A Partial Store-and-Forward Scheduling Method for Inter-Datacenter Bulk Data Transfers

Abstract

Temporarily storing delay-tolerant data at peak hours and forwarding the data at offpeak hours, i.e., performing Store-and-Forward (SnF) using datacenter storage, can mitigate peak-hour congestion and exploit off-peak-hour bandwidth in inter-datacenter networks. Most prior studies considered a case where all nodes along their routing paths provided SnF options for the scheduling decision making. Intuitively, their solutions maximize the scheduling flexibility. However, the computational complexity of their solutions increases exponentially with the hop count. Meanwhile, SnF operations are generally performed on a portion of nodes rather than every node along the path. Thus, the considered case seems to be unnecessary in practice. In this paper, our studies reveal that desirable performance can be attained by involving a portion of nodes rather than all nodes along the path in the decision making. Thus, we propose a partial SnF scheduling method, which involves a portion of nodes in scheduling and introduces a network abstraction based on the involved nodes. Simulations demonstrate that the proposed method has lower complexity while achieving high performance.

Modeling inter-DC bulk data transfer in optical networks with edge storage

“…As an alternati ve to bandwidth expansion, putting off the delivery of delay-insensitive bulk transfer requests to off-peak hours by storing them at edge of the networks is a cost-effective way to reduce bandwidth contention during peak-hours and improve overall network utilization, particularly when bandwidth demand is unevenly distributed in space and time [7], [8]. However, introducing storage into the transfer process adds an additional dimension of complexity to the problem [9], [10], making performance evaluation more difficult. While a considerable amount of interesting research work has been conducted experimentally, or algorithmically, our understanding of the performance gain that storage may bring to data transfer across large geographic areas lags far behind.…”