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This survey article reviews the challenges associated with deploying andoptimizing big data applications and machine learning algorithms in cloud datacenters and networks. The MapReduce programming model and its widely-usedopen-source platform; Hadoop, are enabling the development of a large number ofcloud-based services and big data applications. MapReduce and Hadoop thusintroduce innovative, efficient, and accelerated intensive computations andanalytics. These services usually utilize commodity clusters withingeographically-distributed data centers and provide cost-effective and elasticsolutions. However, the increasing traffic between and within the data centersthat migrate, store, and process big data, is becoming a bottleneck that callsfor enhanced infrastructures capable of reducing the congestion and powerconsumption. Moreover, enterprises with multiple tenants requesting various bigdata services are challenged by the need to optimize leasing their resources atreduced running costs and power consumption while avoiding under or overutilization. In this survey, we present a summary of the characteristics ofvarious big data programming models and applications and provide a review ofcloud computing infrastructures, and related technologies such asvirtualization, and software-defined networking that increasingly support bigdata systems. Moreover, we provide a brief review of data centers topologies,routing protocols, and traffic characteristics, and emphasize the implicationsof big data on such cloud data centers and their supporting networks. Wideranging efforts were devoted to optimize systems that handle big data in termsof various applications performance metrics and/or infrastructure energyefficiency. Finally, some insights and future research directions are provided.

A Survey of Big Data Machine Learning Applications Optimization in Cloud Data Centers and Networks