه‌ها

Index terms؛ Cloud computing؛ the Internet of things؛ Spot instances؛ Task offloading

مراجع

[1]           M. Bhatia and S. K. Sood, “Exploring Temporal Analytics in Fog-Cloud Architecture for Smart Office HealthCare,” Mobile Networks and Applications, vol. 24, no. 4, pp. 1392–1410, 2019.

[2]           M. Bhatia, S. K. Sood, and S. Kaur, “Quantumized approach of load scheduling in fog computing environment for IoT applications,” Computing, vol.102, pp. 1097-1115, 2020.

[3]           M. Conti, A. Dehghantanha, K. Franke, and S. Watson, “Internet of Things security and forensics: Challenges and opportunities,” Future Generation Computer Systems, vol. 78. Elsevier B.V., pp. 544–546, 01-Jan-2018.

[4]           A. Čolaković and M. Hadžialić, “Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues,” Computer Networks, vol. 144, pp. 17–39, 2018.

[5]           H. A. Alameddine, S. Sharafeddine, S. Sebbah, S. Ayoubi, and C. Assi, “Dynamic task offloading and scheduling for low-latency IoT services in multi-access edge computing,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 3, pp. 668–682, 2019.

[6]           M. Aazam, S. Zeadally, and K. A. Harras, “Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities,” Future Generation Computer Systems, vol. 87, pp. 278–289, 2018.

[7]           P. M. T. Grance, “The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology,” Recommendation of the National institute of Standards and Technology, vol. 145, p. 7, 2011.

[8]           D. Kumar, G. Baranwal, Z. Raza, and D. P. Vidyarthi, “A Survey on Spot Pricing in Cloud Computing,” Journal of Network and Systems Management, vol. 26, no. 4, pp. 809–856, Oct. 2018.

[9]           C. K. Swain, N. Saini, and A. Sahu, “Reliability aware scheduling of bag of real time tasks in cloud environment,” Computing, vol. 102, no. 2, pp. 451–475, 2020.

[10]         R. G. Martinez, A. Lopes, and L. Rodrigues, “Planning workflow executions when using spot instances in the cloud,” in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing  - SAC ’19,  pp. 310–317, 2019.

[11]         H. M. Nguyen, G. Kalra, T. J. Jun, S. Woo, and D. Kim, “ESNemble: an Echo State Network-based ensemble for workload prediction and resource allocation of Web applications in the cloud,” The Journal of Supercomputing, vol. 75, no. 10, pp. 6303–6323, 2019.

[12]         “Amazon EC2 Spot – Save up-to 90% on On-Demand Prices.” https://aws.amazon.com/ec2/spot/ (accessed Mar. 12, 2019).

[13]         “Preemptible VMs - Compute Instances  |  Google Cloud.” https://cloud.google.com/preemptible-vms/ (accessed Apr. 16, 2019).

[14]         “Announcing low-priority VMs on scale sets now in public preview | Azure Blog and Updates | Microsoft Azure.” https://azure.microsoft.com/en-us/blog/low-priority-scale-sets/ (accessed Mar. 28, 2020).

[15]         S. G. Domanal and G. R. M. Reddy, “An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment,” Future Generation Computer Systems, vol. 84, pp. 11–21, 2018.

[16]         A. K. Mishra, D. K. Yadav, Y. Kumar, and N. Jain, “Improving reliability and reducing cost of task execution on preemptible VM instances using machine learning approach,” J. Supercomput., vol. 75, no. 4, pp. 2149–2180, Apr. 2019.

[17]         S. Agarwal, A. K. Mishra, and D. K. Yadav, “Forecasting price of amazon spot instances using neural networks,” International Journal of Applied Engineering Research, vol. 12, no. 20, pp. 10276–10283, 2017.

[18]         M. Baughman, K. Chard, I. Foster, R. Wolski, and C. Haas, “Predicting Amazon Spot Prices with LSTM Networks,” Proceedings of the 9th Workshop on Scientific Cloud Computing, pp. 1–7, 2018.

[19]         H. Al-Theiabat, M. Al-Ayyoub, M. Alsmirat, and M. Aldwair, “A Deep Learning Approach for Amazon EC2 Spot Price Prediction,” in 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–5, 2018.

[20]         A. Deldari, M. Naghibzadeh, and S. Abrishami, “CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud,” J. Supercomput., vol. 73, no. 2, pp. 756–781, 2017.

