Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Analysis, modeling and simulation of workload patterns in a large-scale utility cloud
AU - Moreno, Ismael Solis
AU - Garraghan, Peter
AU - Townend, Paul
AU - Xu, Jie
PY - 2014/4/1
Y1 - 2014/4/1
N2 - Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.
AB - Understanding the characteristics and patterns of workloads within a Cloud computing environment is critical in order to improve resource management and operational conditions while Quality of Service (QoS) guarantees are maintained. Simulation models based on realistic parameters are also urgently needed for investigating the impact of these workload characteristics on new system designs and operation policies. Unfortunately there is a lack of analyses to support the development of workload models that capture the inherent diversity of users and tasks, largely due to the limited availability of Cloud tracelogs as well as the complexity in analyzing such systems. In this paper we present a comprehensive analysis of the workload characteristics derived from a production Cloud data center that features over 900 users submitting approximately 25 million tasks over a time period of a month. Our analysis focuses on exposing and quantifying the diversity of behavioral patterns for users and tasks, as well as identifying model parameters and their values for the simulation of the workload created by such components. Our derived model is implemented by extending the capabilities of the CloudSim framework and is further validated through empirical comparison and statistical hypothesis tests. We illustrate several examples of this work's practical applicability in the domain of resource management and energy-efficiency.
KW - Computational modeling
KW - Analytical models
KW - Cloud computing
KW - Computer applications
KW - Resource management
KW - Mathematical model
U2 - 10.1109/TCC.2014.2314661
DO - 10.1109/TCC.2014.2314661
M3 - Journal article
VL - 2
SP - 208
EP - 221
JO - IEEE Transactions on Cloud Computing
JF - IEEE Transactions on Cloud Computing
SN - 2168-7161
IS - 2
ER -