Heuristic Resource Reservation Policies for Public Clouds in the IoT Era

With the advances in the IoT era, the number of wireless sensor devices has been growing rapidly. This increasing number gives rise to more complex networks where more complex tasks can be executed by utilizing more computational resources from the public clouds. Cloud service providers use various...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 22; no. 23; p. 9034
Main Author Gul, Omer Melih
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 22.11.2022
MDPI
Subjects
Algorithms
Analysis
Blockchain
Cloud Computing
cost optimization
Costs
Integer programming
Internet of Things
Policy
Polynomials
Prices
public cloud
resource reservation
Robots
Sensors
Software services
Task complexity
Traveling salesman problem
User requirements
Workloads
resource reservation
public cloud
cost optimization
cloud computing
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Summary:With the advances in the IoT era, the number of wireless sensor devices has been growing rapidly. This increasing number gives rise to more complex networks where more complex tasks can be executed by utilizing more computational resources from the public clouds. Cloud service providers use various pricing models for their offered services. Some models are appropriate for the cloud service user's short-term requirements whereas the other models are appropriate for the long-term requirements of cloud service users. Reservation-based price models are suitable for long-term requirements of cloud service users. We used the pricing schemes with spot and reserved instances. Reserved instances support a hybrid cost model with fixed reservation costs that vary with contract duration and an hourly usage charge which is lower than the charge of the spot instances. Optimizing resources to be reserved requires sufficient research effort. Recent algorithms proposed for this problem are generally based on integer programming problems, so they do not have polynomial time complexity. In this work, heuristic-based polynomial time policies are proposed for this problem. It is exhibited that the cost for the cloud service user which uses our approach is comparable to optimal solutions, i.e., it is near-optimal.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22239034