A perennial question in computer networks is where to place functionality among components of a distributed computer system. In data centers, one option is to move all intelligence to the edge, essentially relegating switches and middleboxes, regardless of their programmability, to simple static routing policies. Another is to add more intelligence to the middle of the network in the hopes that it can handle any issue that arises. This paper presents an architecture, called Volur, that provides a third option by facilitating the co-existence of an intelligent network with an intelligent edge. The key architectural principle of Volur is predictability of the network. We describe the key design requirements, and show through case studies how our approach facilitates more democratic innovation of all parts of the network. We also demonstrate the practicality of our architecture by describing how to implement the architecture on top of existing hardware and by deploying a prototype on top of a large production data center.
Network and end host coordination. The idea of an intelligent network assisting intelligent hosts has been explored in other areas. For example, ECN-based transport @cite_22 @cite_28 provides end hosts with information about the utilization of the network.
@cite_28 @cite_22
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The evolution of the Internet into a distributed Information access system calls for a paradigm shift to enable an evolvable future network architecture. Information-Centric Networking (ICN) proposals rethink the communication model around named data, in contrast with the host-centric transport view of TCP IP. Information retrieval is natively pull-based, driven by user requests, point-to-multipoint and intrinsically coupled with in-network caching. In this paper, we tackle the problem of joint multipath congestion control and request forwarding in ICN for the first time. We formulate it as a global optimization problem with the twofold objective of maximizing user throughput and minimizing overall network cost. We solve it via decomposition and derive a family of optimal congestion control strategies at the receiver and of distributed algorithms for dynamic request forwarding at network nodes. An experimental evaluation of our proposal is carried out in different network scenarios to assess the performance of our design and to highlight the benefits of an ICN approach. Data communication across the Internet has significantly changed under the pressure of massive content delivery. Content-Centric Networking (CCN) rethinks Internet communication paradigm around named data retrieval, in contrast with the host-to-host transport model of TCP IP. Content retrieval is natively pull-based driven by user requests, point-to-multipoint and intrinsically coupled with the availability of network storage. By leveraging the key features of CCN transport, in this paper we propose for the first time a congestion control mechanism realizing efficient multipath communication over content-centric networks. Our proposal is based on a Remote Adaptive Active Queue Management (RAAQM) at the receiver that performs a per-route control of bottleneck queues along the paths. We analyze the stability of the proposed solution and assess its performance by means of CCN packet-level simulations under random and optimal route selection. Datacenter networks deal with a variety of workloads, ranging from latency-sensitive small flows to bandwidth-hungry large flows. Load balancing schemes based on flow hashing, e.g., ECMP, cause congestion when hash collisions occur and can perform poorly in asymmetric topologies. Recent proposals to load balance the network require centralized traffic engineering, multipath-aware transport, or expensive specialized hardware. We propose a mechanism that avoids these limitations by (i) pushing load-balancing functionality into the soft network edge (e.g., virtual switches) such that no changes are required in the transport layer, customer VMs, or networking hardware, and (ii) load balancing on fine-grained, near-uniform units of data (flowcells) that fit within end-host segment offload optimizations used to support fast networking speeds. We design and implement such a soft-edge load balancing scheme, called Presto, and evaluate it on a 10 Gbps physical testbed. We demonstrate the computational impact of packet reordering on receivers and propose a mechanism to handle reordering in the TCP receive offload functionality. Presto's performance closely tracks that of a single, non-blocking switch over many workloads and is adaptive to failures and topology asymmetry. A number of large-scale distributed Internet applications could potentially benefit from some level of knowledge about the relative proximity between its participating host nodes. For example, the performance of large overlay networks could be improved if the application-level connectivity between the nodes in these networks is congruent with the underlying IP-level topology. Similarly, in the case of replicated Web content, client nodes could use topological information in selecting one of multiple available servers. For such applications, one need not find the optimal solution in order to achieve significant practical benefits. Thus, these applications, and presumably others like them, do not require exact topological information and can instead use sufficiently informative hints about the relative positions of Internet hosts. In this paper, we present a binning scheme whereby nodes partition themselves into bins such that nodes that fall within a given bin are relatively close to one another in terms of network latency. Our binning strategy is simple (requiring minimal support from any measurement infrastructure), scalable (requiring no form of global knowledge, each node only needs knowledge of a small number of well-known landmark nodes) and completely distributed (requiring no communication or cooperation between the nodes being binned). We apply this binning strategy to the two applications mentioned above: overlay network construction and server selection. We test our binning strategy and its application using simulation and Internet measurement traces. Our results indicate that the performance of these applications can be significantly improved by even the rather coarse-grained knowledge of topology offered by our binning scheme.