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Abstract-Modern data centres provide large aggregate network capacity and multiple paths among servers. Traffic is very diverse; most of the data is produced by long, bandwidth hungry flows but the large majority of flows, which commonly come with strict deadlines regarding their completion time, are short. It has been shown that TCP is not efficient for any of these types of traffic in modern data centres. More recent protocols such MultiPath TCP (MPTCP) are very efficient for long flows, but are ill-suited for short flows.In this paper, we present AMPTCP, a novel transport protocol which, compared to TCP and MPTCP, reduces short flows' completion times, while providing excellent goodput to long flows. To do so, AMPTCP runs in two phases; initially, it randomly scatters packets in the network under a single congestion window exploiting all available paths. This is beneficial to latency-sensitive flows. After a specific amount of data is sent, AMPTCP switches to a regular MultiPath TCP mode. AMPTCP is incrementally deployable in existing data centres as it does not require any modifications outside the transport layer and behaves well when competing with legacy TCP and MPTCP flows. Our extensive experimental evaluation in simulated FatTree topologies shows that all design objectives for AMPTCP are met.

Abstract-Understanding which node failures in a network have more impact is an important problem. Current understanding, motivated by the scale free models of network growth, places emphasis on the degree of the node. This is not a satisfactory measure; the number of connections a node has does not capture how redundantly it is connected into the whole network. Conversely, the structural entropy of a graph captures the resilience of a network well, but is expensive to compute, and, being a global measure, does not attribute any specific value to a given node. This lack of locality prevents the use of global measures as a way of identifying critical nodes. In this paper we introduce local vertex measures of entropy which do not suffer from such drawbacks. In our theoretical analysis we establish the possibility that our local vertex measures approximate global entropy, with the advantage of locality and ease of computation. We establish properties that vertex entropy must have in order to be useful for identifying critical nodes. We have access to a proprietary event, topology and incident dataset from a large commercial network. Using this dataset, we demonstrate a strong correlation between vertex entropy and incident generation over events.

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Abstract-With the proliferation of smartphones and their advanced connectivity capabilities, opportunistic networks have gained a lot of traction during the past years; they are suitable for increasing network capacity and sharing ephemeral, localised content. They can also offload traffic from cellular networks to device-to-device ones, when cellular networks are heavily stressed. Opportunistic networks can play a crucial role in communication scenarios where the network infrastructure is inaccessible due to natural disasters, large-scale terrorist attacks or government censorship. Geocasting, where messages are destined to specific locations (casts) instead of explicitly identified devices, has a large potential in real world opportunistic networks, however it has attracted little attention in the context of opportunistic networking.In this paper we propose Geocasting Spray And Flood (GSAF), a simple but efficient and flexible geocasting protocol for opportunistic, delay-tolerant networks. GSAF follows a simple but elegant and flexible approach where messages take random walks towards the destination cast. Messages that follow directions away from the cast are extinct when the device buffer gets full, freeing space for new messages to be delivered. In GSAF, casts do not have to be pre-defined; instead users can route messages to arbitrarily defined casts. Our extensive evaluation shows that GSAF is efficient, in terms of message delivery ratio and latency as well as network overhead.