Conference services like Zoom, Microsoft Teams, and Google Meet facilitate millions of daily calls, yet ensuring high performance at low costs remains a significant challenge. This paper revisits the problem of packing calls across Media Processor (MP) servers that host the calls within individual datacenters (DCs). We show that the algorithm used in Microsoft Teams- a large scale conferencing service as well as other state-of-art algorithms are prone to placing calls resulting in some of the MPs becoming hot (high CPU utilization) that leads to degraded performance and/or elevated hosting costs. The problem arises from disregarding the variability in CPU usage among calls, influenced by differences in participant numbers and media types (audio/video), compounded by bursty call arrivals. To tackle this, we propose PnM, a multi-step framework which (a) optimizes initial call assignments by leveraging historical data and (b) periodically migrates calls from hot MPs using linear optimization, aiming to minimize hot MP usage. Evaluation based on a 24-hour trace of over 10 million calls in one DC shows that PnM reduces participant numbers on hot MPs by at least 2.5×.

Assistance through wheelchair-mounted manipulators has the potential to enhance the independence of individuals with disabilities. However, existing approaches primarily focus on single-arm systems or require extensive user input and demonstrations to infer intentions. In this study, we present a shared control framework for intuitive dual-arm operation through a standard 2D joystick, focusing on pick-and-place tasks. Our approach infers user intent in real-time, eliminating the need for specifying prior goals or beliefs. To address the challenges of controlling two 7-DoF (Kinova Gen3) manipulators, we propose two distinct control methods: one for pre-grasp positioning and one for grasp execution. The first method employs a shared control policy to optimize the pre-grasp positioning of the wheelchair base, ensuring ergonomic alignment for front-grasping tasks while incorporating mobile manipulation to reduce task completion time. The second method allows users to maintain high-level goal control and fine-tuning through task-specific arbitration. Experimental results demonstrate high grasp quality and task efficiency across pick-and-place scenarios, establishing the feasibility of shared control for bimanual manipulation and wheelchair navigation. We believe this is the first unified framework for mobile bimanual manipulation using standard wheelchair controls.

Network measurement has long focused on the bits and bytes --- low-level network metrics such as latency and throughput, which have the advantage of being objective and directly characterizing the performance of the network. We argue that users provide a rich and largely untapped source of implicit as well as explicit signals that could complement and expand the coverage of traditional methods. Implicit feedback leverages user actions to indirectly infer the network performance and the resulting quality of user experience. Explicit feedback leverages user input, typically provided offline, to expand the reach of network measurement, especially for newer ones.

Resource management is important for conferencing services (such as Microsoft Teams, Zoom) to ensure good user experience while keeping the costs low. Key to this is the efficient provisioning and assignment of media processing (MP) servers, which do the heavy lifting of mixing and redistributing the media streams from and to the call participants.

Speeding up network updates is crucial to maintain high agility and to react quickly to network failures. In this paper, we present Ctalyst---a new design to reduce the network update time. We observe that networks offer a power of choice, where there are many equally-good alternative paths that traffic flows can be assigned to, which is facilitated by redundancy in networks. Catalyst exploits this power of choice to assign flows to alternative paths to merge stages in the dependency graph (that captures the update plan), which in turn reduces the total update time. Furthermore, we observe that because of the prevalence of switch stragglers---switches that unexpectedly take longer time to update, simply assigning a flow to a single (shortest) path is not an optimal design as even a single switch straggler can substantially increase the update time. Thus, the second principle in Catalyst is to compute multiple paths for individual flows offline, among which one would be selected at runtime based on temporal switch conditions, in order to enable a fast update. Our evaluation using a load-balancer setting in a data center network shows that Catalyst effectively reduces the total update time by 1.14--2.15x.

CoFlow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art CoFlow schedulers in essence approximate the classic online Shortest-Job-First (SJF) scheduling, designed for a single CPU, in a distributed setting, with no coordination among how the flows of a CoFlow at individual ports are scheduled, and as a result suffer two performance drawbacks: (1) The flows of a CoFlow may suffer the out-of-sync problem -- they may be scheduled at different times and become drifting apart, negatively affecting the CoFlow completion time (CCT); (2) FIFO scheduling of flows at each port bears no notion of SJF, leading to suboptimal CCT.

Layer-7 load balancing is a foundational building block of online services. The lack of offerings from major public cloud providers have left online services to build their own load balancers (LB), or use third-party LB design such as HAProxy. The key problem with such proxy-based design is each proxy instance is a single point of failure, as upon its failure, the TCP flow state for the connections with the client and server is lost which breaks the user flows. This significantly affects user experience and online services revenue.

