Estimating long-term causal effects by combining long-term observational and short-term experimental data is a crucial but challenging problem in many real-world scenarios. In existing methods, several ideal assumptions, e.g. latent unconfoundedness assumption or additive equi-confounding bias assumption, are proposed to address the latent confounder problem raised by the observational data. However, in real-world applications, these assumptions are typically violated which limits their practical effectiveness. In this paper, we tackle the problem of estimating the long-term individual causal effects without the aforementioned assumptions. Specifically, we propose to utilize the natural heterogeneity of data, such as data from multiple sources, to identify latent confounders, thereby significantly avoiding reliance on idealized assumptions. Practically, we devise a latent representation learning-based estimator of long-term causal effects. Theoretically, we establish the identifiability of latent confounders, with which we further achieve long-term effect identification. Extensive experimental studies, conducted on multiple synthetic and semi-synthetic datasets, demonstrate the effectiveness of our proposed method.

Open vSwitch (OvS) has been widely used in cloud networks in view of its programmability and flexibility. However, we observe a huge performance drop when it loads practical cloud networking services (e.g., tunneling and firewalling). Our further analysis reveals that the root cause lies in the gap between the needs of supporting various selections of packet header fields and the one-size-fits-all data path in the vanilla OvS. Motivated by this, we design Hoda, a high-performance OvS dataplane with multiple specialized data paths. Specifically, Hoda constructs the specialized parser and microflow cache for each OpenFlow program so as to achieve lightweight parsing and caching. We also propose a configurable version of Hoda that introduces configuration knobs in the data path to ease specialization. The experiments with real-life OpenFlow rules show that Hoda achieves up to 1.7× speed up over the state-of-the-art OvS and 1.5× speed up over mSwitch. Hoda has also been deployed in a large cloud to serve various online services; the A/B test in the cloud reveals a 20% request process time reduction for Ngnix services.

Group communication is a one-to-many communication pattern that distributes data from one sender to multiple receivers. Multicast as a group communication method is extensively employed in content distribution scenarios, such as IP-based cable TV, live streaming, video conference, data delivery, etc. Since application-layer multicast cannot touch network elements, it suffers from inefficient network utilization. Some existing network-assisted multicast are limited by scalability issues caused by the inability to maintain a sharply increased per-flow state in individual network elements. Moreover, existing network-assisted multicast is unable to meet the newly emerging need, such as load balancing and traffic engineering. This paper presents a novel multicast technique called Carrier-Grade Minimalism Multicast (CGM2). Through a string of bits, the bitmap, nodes are instructed to locally replicate packets and forward them over several interfaces without maintaining a per-flow state. Bitmaps of nodes along the multicast path are encapsulated in the recursive format as a binary in-packet tree for explicit forwarding path.

Today, Internet service deployment is typically implemented with server replication at multiple locations. Domain name system (DNS), which translates human-readable domain names into network-routable IP addresses, is typically used for distributing users to different server replicas. However, DNS relies on several network-based queries and the queries delay the connection setup process between the client and the server replica. In this article, we propose Artemis, a practical low-latency naming and routing system that supports optimal server (replica) selection based on user-defined policies and provides lower query latencies than DNS. Artemis uses a DNS-like domain name-IP mapping for replica selection and achieves low query latency by combining the name resolution process with the transport layer handshake process. In Artemis, all server replicas at different locations share the same anycast IP address, called Service Address. Clients use the Service Address to establish a transport layer connection with the server. The client's initial handshake packet is routed over an overlay network to reach the optimal server. Then the server migrates the transport layer connection to its original unicast IP address after finishing the handshake process. After that, service discovery is completed, and the client communicates with the server directly via IP addresses. To validate the effectiveness of Artemis, we evaluate its performance via both real trace-driven simulation and real-world deployment. The result shows that Artemis can handle a large number of connections and reduce the connection setup latency compared with state-of-the-art solutions. More specifically, our deployment across 11 Google data centers shows that Artemis reduces the connection setup latency by 39.4% compared with DNS.

Group communication is a communication pattern where data is distributed from one sender to many receivers. It is widely applied in content distribution scenarios, such as IPTV, live streaming, video conference, data delivery, and others. Multicast is an efficient one-to-many data distribution method. However, application-layer multicast still has problems of inefficient network utilization as it is not allowed to control network elements. Existing network assisted multicast is unable to meet the newly emerging demand, such as multicast traffic engineering and load balancing. Some implementations of network assisted multicast are also limited to scalability issues caused by limitations of per-flow state maintained in network elements. In this paper, we introduce a novel multicast technology called Carrier-Grade Minimalism Multicast (CGMM) which leverages a string of bits, the bitmap, to instruct node to locally duplicate packets and forward them over several links without per-flow state and encapsulates bitmaps as binary in-packet tree for explicit forwarding path.