TY - GEN
T1 - Performance modeling of cloud apps using message queueing as a service (MaaS)
AU - Salah, Khaled
AU - Sheltami, Tarek Rahil
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/4/13
Y1 - 2017/4/13
N2 - This paper presents an analytical model to study the performance of cloud applications using message queueing as a service (MaaS). MaaS is a cloud service which allows the development departments to focus on delivering business and computing applications without being concerned with the underlying message queueing infrastructure to be scalable, secure, and reliable. Estimating the service delay (prior to provisioning cloud resources) of this type of cloud apps is an important engineering and resource management problem. Such estimation would help in computing the overall network and service delay that users experience. In a way, cloud providers would allocate the appropriate capacity for the needed cloud resources to meet the Service Level Agreement (SLA) parameters. In this paper, we present an analytical model by using Markov chain to study the performance of cloud apps which use MaaS. Given the expected request arrival rate, the queue size, and the expected service rate of each processing stage of the cloud app, our analytical model can estimate the app performance in terms of key SLA parameters which include response time, throughput, and request loss. In addition, our model yields equations for other key performance measures which include system idleness and utilization, queuing delay, and system and queue occupancies. Our analytical model is verified and validated by using discrete-event simulation and experimental measurements taken from an experiment conducted on AWS (Amazon Web Services) cloud.
AB - This paper presents an analytical model to study the performance of cloud applications using message queueing as a service (MaaS). MaaS is a cloud service which allows the development departments to focus on delivering business and computing applications without being concerned with the underlying message queueing infrastructure to be scalable, secure, and reliable. Estimating the service delay (prior to provisioning cloud resources) of this type of cloud apps is an important engineering and resource management problem. Such estimation would help in computing the overall network and service delay that users experience. In a way, cloud providers would allocate the appropriate capacity for the needed cloud resources to meet the Service Level Agreement (SLA) parameters. In this paper, we present an analytical model by using Markov chain to study the performance of cloud apps which use MaaS. Given the expected request arrival rate, the queue size, and the expected service rate of each processing stage of the cloud app, our analytical model can estimate the app performance in terms of key SLA parameters which include response time, throughput, and request loss. In addition, our model yields equations for other key performance measures which include system idleness and utilization, queuing delay, and system and queue occupancies. Our analytical model is verified and validated by using discrete-event simulation and experimental measurements taken from an experiment conducted on AWS (Amazon Web Services) cloud.
KW - Cloud Applications
KW - Cloud Computing
KW - Cloud Queues
KW - Performance Analysis
KW - SLA
UR - http://www.scopus.com/inward/record.url?scp=85018900195&partnerID=8YFLogxK
U2 - 10.1109/ICIN.2017.7899251
DO - 10.1109/ICIN.2017.7899251
M3 - Conference contribution
AN - SCOPUS:85018900195
T3 - Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017
SP - 65
EP - 71
BT - Proceedings of the 2017 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017
A2 - Secci, Stefano
A2 - Crespi, Noel
A2 - Manzalini, Antonio
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th Conference on Innovations in Clouds, Internet and Networks, ICIN 2017
Y2 - 7 March 2017 through 9 March 2017
ER -