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Open AccessArticle

Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

1
KICT, International Islamic University, Kuala Lumpur 50728, Malaysia
2
Department of Computer Science, School of Arts and Sciences, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan
3
Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan
4
Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
5
Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
*
Author to whom correspondence should be addressed.
Energies 2020, 13(21), 5706; https://doi.org/10.3390/en13215706
Received: 17 July 2020 / Revised: 27 August 2020 / Accepted: 9 September 2020 / Published: 31 October 2020
(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids)
Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts. View Full-Text
Keywords: data-intensive smart application; cloud computing; resource allocation; real-time systems; smart grid data-intensive smart application; cloud computing; resource allocation; real-time systems; smart grid
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MDPI and ACS Style

Qureshi, M.S.; Qureshi, M.B.; Fayaz, M.; Zakarya, M.; Aslam, S.; Shah, A. Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems. Energies 2020, 13, 5706. https://doi.org/10.3390/en13215706

AMA Style

Qureshi MS, Qureshi MB, Fayaz M, Zakarya M, Aslam S, Shah A. Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems. Energies. 2020; 13(21):5706. https://doi.org/10.3390/en13215706

Chicago/Turabian Style

Qureshi, Muhammad S.; Qureshi, Muhammad B.; Fayaz, Muhammad; Zakarya, Muhammad; Aslam, Sheraz; Shah, Asadullah. 2020. "Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems" Energies 13, no. 21: 5706. https://doi.org/10.3390/en13215706

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