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Network traffic workloads are widely utilized in applied research to verify correctness and to measure the impact of novel algorithms, protocols, and network functions. We provide a comprehensive survey of traffic generators referenced by researchers over the last 13 years, providing in-depth classification of the functional behaviors of the most frequently cited generators. These classifications are then used as a critical component of a methodology presented to aid in the selection of generators derived from the workload requirements of future research. 

Distributed networked systems form an essential resource for computation and applications ranging from commercial, military, scientific, and research communities. Allocation of resources on a given infrastructure is realized through various mapping systems that are tailored towards specific use cases of the requesting applications. While HPC system requests demand compute resources heavy on processor and memory, cloud applications may demand distributed web services that are composed of networked processing and some memory. All resource requests allocate on the infrastructure with some form of network connectivity. However, during mapping of resources, the features and topology constraints of network components are typically handled indirectly through abstractions of user requests. This paper is on a novel graph representation that enables precise mapping methods for distributed networked systems. The proposed graph representations are demonstrated to allocate specific network components and adjacency requirements of a requested graph on a given infrastructure. Furthermore, we report on application of business policy requirements that resulted in increased utilization and a gradual decrease in idle node count as requests are mapped using our proposed methods. 

Network traffic modeling plays an important role in the generation of realistic network traffic in test environments. Especially in cases where researchers carry out experiments with real production-like traffic, as seen in specific home, enterprise, campus, LAN, or WAN networks. We present our ongoing work on a new framework that enables the methodical creation of application-agnostic traffic models from given network traces of a known network topology. The framework then uses these models to generate realistic traffic on a given network topology. We share a preliminary evaluation of the framework based on repeatable experiments where we model a typical web application traffic and then regenerate the traffic based on the model in a test network on our VTS (Virtual Topology Services) testbed.