[21]         Z. Cai, X. Li, R. Ruiz, and Q. Li, “Price forecasting for spot instances in Cloud computing,” Future Generation Computer Systems, vol. 79, pp. 38–53, 2018.

[22]         S. Shastri and D. Irwin, “HotSpot: Automated Server Hopping in Cloud Spot Markets Supreeth,” Proceedings of the 2017 Symposium on Cloud Computing - SoCC '17, pp. 493–505, 2017.

[23]         Z. Shen, R. van Renesse, Q. Jia, H. Weatherspoon, and W. Song, “Smart spot instances for the supercloud,” Proceedings of the 3rd Workshop on CrossCloud Infrastructures Platforms, pp. 1–6, 2016.

[24]         I. Jangjaimon and N. F. Tzeng, “Effective cost reduction for elastic clouds under spot instance pricing through adaptive checkpointing,” IEEE Transactions on Computers, vol. 64, no. 2, pp. 396–409, 2015.

[25]         S. Yi, A. Andrzejak, and D. Kondo, “Monetary Cost-Aware Checkpointing and Migration on Amazon Cloud Spot Instances,” IEEE Transactions on Services Computing, vol. 5, no. 4, pp. 512–524, 2012.

[26]         D. Poola, K. Ramamohanarao, and R. Buyya, “Fault-tolerant workflow scheduling using spot instances on clouds,” Procedia Computer Science, vol. 29, pp. 523–533, 2014.

[27]         S. Basu et al., “An intelligent/cognitive model of task scheduling for IoT applications in cloud computing environment,” Future Generation Computer Systems, vol. 88, pp. 254–261, Nov. 2018.

[28]         H. M. Dipu Kabir, A. S. Sabyasachi, A. Khosravi, M. A. Hosen, S. Nahavandi, and R. Buyya, “A cloud bidding framework for deadline constrained jobs,” in Proceedings of the IEEE International Conference on Industrial Technology, 2019.

[29]         J. Fabra, J. Ezpeleta, and P. Álvarez, “Reducing the price of resource provisioning using EC2 spot instances with prediction models,” Future Generation Computer Systems, vol. 96, pp. 348–367, Jul. 2019.

[30]         V. Khandelwal, A. Chaturvedi, and C. P. Gupta, “Amazon EC2 Spot Price Prediction using Regression Random Forests,” IEEE Transactions on Cloud Computing, pp. 1–1, 2017.

[31]         D. Liu, Z. Cai, and X. Li, “Hidden markov model based spot price prediction for cloud computing,” Proceedings - 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017 pp. 996–1003, 2018.

[32]         P. Sharma, S. Lee, T. Guo, D. Irwin, and P. Shenoy, “SpotCheck: designing a derivative IaaS cloud on the spot market,” Proceedings of the Tenth European Conference on Computer Systems - EuroSys '15, pp. 1–15, 2015.

[33]         A. A. Mutlag, M. K. A. Ghani, N. al Arunkumar, M. A. Mohammed, and O. Mohd, “Enabling technologies for fog computing in healthcare IoT systems,” Future Generation Computer Systems, vol. 90, pp. 62–78, 2019.

[34]         M. Goudarzi, H. Wu, M. S. Palaniswami, and R. Buyya, “An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments,” IEEE Transactions on Mobile Computing, vol.20, pp. 1298-1311, 2021.

[35]         “Amazon EC2 Spot Two-Minute Warning is Now Available via Amazon CloudWatch Events.”https:// aws. amazon.com/about-aws/whats-new/2018/01/ amazon-ec2-spot-two-minute-warning-is-now-available-via-amazon-cloudwatch-events/(accessed Apr. 14, 2019).

[36]         R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and 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, no. 1, pp. 23–50, 2011.

[37]         “AWS Spot Pricing Market | Kaggle.” https://www.kaggle.com/noqcks/aws-spot-pricing-market (accessed Apr. 07, 2019).

[38]         “Spot Instance Pricing History - Amazon Elastic Compute Cloud. ”https://docs.aws.amazon.com/ AWSEC2/latest/UserGuide/using-spot-instances-history.html (accessed Apr. 07, 2019).

[39]         O. A. Ben-Yehuda, M. Ben-Yehuda, A. Schuster, and D. Tsafrir, “Deconstructing Amazon EC2 spot instance pricing,” Proceedings - 2011 3rd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2011, pp. 304–311, 2011.

[40]     “25 Datasets for Deep Learning in IoT | Packt Hub.” https://hub.packtpub.com/25-datasets-deep-learning-iot/ (accessed Mar. 26, 2020).