Modelling and Simulation of ElasticSearch using CloudSim Simulation can be a powerful technique for evaluating the performance of largescale cloud computing services in a relatively low cost, low risk and timesensitive manner. Largescale data indexing, distribution and management is complex to analyse in a timely manner. In this paper, we extend the CloudSim cloud simulation framework to model and simulate a distributed search engine architecture and its workload characteristics. To test the simulation framework, we develop a model based on a realworld ElasticSearch deployment on Linknovate.com. An experimental evaluation of the framework, comparing simulated and actual query response time, precision and resource utilisation, suggests that the proposed framework is capable of predicting performance at different scales in a precise, accurate and efficient manner. The results can assist ElasticSearch users to manage their scalability and infrastructure requirements.

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.