TIO: A VLC-Enabled Hybrid Data Center Network Architecture

To satisfy the ever-increasing bandwidth demand of modern data centers, researchers have proposed hybrid Data Center Networks(DCNs), which employ high-bandwidth Optical Circuit Switches(OCSs) to compensate for Electrical Packet Switches(EPS). Existing designs, such as Helios and c-Through, mainly focus on reconfiguring optical devices to meet the estimated traffic requirements. However, these designs face two major challenges in their OCS-based networks, namely, the complex control mechanism and cabling problems. To solve these challenges, we propose TIO, a hybrid DCN that employs Visible Light Communication(VLC) instead of wired OCS design to connect racks. TIO integrates the wireless VLC-based Jellyfish and wired EPS-based Fat Tree seamlessly and combines the opposite and complementary characteristics, including wireless VLC direct connection and wired electrical packet switching, random graph, and Clos topology properties. To further exploit the merits of TIO, we design a hybrid routing scheme and congestion-aware flow scheduling method. Comprehensive evaluations indicate that TIO outperforms the Jellyfish and Fat Tree in both topology properties and network performance, and the flow scheduling method also evidently improves performance.

Minimum-Cost Forest for Uncertain Multicast with Delay Constraints

The use of multicast transmission can efficiently reduce the consumption of network resources by jointly serving multiple destinations with a single source node. Currently, many multicast applications impose the constraint wherein multicast flows must be processed by a series of Virtual Network Functions(VNFs) before reaching their destinations. Given a multicast transmission, there are usually multiple server nodes, each of which is able to host all the required VNFs. Thus, the multicast flow should be initially steered to one or a few selected server nodes that act as pseudo sources, and the destinations will then retrieve new flow from any of these pseudo sources.In this paper, we model this kind of multicast as an uncertain multicast with multiple pseudo sources, whose routing structure is usually a forest consisting of multiple isolated trees. We then characterize and construct the Delay-guaranteed Minimum Cost Forest(D-MCF) such that each path from the source to the destination satisfies the end-to-end delay constraint. To tackle this NP-hard problem, we design two efficient methods, the Partition Algorithm(PA) and the Combination Algorithm(CA), to approximate the optimal solution. Theoretical analyses and evaluations indicate that these two methods can generate the desired routing forest for any multicast transfer.Moreover, the PA method achieves a better balance between performance and time consumption than the CA method. The evaluation results show that PA-(Ω+20) can reduce total cost by 49:02% while consuming 12:59% more time, thus significantly outperforming the CA-(Ω+20) method.

Uncertain multicast under dynamic behaviors

Multicast transfer can efficiently save the bandwidth consumption and reduce the load on the source node than a series of independent unicast transfers.Nowadays,many applications employ the content replica strategy to improve the robustness and efficiency;hence each file and its replicas are usually distributed among multiple sources.In such scenarios,the traditional deterministic multicast develops into the Uncertain multicast,which has more flexibility in the source selection.In this paper,we focus on building and maintaining a minimal cost forest(MCF)for any uncertain multicast,whose group members(source nodes and destination nodes)may join or leave after constructing a MCF.We formulate this dynamic minimal cost forest(DMCF)problem as a mixed integer programming model.We then design three dedicated methods to approximate the optimal solution.Among them,our a-MCF aims to efficiently construct an MCF for any given uncertain multicast,without dynamic behaviors of multicast group members.The d-MCF method motivates to slightly update the existing MCF via local modifications once appearing a dynamic behavior.It can achieve the balance between the minimal cost and the minimal modifications to the existing forest.The last r-MCF is a supplement method to the d-MCF method,since many rounds of local modifications maymake the resultant forest far away from the optimal forest.Accordingly,our r-MCF method monitors the accumulated degradation and triggers the rearrangement process to reconstruct an new MCF when necessary.The comprehensive evaluation results demonstrate that our methods can well tackle the proposed DMCF problem.

Comparing Set Reconciliation Methods Based on Bloom Filters and Their Variants

Set reconciliation between two nodes is widely used in network applications. The basic idea is that each member of a node pair has an object set and seeks to deliver its unique objects to the other member. The Standard Bloom Filter （SBF） and its variants, such as the Invertible Bloom Filter （IBF）, are effective approaches to solving the set reconciliation problem. The SBF-based method requires each node to represent its objects using an SBF, which is exchanged with the other node. A receiving node queries the received SBF against its local objects to identify the unique objects. Finally, each node exchanges its unique objects with the other node in the node pair. For the IBF- based method, each node represents its objects using an IBF, which is then exchanged. A receiving node subtracts the received IBF from its local IBF so as to decode the different objects between the two sets. Intuitively, it would seem that the IBF-based method, with only one round of communication, entails less communication overhead than the SBF-based method, which incurs two rounds of communication. Our research results, however, indicate that neither of these two methods has an absolute advantages over the others. In this paper, we aim to provide an in-depth understanding of the two methods, by evaluating and comparing their communication overhead. We find that the best method depends on parameter settings. We demonstrate that the SBF-based method outperforms the IBF-based method in most cases. But when the number of different objects in the two sets is below a certain threshold, the IBF-based method outperforms the SBF-based method.