Dynamic Placement of Distributed Analytics Services

Military / Coalition Issue

The dynamic coalition environments require the distributed analytics services be dynamically composed, deployed, and executed utilizing available (often limited) computation, storage, and communication resources, while satisfying the constraints imposed by coalition policies. To achieve the desired level of performance and reliability of the distributed analytics services, the analytics tasks and data objects must be flexibly placed on top of the resources whose availability fluctuate over time.

Core idea and key achievements

We have developed a set of algorithms to determine where to place analytics services and what services should be placed, in dynamically changing environments. Our algorithms have the following key characteristics:

  • efficient execution with bounded time and space complexity;
  • provable optimality guarantee in worst case scenarios;
  • adaptable to system and user dynamics.

These results were obtained using analytical techniques from the fields of online learning, approximation algorithms, and optimization. Such guaranteed worst-case performance is very useful for ensuring expected system behaviour even in highly dynamic settings.

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Implications for Defence

Current tactical analytics approaches use centrally located services requiring significant amounts of computing and networking resources. In the future, distributed analytics can significantly enhance coalition operations at the tactical edge, providing situation awareness for a variety of applications (e.g., ISR, C2). The techniques that we have developed will support agile analytics to soldiers in the field by dynamically placing services at suitable locations. Our algorithms have theoretical worst-case guarantees which ensure robustness in challenging environments.

Readiness & alternative Defence uses

A set of algorithms are described in published papers and many of them are also available as source code.

Resources and references


UCL, PSU, IBM US, BBN, Southampton, Yale, ARL