Evaluating Performance of Active Containers on PaaS Fog under Batch Arrivals: A Modeling Approach Modelbased performance evaluation of largescale PaaS Fog Datacenter requires to develop a hierarchical model, which is usually composed of a series of monolithic models. This paper proposes an approximate analytic modeling approach to evaluate the performance of a pool of active containers on a PaaS Fog physical node under batch task arrivals, by using an MmathrmxGmmK queue. We describe the details of the proposed model and the formulas for calculating performance measures of interest. Experiment results indicate that the proposed approach (including the model and the formulas) can approximately capture the system behaviors even when the task servicetime distribution has a large coefficient of variation (u003e1.5).

Benchmarking and Performance Modelling of MapReduce Communication Pattern Understanding and predicting the performance of big data applications running in the cloud or onpremises could help minimise the overall cost of operations and provide opportunities in efforts to identify performance bottlenecks. The complexity of the lowlevel internals of big data frameworks and the ubiquity of application and workload configuration parameters makes it challenging and expensive to come up with comprehensive performance modelling solutions. In this paper, instead of focusing on a wide range of configurable parameters, we studied the lowlevel internals of the MapReduce communication pattern and used a minimal set of performance drivers to develop a set of phase level parametric models for approximating the execution time of a given application on a given cluster. Models can be used to infer the performance of unseen applications and approximate their performance when an arbitrary dataset is used as input. Our approach is validated by running empirical experiments in two setups. On average, the error rate in both setups is xc2xb110% from the measured values.