We study the scheduling of flows on a switch with the goal of optimizing metrics related to the response time of the flows. The input to the problem is a sequence of flow requests on a switch, where the switch is represented by a bipartite graph with a capacity on each vertex (or port), and a flow request is an edge with associated demand. In each round, a subset of edges can be scheduled subject to the constraint that the total demand of the scheduled edges incident on any vertex is at most the capacity of the vertex. Previous work has essentially settled the complexity of metrics based on {\em completion time}. The objective of average or maximum {\em response time}, however, is much more challenging. To the best of our knowledge, there are no prior approximation algorithms results for these metrics in the context of flow scheduling. We present the first approximation algorithms for flow scheduling over a switch to optimize response time based metrics. For the average response time metric, whose NP-hardness follows directly from past work, we present an offline O(1 + O(\log(n))/c) approximation algorithm for unit flows, assuming that the port capacities of the switch can be increased by a factor of 1 + c, for any given positive integer c. For the maximum response time metric, we first establish that it is NP-hard to achieve an approximation factor of better than 4/3 without augmenting capacity. We then present an offline algorithm that achieves {\em optimal maximum response time}, assuming the capacity of each port is increased by at most 2 d_{max} - 1, where d_{max} is the maximum demand of any flow. Both algorithms are based on linear programming relaxations. We also study the online version of flow scheduling using the lens of competitive analysis, and present preliminary results along with experiments that evaluate the performance of fast online heuristics.
We study the scheduling of flows on a switch with the goal of optimizing metrics related to the response time of the flows. The input to the problem is a sequence of flow requests on a switch, where the switch is represented by a bipartite graph with a capacity on each vertex (or port), and a flow request is an edge with associated demand. In each round, a subset of edges can be scheduled subject to the constraint that the total demand of the scheduled edges incident on any vertex is at most the capacity of the vertex. Previous work has essentially settled the complexity of metrics based on {\em completion time}. The objective of average or maximum {\em response time}, however, is much more challenging. We present approximation algorithms for flow scheduling over a switch to optimize response time based metrics. For the average response time metric, whose NP-hardness follows directly from past work, we present an offline O(1 + O(\log(n))/c) approximation algorithm for unit flows, assuming that the port capacities of the switch can be increased by a factor of 1 + c, for any given positive integer c. For the maximum response time metric, we first establish that it is NP-hard to achieve an approximation factor of better than 4/3 without augmenting capacity. We then present an offline algorithm that achieves {\em optimal maximum response time}, assuming the capacity of each port is increased by at most 2 d_{max} - 1, where d_{max} is the maximum demand of any flow. Both algorithms are based on linear programming relaxations. We also study the online version of flow scheduling using the lens of competitive analysis, and present preliminary results along with experiments that evaluate the performance of fast online heuristics.
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To the best of our knowledge, there are no prior approximation algorithms results for these metrics in the context of flow scheduling. We present the first
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