Traffic Optimization in Data Center and Software-Defined Programmable Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Low-complexity Flow Scheduling for Commodity Switches in Data Center Networks

Recently proposed approaches to minimize the Flow Completion Time (FCT) in data centers do not require any apriory information about the flow size, thus appearing to be both practical and efficient. These solutions are based on a system of multiple priority queues (PQs) at both the servers and theswitches and they may require to solve a complex algorithm to optimally split the traffic across the different PQs. However, the actual availability of priority queues at the switches is typically limited, thus restricting the applicability of these approaches. In this paper, we propose a novel approach, named NOS2, which requires only 2 PQs at the switches while maintaining multiple PQs at the servers, and leverages a central controller that optimally coordinates the traffic split among the different priority levels. We show by simulation that NOS2 is able to achieve performance close to state-of-art solutions with significantly smaller implementation complexity. Thus, NOS2 is expected to provide a better trade-off between performance and implementation complexity

A Traffic-Aware Perspective on Network Disaggregated Sketches

Sketches have emerged as a powerful tool for network traffic monitoring due to the good trade-off between accuracy and memory footprint offered by such techniques. Yet, implementing sketches on commercial switches raises numerous challenges related to availability of memory and its access frequency. Recently, disaggregated sketches, i.e., fragments of single network-wide sketches distributed across multiple switches, were introduced to cope with these limitations. However, none of the current approaches exploit any knowledge about the network traffic patterns when deploying such schemes. In this paper, we investigate the impact of traffic patterns on the performance of disaggregated sketches. Our findings show that blindly updating all fragments of a sketch might degrade the monitoring accuracy. Instead, taking into account the spatial distribution of the traffic may lead to globally better monitoring accuracy. Finally, we provide hints on the existence of an optimal solution for such a problem which opens new opportunities for the design of traffic-aware update policies for sketches

Data plane assisted state replication with Network Function Virtualization

Modern 5G networks are capable of providing ultra-low latency and highly scalable network services by employing modern networking paradigms such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). The latter enables performance-critical network applications to be run in a distributed fashion directly inside the infrastructure. Being distributed, those applications rely on sophisticated state replication algorithms to synchronize states among each other. Nevertheless, current implementations of such algorithms do not fully exploit the potential of the modern infrastructures, thus leading to sub-optimal performance. In this paper, we propose STARE, a novel state replication system tailored for 5G networks. At its core, STARE exploits stateful SDN to offload replication-related processes to the data plane, ultimately leading to reduced communication delays and processing overhead for VNFs. We provide a detailed description of the STARE architecture alongside a publicly-available P4- based implementation. Furthermore, our evaluation shows that STARE is capable of scaling to big networks while introducing low overhead in the network

Revamping Cloud Gaming with Distributed Engines

While cloud gaming has brought considerable advantages for its customers, from the point of view of cloud providers, multiple aspects related to infrastructure management still fall short of such kind of service. Indeed, differently from traditional cloud-ready applications, modern game engines are still based on monolithic software architectures. This aspect precludes the applicability of fine-grained resource management and service orchestration schemes, ultimately leading to poor cost-effectiveness. To mitigate these shortcomings, we propose a Cloud-Oriented Distributed Engine for Gaming (CODEG). Thanks to its distributed nature, CODEG is capable of fully exploiting the resource heterogeneity present in cloud data centers, while providing the possibility of spanning its service on multiple network layers up to the edge clouds

LOcAl DEcisions on Replicated States (LOADER) in programmable data planes: programming abstraction and experimental evaluation

Programmable data planes recently emerged as a prominent innovation in Software Defined Networking (SDN), by permitting support of stateful flow processing functions over hardware network switches specifically designed for network processing. Unlike early SDN solutions such as OpenFlow, modern stateful data planes permit to keep (and dynamically update) local per-flow states inside network switches, thus dramatically improving reactiveness of network applications to state changes. Still, also in stateful data planes, the control and update of non-local states is assumed to be completely delegated to a centralized controller and thus accessed only at the price of extra delay. Our LOADER proposal aims at contrasting the apparent dichotomy between local states and global states. We do so by introducing a new possibility: permit to take localized (in-switch) decisions not only on local states but also on replicated global states, thus providing support for network-wide applications without incurring the drawbacks of classical approaches. To this purpose, i) we provide high-level programming abstractions devised to define the states and the update logic of a generic network-wide application, and ii) we detail the underlying low level state management and replication mechanisms. We then show LOADER's independence of the stateful data plane technology employed, by implementing it over two distinct stateful data planes (P4 switches and OPP - Open Packet Processor - switches), and by experimentally validating both implementations in an emulated testbed using a simple distributed Deny-of-Service (DoS) detection application

Optimal state replication in stateful data planes

In SDN stateful data planes, switches can execute algorithms to process traffic based on local states. This approach permits to offload decisions from the controller to the switches, thus reducing the latency when reacting to network events. We consider distributed network applications that process traffic at each switch based on local replicas of network-wide states. Replicating a state across multiple switches poses many challenges, because the number of state replicas and their placement affects both the data traffic distribution and the amount of synchronization traffic among the replicas. In this paper, we formulate the optimal placement problem for replicated states, taking into account the data traffic routing, to ensure that traffic flows are properly managed by network applications, and the synchronization traffic between replicas, to ensure state coherence. Due to the high complexity required to find the optimal solution, we also propose an approximated algorithm to scale to large network instances. We numerically show that this algorithm, despite its simplicity, well approximates the optimal solution. We also show the beneficial effects of state replication with respect to the single-replica scenario, so far considered in the literature. Finally, we provide an asymptotic analysis to find the optimal number of replicas