Programmable data plane hardware creates new opportunities for infusing intelligence into the network. This raises a fundamental question: what kinds of computation should be delegated to the network?In this paper, we discuss the opportunities and challenges for co-designing data center distributed systems with their network layer. We believe that the time has finally come for offloading part of their computation to execute in-network. However, in-network computation tasks must be judiciously crafted to match the limitations of the network machine architecture of programmable devices. With the help of our experiments on machine learning and graph analytics workloads, we identify that aggregation functions raise opportunities to exploit the limited computation power of networking hardware to lessen network congestion and improve the overall application performance. Moreover, as a proof-of-concept, we propose DAIET, a system that performs in-network data aggregation. Experimental results with an initial prototype show a large data reduction ratio (86.9%-89.3%) and a similar decrease in the workers' computation time.

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Virtual Network Functions (VNFs) are often implemented using virtual machines (VMs) because they provide an isolated environment compatible with classical cloud computing technologies. Unfortunately, VMs are demanding in terms of required resources and therefore are not suitable for resourceconstrained devices such as residential CPEs. However, such hardware often runs a Linux-based operating system that supports several software modules (e.g., iptables) that can be used to implement network functions (e.g., a firewall), which can be exploited to provide some of the services offered by simple VNFs, but with reduced overhead. In this paper we propose and validate an architecture that integrates those native software components in a Network Function Virtualization (NFV) platform, making their use transparent from the user's point of view.

Due to the increasing popularity of smartphones and tablets, mobile apps are becoming the preferred portals for users to access various network services in both residential and enterprise environments. Predominantly using generic HTTP or HTTPS protocols, traffic from different mobile apps is largely indistinguishable. This loss of visibility into mobile app traffic brings new challenges to network management and traffic analysis. It has became very hard to implement network policies based on the differentiation between traffic from compliant and non-compliant mobile apps. This paper presents a system that not only provides network administrators the much desired capability of enforcing policies on mobile app traffic, but also does that at a fine per-user granularity. The proposed system takes a Network Functions Virtualization (NFV) approach and virtualizes an edge router into multiple virtual data planes. Specifically, each data plane serves solely to one particular user and consists of user-specific virtualized network functions. The independence of the virtual data planes facilitates enforcing network policies at the per-user level. To enable policy enforcement on mobile apps, our system includes a sophisticated mobile app identification module to recognize traffic from different apps using preloaded traffic signatures. By exploiting TLS proxying, our system can even enforce policies on those mobile apps adopting traffic encryption. We have implemented a prototype of the proposed system as a wireless access point (AP) using a commodity small form factor PC. Our preliminary experimental evaluations show that the system can scale to modest number of users without much impacting user experience in using the network.