Load balancing is a foundational function of datacenter infrastructures and is critical to the performance of online services hosted in datacenters. As the demand for cloud services grows, expensive and hard-to-scale dedicated hardware load balancers are being replaced with software load balancers that scale using a distributed data plane that runs on commodity servers. Software load balancers offer low cost, high availability and high flexibility, but suffer high latency and low capacity per load balancer, making them less than ideal for applications that demand either high throughput, or low latency or both. In this paper, we present Duet, which offers all the benefits of software load balancer, along with low latency and high availability -- at next to no cost. We do this by exploiting a hitherto overlooked resource in the data center networks -- the switches themselves. We show how to embed the load balancing functionality into existing hardware switches, thereby achieving organic scalability at no extra cost. For flexibility and high availability, Duet seamlessly integrates the switch-based load balancer with a small deployment of software load balancer. We enumerate and solve several architectural and algorithmic challenges involved in building such a hybrid load balancer. We evaluate Duet using a prototype implementation, as well as extensive simulations driven by traces from our production data centers. Our evaluation shows that Duet provides 10x more capacity than a software load balancer, at a fraction of a cost, while reducing latency by a factor of 10 or more, and is able to quickly adapt to network dynamics including failures.

We present Dionysus, a system for fast, consistent network updates in software-defined networks. Dionysus encodes as a graph the consistency-related dependencies among updates at individual switches, and it then dynamically schedules these updates based on runtime differences in the update speeds of different switches. This dynamic scheduling is the key to its speed; prior update methods are slow because they pre-determine a schedule, which does not adapt to runtime conditions. Testbed experiments and data-driven simulations show that Dionysus improves the median update speed by 53--88% in both wide area and data center networks compared to prior methods.

There is an ongoing desire to make virtual humans a more accessible tool for use in entertainment, training, and evaluations. From the graphical level to the animation level to the intelligence level, complexities abound. As research progresses some of these complexities become hidden from the end user. Ultimately, we would like to treat agents as real humans and instruct them as you might another person. Here we present a framework, inspired by natural language constructs, that aims to obfuscate the complexities and allow users to control virtual humans through structured English input. Our focus is on object and environment interactions, particularly spatial relationships.

While the initial wave of in-memory key-value stores has been optimized for serving relatively fixed content to a very large number of users, an emerging class of enterprise-scale data analytics workloads focuses on capturing, analyzing, and reacting to data in real-time. At the same time, advances in network technologies are shifting the performance bottleneck from the network to the memory subsystem. To address these new trends, we present a bottom-up approach to building a high performance in-memory key-value store, Mercury, for both traditional, read-intensive as well as emerging workloads with high write-to-read ratio. Mercury's architecture is based on two key design principles: (i) economizing the number of DRAM accesses per operation, and (ii) reducing synchronization overheads. We implement these principles with a simple hash table with linked-list based chaining, and provide high concurrency with a fine-grained, cache-friendly locking scheme. On a commodity single-socket server with 12 cores, Mercury scales with number of cores and executes 14 times more queries/second than a popular hash-based key-value system, Memcached, for both read and write-heavy workloads.

A primary challenge in exploiting Cognitive Radio Networks (CRNs), known as the rendezvous problem, is for the users to find each other in the dynamic open spectrum. We study blind rendezvous, where users search for each other without any infrastructural aid. Previous work in this area have focused on efficient blind rendezvous algorithms for two users but the solution for multiple users is still far from optimal. In particular, when two users encounter, one user inherits the other's hopping sequence but the sequence is never shortened or split among the encountering users. We denote this class of algorithms as uncoordinated channel hopping algorithms. In this paper, we introduce a new class of distributed algorithms for multi-user blind rendezvous, called Coordinated Channel Hopping (CCH), where users adjust, or coordinate, the sequence of channels being hopped as they rendezvous pairwise. Compared to existing rendezvous algorithms, our algorithms achieve 80% lower Time To Rendezvous (TTR) in case of multiple users.

A primary challenge in multicasting video in a wireless LAN is to deal with the client diversity -- clients may have different channel characteristics and hence receive different numbers of transmissions from the AP. A promising approach to overcome this problem is to combine scalable video coding techniques such as MRC or MDC, which divide a video stream into multiple substreams, with inter-layer network coding. The fundamental challenge in such an approach is to determine the strategy of coding the packets across different layers that maximizes the number of decoded layers at all clients. In [7], the authors showed that inter-layer NC indeed helps the delivery of MRC coded media over the WiFi, and proposed how to efficiently search for the optimal coding strategies online.