Time and cost efficient cloud resource allocation for real-time data-intensive smart systems
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 m...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/84214/1/84214_Time%20and%20Cost%20Efficient%20Cloud%20Resource.pdf http://irep.iium.edu.my/84214/ https://www.mdpi.com/1996-1073/13/21/5706/htm https://doi.org/10.3390/en13215706 |
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| Summary: | 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. |
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