Conference Paper

LIFO-Backpressure achieves near optimal utility-delay tradeoff

Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA
DOI: 10.1109/WIOPT.2011.5930067 Conference: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), 2011 International Symposium on
Source: IEEE Xplore


There has been considerable recent work developing a new stochastic network utility maximization framework using Backpressure algorithms, also known as MaxWeight. A key open problem has been the development of utility-optimal algorithms that are also delay efficient. In this paper, we show that the Backpressure algorithm, when combined with the LIFO queueing discipline (called LIFO-Backpressure), is able to achieve a utility that is within O(1/V) of the optimal value for any scalar V ≥ 1, while maintaining an average delay of O([log(V)]2) for all but a tiny fraction of the network traffic. This result holds for general stochastic network optimization problems and general Markovian dynamics. Remarkably, the performance of LIFO-Backpressure can be achieved by simply changing the queueing discipline; it requires no other modifications of the original Backpressure algorithm. We validate the results through empirical measurements from a sensor network testbed, which show good match between theory and practice.

13 Reads
  • Source
    • "Most prominently, authors in [18] restrict the node selection step (9) of SBPR only within a subset of links that offer a bounded maximum number of hops towards the target. Other studies have shown that simply altering the queueing discipline from FIFO to LIFO yields considerable latency gains [19]. Finally, it is worth noting that SBPR can be easily made TCP compatible [20]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Software-Defined Networking enables the centralized orchestration of data traffic within a network. However, proposed solutions require a high degree of architectural penetration. The present study targets the orchestration of network elements that do not wish to yield much of their internal operations to an external controller. Backpressure routing principles are used for deriving flow routing rules that optimally stabilize a network, while maximizing its throughput. The elements can then accept in full, partially or reject the proposed routing rule-set. The proposed scheme requires minimal, relatively infrequent interaction with a controller, limiting its imposed workload, promoting scalability. The proposed scheme exhibits attracting network performance gains, as demonstrated by extensive simulations and proven via mathematical analysis.
    IEEE ISCC, Larnaka, Cyprus; 07/2015
  • Source
    • "V * N ) be a vector of Lagrange multipliers , which solves the dual problem (9) with parameter V . The following theorem from [9] describes a steady state property of the drift-plus-penalty algorithm: "
    [Show abstract] [Hide abstract]
    ABSTRACT: One practical open problem is the development of a distributed algorithm that achieves near-optimal utility using only a finite (and small) buffer size for queues in a stochastic network. This paper studies utility maximization (or cost minimization) in a finite-buffer regime and considers the corresponding delay and reliability (or rate of packet drops) tradeoff. A floating-queue algorithm allows the stochastic network optimization framework to be implemented with finite buffers at the cost of packet drops. Further, the buffer size requirement is significantly smaller than previous works in this area. With a finite buffer size of $B$ packets, the proposed algorithm achieves within $O(e^{-B})$ of the optimal utility while maintaining average per-hop delay of $O(B)$ and an average per-hop drop rate of $O(e^{-B})$ in steady state. From an implementation perspective, the floating-queue algorithm requires little modification of the well-known Drift-Plus-Penalty policy (including MaxWeight and Backpressure policies). As a result, the floating-queue algorithm inherits the distributed and low complexity nature of these policies.
  • Source
    • "However, under LIFO, packet 1 and packet 2 are in the queue forever. Thus the rest of the queue is equivalent to a queue with threshold range [0] [4]. A snapshot of simulation (Fig. 3) illustrates the threshold effect of ETX on the queue length. "

    IEEE Transactions on Control of Network Systems 01/2015; DOI:10.1109/TCNS.2015.2428453
Show more


13 Reads
Available from