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Network Testing and Analytics Series

Series Topic


The objective of the Network Testing and Analytics Series of IEEE Communications Magazine is to provide a forum across the academia and the industry to address the design and implementation defects unveiled by network testing and to survey analytics solutions that can help improve infrastructure and operations. In the industry, testing has been used to evaluate the design and implementation of a system. But in the academia, a more common practice is to evaluate a design by mathematical analysis or simulation without actual implementations. A less common practice is to evaluate a design by testing a partial implementation. That is, the academia focuses more deeply on algorithmic design evaluation while the industry has broader concerns on both algorithmic design issues and system implementation issues. Often, an optimized algorithmic component could not guarantee the optimal operation of the whole system when other components throttle the overall performance.

Network analytics complement testing and modelling activities during design, as well as during implementation and subsequent production and operation phases of a network or system. To ensure that network and system solutions deliver their required service quality, a range of testing and analytics capabilities and practices are required to model, measure and evaluate the effectiveness of the solutions over their lifecycles.

This series serves as a forum to bridge the gap on network testing and analytics activities, where the design or implementation defects and outcomes found by either community could be referred by another community. The defects could be found in various dimensions of testing. The type of testing could be functionality, performance, conformance, interoperability and stability of the systems under test (SUT) in the lab or in the field. The SUT could be black-box without source code or binary code, grey-box with binary code or interface, or white-box with source code. For grey-box or white-box testing, profiling would help to identify and diagnose system bottlenecks. For black-box testing, benchmarking devices of the same class could reflect the state of the art. The SUT could range from link-layer systems such as Ethernet, WLAN, WiMAX, 3G/4G cellular, and xDSL, to mid-layer switches and routers, upper-layer systems such as VoIP, SIP signaling, multimedia, network security, and consumer devices such as handhelds.

With the increasing complexity of networks and systems - further fueled by the adoption of software-defined networking and network functions virtualization – management, operations and troubleshooting is expected to become more complex. Predictive network analytics has an increasingly important role to play in helping identify and resolve issues before they impact operational performance. Measurement of network traffic and customer experience for the purpose of providing a consistent and predictable level of services has been the norm in network engineering although there are ongoing and increasing challenges related to scale and complexity.

In summary, the Network Testing and Analytics Series solicits articles falling in, but not limited to, the following topics:

  • Testing functionality, performance, conformance, interoperability, and stability
  • Testing systems and services of 10G Ethernet, Power over Ethernet, WLAN, WiMAX, 3G/4G/5G cellular, xDSL, switches, routers, IPv6, VoIP, SIP signaling, storage area networks, network security, consumer handhelds, and software defined networking (SDN)
  • Testing various layers of network devices including black-boxes, white-boxes, and grey-boxes
  • Benchmarking and profiling network systems and services
  • Network lab testing and field testing
  • Designing network test methodologies, test tools, and test beds
  • Evaluating false positive and negative of network security
  • Analyzing lab-found and customer-found defects
  • Network analytics infrastructure
  • Custom analytics solutions for planning, traffic management and overall improvement of network infrastructure and operations
  • Network performance monitoring and diagnostics
  • Predictive network analytics to help detect network and application issues
  • Analytic tools for troubleshooting and identifying root causes


Ying-Dar Lin
National Chiao Tung University - Network Benchmarking Lab (NCTU-NBL)

Erica Johnson
University of New Hampshire - InterOperability Lab (UNH-IOL)

Irena Atov