TY - JOUR
T1 - Energy-Aware VM Placement and Task Scheduling in Cloud-IoT Computing
T2 - Classification and Performance Evaluation
AU - Ismail, Leila
AU - Materwala, Huned
N1 - Funding Information:
Manuscript received March 2, 2018; revised April 22, 2018, May 29, 2018, and June 26, 2018; accepted July 31, 2018. Date of publication August 15, 2018; date of current version January 16, 2019. This work was supported by the Emirates Center for Energy and Environment Research of the United Arab Emirates University under Grant 31R101. (Corresponding author: Leila Ismail.) The authors are with the HPC, Grid/Cloud Computing, and Distributed Systems Research Laboratory, College of Information Technology, UAE University, Al Ain 15551, UAE (e-mail: leila@uaeu.ac.ae). Digital Object Identifier 10.1109/JIOT.2018.2865612
Publisher Copyright:
© 2014 IEEE.
PY - 2018/12
Y1 - 2018/12
N2 - Cloud Internet of Things (IoT) is a novel paradigm, where the limitations of IoT associated devices in terms of storage, data access, scalability, networking and computing, and complex analysis are solved through use of the cloud computing infrastructure. The pervasive adoption of cloud in the IoT framework, makes the underlying data centers exacerbate problems like the environmental carbon footprint and operational costs which arise from the high energy consumption of computing servers. Several works proposed virtual machine placement and task scheduling algorithms to reduce the energy consumption of the underlying cloud infrastructure. However, each algorithm uses a different environment, experimental setup, power consumption model and workload for its evaluation, making it difficult to compare among them. In this paper, we give a classification and evaluation of 13 different algorithms using a unified setup, with the aim of achieving an objective comparison. The workload used for the evaluation is selected to typify IoT applications, such as connected vehicles, wide area measurement systems for the power grid, and smart meters for advanced meter infrastructure. The detailed performance analysis is elaborated in this paper.
AB - Cloud Internet of Things (IoT) is a novel paradigm, where the limitations of IoT associated devices in terms of storage, data access, scalability, networking and computing, and complex analysis are solved through use of the cloud computing infrastructure. The pervasive adoption of cloud in the IoT framework, makes the underlying data centers exacerbate problems like the environmental carbon footprint and operational costs which arise from the high energy consumption of computing servers. Several works proposed virtual machine placement and task scheduling algorithms to reduce the energy consumption of the underlying cloud infrastructure. However, each algorithm uses a different environment, experimental setup, power consumption model and workload for its evaluation, making it difficult to compare among them. In this paper, we give a classification and evaluation of 13 different algorithms using a unified setup, with the aim of achieving an objective comparison. The workload used for the evaluation is selected to typify IoT applications, such as connected vehicles, wide area measurement systems for the power grid, and smart meters for advanced meter infrastructure. The detailed performance analysis is elaborated in this paper.
KW - Cloud Internet of Things (IoT)
KW - Energy-aware task scheduling algorithms
KW - Energy-aware virtual machine placement algorithms
KW - Green cloud computing
KW - IoT
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U2 - 10.1109/JIOT.2018.2865612
DO - 10.1109/JIOT.2018.2865612
M3 - Article
AN - SCOPUS:85051675520
SN - 2327-4662
VL - 5
SP - 5166
EP - 5176
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 6
M1 - 8437124
ER -