Energy-Aware VM Placement and Task Scheduling in Cloud-IoT Computing: Classification and Performance Evaluation

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 f...

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Veröffentlicht in:IEEE internet of things journal Jg. 5; H. 6; S. 5166 - 5176
Hauptverfasser: Ismail, Leila, Materwala, Huned
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Classification
Classification algorithms
Cloud computing
Cloud Internet of Things (IoT)
Computational modeling
Computing costs
Data centers
Energy consumption
Energy management
energy-aware task scheduling algorithms
energy-aware virtual machine placement algorithms
Environmental impact
green cloud computing
Heuristic algorithms
Infrastructure
Internet of Things
IoT
Measuring instruments
Operating costs
Performance evaluation
Placement
Power consumption
Task analysis
Task scheduling
Virtual environments
Workload
Workloads
ISSN:2327-4662, 2327-4662
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2018.2865612