Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/23100
Title: Time and cost efficient cloud resource allocation for real-time data-intensive smart systems
Authors: Qureshi, Muhammad Shuaib 
Qureshi, Muhammad Bilal 
Fayaz, Muhammad 
Zakarya, Muhammad 
Aslam, Sheraz 
Shah, Asadullah 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Data-intensive smart application;Cloud computing;Resource allocation;Real-time systems;Smart grid
Issue Date: 1-Nov-2020
Source: Energies, 2020, vol. 13, no. 21, articl. no. 5706
Volume: 13
Issue: 21
Journal: Energies 
Abstract: 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.
URI: https://ktisis.cut.ac.cy/handle/10488/23100
ISSN: 1996-1073
DOI: 10.3390/en13215706
Rights: © by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : International Islamic University 
University of Central Asia 
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology 
Abdul Wali Khan University 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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