DCCast: Efficient Point to Multipoint Transfers Across Datacenters

Using multiple datacenters allows for higher availability, load balancing and reduced latency to customers of cloud services. To distribute multiple copies of data, cloud providers depend on inter-datacenter WANs that ought to be used efficiently considering their limited capacity and the ever-increasing data demands. In this paper, we focus on applications that transfer objects from one datacenter to several datacenters over dedicated inter-datacenter networks. We present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses forwarding trees to efficiently deliver an object from a source datacenter to required destination datacenters. With low computational overhead, DCCast selects forwarding trees that minimize bandwidth usage and balance load across all links. With simulation experiments on Google's GScale network, we show that DCCast can reduce total bandwidth usage and tail Transfer Completion Times (TCT) by up to $50\%$ compared to delivering the same objects via independent point-to-point (P2P) transfers.Comment: 9th USENIX Workshop on Hot Topics in Cloud Computing, https://www.usenix.org/conference/hotcloud17/program/presentation/noormohammadpou

QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts

Large inter-datacenter transfers are crucial for cloud service efficiency and are increasingly used by organizations that have dedicated wide area networks between datacenters. A recent work uses multicast forwarding trees to reduce the bandwidth needs and improve completion times of point-to-multipoint transfers. Using a single forwarding tree per transfer, however, leads to poor performance because the slowest receiver dictates the completion time for all receivers. Using multiple forwarding trees per transfer alleviates this concern--the average receiver could finish early; however, if done naively, bandwidth usage would also increase and it is apriori unclear how best to partition receivers, how to construct the multiple trees and how to determine the rate and schedule of flows on these trees. This paper presents QuickCast, a first solution to these problems. Using simulations on real-world network topologies, we see that QuickCast can speed up the average receiver's completion time by as much as $10\times$ while only using $1.04\times$ more bandwidth; further, the completion time for all receivers also improves by as much as $1.6\times$ faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018, Honolulu, H

Vehicular Carriers for Big Data Transfers

International audienceIn the latest years, Internet traffic has increased at a significantly faster pace than its capacity, preventing efficient bulk data transfers such as datacenter migrations and high-definition user-generated multimedia data. In this paper, we propose to take advantage of the existing worldwide road infrastructure as an offloading channel to help the legacy Internet assuage its burden. One of the motivations behind our work is that a significant share of the Internet traffic is elastic and tolerates a certain delay before consumption. Our results suggest that piggybacking data on vehicles can easily lead to network capacity in the petabyte range. Furthermore, such a strategy exceeds by far the performance of today's alternatives that, although yielding good performance levels, still rely on the legacy Internet and inherent then its intrinsic limitations. We show through a number of analyses that our proposal has the potential to obtain remarkable reductions in transfer delays while being economically affordable

Time-Dimensional Traffic Engineering with Storage Aware Routing

Because of the popularity of rich content, such as video files, the amount of traffic on the Internet continues to grow every year. Not only is the overall traffic increasing, but also the temporal fluctuations in traffic are increasing, and differences in the amounts of traffic between peak and off-peak periods are becoming very large. Consequently, efficient use of link bandwidth is becoming more challenging. In this paper, we propose a new system for content distribution: storage aware routing (SAR). With SAR, routers having large storage capacities can exploit those links that are underutilized. Our performance evaluations show that SAR can smooth the fluctuations in link utilization

On the dynamics of valley times and its application to bulk-transfer scheduling

Periods of low load have been used for the scheduling of non-interactive tasks since the early stages of computing. Nowadays, the scheduling of bulk transfers—i.e., large-volume transfers without precise timing, such as database distribution, resources replication or backups—stands out among such tasks, given its direct effect on both the performance and billing of networks. Through visual inspection of traffic-demand curves of diverse points of presence (PoP), either a network, link, Internet service provider or Internet exchange point, it becomes apparent that low-use periods of bandwidth demands occur at early morning, showing a noticeable convex shape. Such observation led us to study and model the time when such demands reach their minimum, on what we have named valley time of a PoP, as an approximation to the ideal moment to carry out bulk transfers. After studying and modeling single-PoP scenarios both temporally and spatially seeking homogeneity in the phenomenon, as well as its extension to multi-PoP scenarios or paths—a meta-PoP constructed as the aggregation of several single PoPs—, we propose a final predictor system for the valley time. This tool works as an oracle for scheduling bulk transfers, with different versions according to time scales and the desired trade-off between precision and complexity. The evaluation of the system, named VTP, has proven its usefulness with errors below an hour on estimating the occurrence of valley times, as well as errors around 10% in terms of bandwidth between the prediction and actual valley trafficThis work has been partially supported by the European Commission under the project H2020 METRO-HAUL (Project ID: 761727

Cost-Efficient Data Backup for Data Center Networks against {\epsilon}-Time Early Warning Disaster

Data backup in data center networks (DCNs) is critical to minimize the data loss under disaster. This paper considers the cost-efficient data backup for DCNs against a disaster with $\varepsilon$ early warning time. Given geo-distributed DCNs and such a $\varepsilon$-time early warning disaster, we investigate the issue of how to back up the data in DCN nodes under risk to other safe DCN nodes within the $\varepsilon$ early warning time constraint, which is significant because it is an emergency data protection scheme against a predictable disaster and also help DCN operators to build a complete backup scheme, i.e., regular backup and emergency backup. Specifically, an Integer Linear Program (ILP)-based theoretical framework is proposed to identify the optimal selections of backup DCN nodes and data transmission paths, such that the overall data backup cost is minimized. Extensive numerical results are also provided to illustrate the proposed framework for DCN data backup