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Optimum transmission delay for function computation in NFV-based networks: the role of network coding and redundant computing

Tahmasebi, B ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/JSAC.2018.2869952
  3. Abstract:
  4. In this paper, we study the problem of delay minimization in NFV-based networks. In such systems, the ultimate goal of any request is to compute a sequence of functions in the network, where each function can be computed at only a specific subset of network nodes. In conventional approaches, for each function, we choose one node from the corresponding subset of the nodes to compute that function. In contrast, in this work, we allow each function to be computed in more than one node, redundantly in parallel, to respond to a given request. We argue that such redundancy in computation not only improves the reliability of the network, but would also, perhaps surprisingly, reduce the overall transmission delay. In particular, we establish that by judiciously choosing the subset of nodes which compute each function, in conjunction with a linear network coding scheme to deliver the result of each computation, we can characterize and achieve the optimal endto- end transmission delay. In addition, we show that using such technique, it is possible to significantly reduce the transmission delay as compared to the conventional approaches. In fact, in some scenarios, such reduction can even scale with the size of the network, where by increasing the number of nodes that can compute the given function in parallel by a multiplicative factor, the end-to-end delay will also decrease by the same factor. Moreover, we show that while finding the subset of nodes for each computation, in general, is a complex integer program, approximation algorithms can be proposed to reduce the computational complexity. In fact, for the case where the number of computing nodes for a given function is upperbounded by a constant, a dynamic programming scheme can be proposed to find the optimum subsets in polynomial times. Our numerical simulations confirm the achieved gain in performance in comparison with conventional approaches. IEEE
  5. Keywords:
  6. Delay-computation trade-off ; Encoding ; network function virtualization (NFV) ; Approximation algorithms ; Codes (symbols) ; Complex networks ; Dynamic programming ; Economic and social effects ; Encoding (symbols) ; Integer programming ; Linear networks ; Network coding ; Optimization ; Polynomial approximation ; Redundancy ; Reliability ; Set theory ; Transfer functions ; Virtual reality ; Conventional approach ; Delays ; End-to-end transmission ; Function computations ; Multiplicative factors ; Network optimization ; Resource management ; Trade off ; Network function virtualization
  7. Source: IEEE Journal on Selected Areas in Communications ; 2018 ; 07338716 (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/8463556