Abstract
Long flows contribute huge volumes of traffic over inter-datacenter WAN. The Flow Completion Time (FCT) is a vital network performance metric that affects the running time of distributed applications and the users' quality of experience. Flow routing techniques based on propagation or queuing latency or instantaneous link utilization are insufficient for minimization of the long flows' FCT. We propose a routing approach that uses the remaining sizes and paths of all ongoing flows to minimize the worst-case completion time of incoming flows assuming no knowledge of future flow arrivals. Our approach can be formulated as an NP-Hard graph optimization problem. We propose BWRH, a heuristic to quickly generate an approximate solution. We evaluate BWRH against several real WAN topologies and two different traffic patterns. We see that BWRH provides solutions with an average optimality gap of less than $0.25\%$. Furthermore, we show that compared to other popular routing heuristics, BWRH reduces the mean and tail FCT by up to $1.46\times$ and $1.53\times$, respectively.
Highlights
Dedicated inter-datacenter networks have been used by multiple organizations to connect dozens of their datacenters such as Google’s B4 [1], [2], Facebook’s Express Backbone [3], and Microsoft’s Global WAN [4]
We show that BWR HEURISTIC (BWRH) improves the mean and tail completion times by up to 1.46× and 1.53×, respectively, given various flow size distributions and scheduling policies
We considered the scheduling policies of First Come First Serve (FCFS), Shortest Remaining Processing Time (SRPT) and Fair Sharing based on max-min fairness [10]
Summary
Dedicated inter-datacenter networks have been used by multiple organizations to connect dozens of their datacenters such as Google’s B4 [1], [2], Facebook’s Express Backbone [3], and Microsoft’s Global WAN [4]. Over inter-datacenter WAN where end-points are managed by the organization that controls the routing [1], [3], [4], one can use routing techniques that differentiate long flows from short flows and use flow properties obtained from applications, including flow size information, to reduce the completion times of long flows In this context, our paper makes the following contributions:. Assuming no knowledge of future flow arrivals and no constraints on the network traffic scheduling policy, we propose to minimize the worst-case completion time of every incoming flow given the network topology, the currently ongoing flows’ paths, and their remaining number of data units. We show that over multiple topologies and with different traffic patterns, BWRH’s optimality gap is, on average, below 0.25%
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