An Autonomic Approach to Risk-Aware Data Center Overbooking

Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to p...

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Vydáno v:IEEE transactions on cloud computing Ročník 2; číslo 3; s. 292 - 305
Hlavní autoři: Tomas, Luis, Tordsson, Johan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE Computer Society 01.07.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Admission Control
Cloud computing
Computer Science
Control Theory
datalogi
Degradation
Elasticity
Fuzzy Logic Programming
Overbooking
Resource management
Resource Utilization
Risk management
Scheduling
Servers
scheduling
overbooking
fuzzy logic programming
control theory
resource utilization
Admission control
ISSN:2168-7161, 2372-0018
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Shrnutí:Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time-changing the acceptable level of risk-depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50 percent increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.
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ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2014.2326166