TY  - JOUR
PB  - ACM
VL  - 40
EP  - 82
SN  - 0163-5999
A1  - Casale, Giuliano
A1  - Tribastone, Mirco
SP  - 73
Y1  - 2013/03//
TI  - Modelling exogenous variability in cloud deployments
ID  - eprints2604
AV  - none
N2  - Describing exogenous variability in the resources used by a cloud application leads to stochastic performance models that are difficult to solve. In this paper, we describe the blending algorithm, a novel approximation for queueing network models immersed in a random environment. Random environments are Markov chain-based descriptions of timevarying operational conditions that evolve independently of the system state, therefore they are natural descriptors for exogenous variability in a cloud deployment. The algorithm adopts the principle of solving a separate transient-analysis subproblem for each state of the random environment. Each subproblem is then approximated by a system of ordinary differential equations formulated according to a fluid limit theorem, making the approach scalable and computationally inexpensive. A validation study on several hundred models shows that blending can save up to two orders of magnitude of computational time compared to simulation, enabling efficient exploration of a decision space, which is useful in particular at design-time.
JF  - Performance Evaluation Review
IS  - 4
UR  - http://dx.doi.org/10.1145/2479942.2479951
ER  -