Communication challenges between autistic and neurotypical individuals stem from a mutual lack of understanding of each other's distinct, and often contrasting, communication styles. Yet, autistic individuals are expected to adapt to neurotypical norms, making interactions inauthentic and mentally exhausting for them. To help redress this imbalance, we build NeuroBridge, an online platform that utilizes large language models (LLMs) to simulate: (a) an AI character that is direct and literal, a style common among many autistic individuals, and (b) four cross-neurotype communication scenarios in a feedback-driven conversation between this character and a neurotypical user. Through NeuroBridge, neurotypical individuals gain a firsthand look at autistic communication, and reflect on their role in shaping cross-neurotype interactions. In a user study with 12 neurotypical participants, we find that NeuroBridge improved their understanding of how autistic people may interpret language differently, with all describing autism as a social difference that "needs understanding by others" after completing the simulation. Participants valued its personalized, interactive format and described AI-generated feedback as "constructive", "logical" and "non-judgmental". Most perceived the portrayal of autism in the simulation as accurate, suggesting that users may readily accept AI-generated (mis)representations of disabilities. To conclude, we discuss design implications for disability representation in AI, the need for making NeuroBridge more personalized, and LLMs' limitations in modeling complex social scenarios.

Autistic individuals often experience difficulties in conveying and interpreting emotional tone and non-literal nuances. Many also mask1 their communication style to avoid being misconstrued by others, spending considerable time and mental effort in the process. To address these challenges in text-based communication, we present TwIPS2, a prototype texting application powered by a large language model (LLM), which can assist users with: a) deciphering tone and meaning of incoming messages, b) ensuring the emotional tone of their message is in line with their intent, and c) coming up with alternate phrasing for messages that could be misconstrued and received negatively by others. We leverage an AI-based simulation and a conversational script to evaluate TwIPS with 8 autistic participants in an in-lab setting. Our findings show TwIPS enables a convenient way for participants to seek clarifications, provides a better alternative to tone indicators, and facilitates constructive reflection on writing technique and style. We also examine how autistic users utilize language for self-expression and interpretation in instant messaging, and gather feedback for enhancing our prototype. We conclude with a discussion around balancing user-autonomy with AI-mediation, establishing appropriate trust levels in AI systems, and autistic users’ customization needs in the context of AI-assisted communication.

This paper presents the design and evaluation of CustomAR – a customizable Augmented Reality (AR) application, designed in collaboration with therapists, that allows them to create and customize picture-based AR experiences for use in an autistic context. Using a 2-week diary study, we gauge whether the application’s customization options and features are sufficient to allow therapists and educational professionals to create AR experiences for the various learning activities they conduct with autistic children, and what challenges they face in this regard. We find that participants think the application would be suitable for creating AR experiences for a wide range of learning activities, such as choice-making and teaching daily living skills, and think that the application’s freeze feature can be helpful when working with children with limited attention. Towards the end of the paper we discuss the challenges users face when trying to incorporate picture-based AR in practical therapy exercises, and how they can be addressed.

Network resource management, or how bandwidth is allocated to flows, tenants, or applications, is a challenging problem. In this paper, we call for using the database abstraction for network resource management. A database provides simple constructs for supporting complex resource management tasks, such as transactions to support bandwidth reservations on multiple links, virtual tables for restricting the view of tenants in a cloud environment, and many others. To highlight the opportunities and challenges in this space, we present a research agenda around new abstractions and policy languages, the necessary data plane support, and potential for domain specific replication and sharding strategies for the resource management database.

Augmented Reality (AR) applications can enable geographically distant users to collaborate using shared video feeds or interactive 3D holograms, and may be particularly useful in the socially distant context of the Covid-19 pandemic. However, a good user experience is key for their success and could be negatively impacted by network impairments, which are an inevitable occurrence in today's best-effort Internet. In this paper, we present the findings of an empirical user study, aimed at understanding the effects of network outages, on user experience and behavior, in a collaborative AR task. We highlight how network outages affected users in different ways depending on their role in the collaborative task, and how giving users explicit information about poor network conditions helped them deal with some of these negative effects. Furthermore, we report the strategies that users themselves adopted, to deal with outages, such as batching instructions, or shifting to a different spatial referencing style when communicating with their partners. Lastly, based on our findings, we present some design implications for future remote-collaborative AR applications.

Mobile devices have become the primary mode for Internet access in developing countries. Yet typical data plans and SMS costs can be overwhelming for low income users in these countries. In this paper, we explore the design and usability of a free but extremely low bit rate communication channel to address this challenge. We propose, a data communication channel that uses to transmit messages between phones, thereby sacrificing performance in exchange for low cost. While the data rate of is extremely low (<1 bps), our prototype implementation and small scale user studies explore the feasibility of this idea for different types of messaging scenarios. Our results show that could be a viable option for messaging scenarios that require short, pre-determined responses (e.g., survey questions) while for traditional SMS-style messaging, a suitable user interface and other customizations are likely required to make it a viable option for users.

We revisit the long-standing problem of providing network QoS to applications, and propose the concept of judicious QoS - combining the cheaper, best effort IP service with the cloud, which offers a highly reliable infrastructure and the ability to add in-network services, albeit at higher cost. Our proposed J-QoS framework offers a range of reliability services with different cost vs. delay trade-offs, including: i) a forwarding service that forwards packets over the cloud overlay, ii) a caching service, which stores packets inside the cloud and allows them to be pulled in case of packet loss or disruption on the Internet, and iii) a novel coding service that provides the least expensive packet recovery option by combining packets of multiple application streams and sending a small number of coded packets across the more expensive cloud paths. We demonstrate the feasibility of these services using measurements from RIPE Atlas and a live deployment on PlanetLab. We also consider case studies on how J-QoS works with services up and down the network stack, including Skype video conferencing, TCP-based web transfers and cellular access networks.

Duplication can be a powerful strategy for overcoming stragglers in cloud services, but is often used conservatively because of the risk of overloading the system. We call for making duplication a first-class concept in cloud systems, and make two contributions in this regard. First, we present duplicate-aware scheduling or DAS, an aggressive duplication policy that duplicates every job, but keeps the system safe by providing suitable support (prioritization and purging) at multiple layers of the cloud system. Second, we present the D-Stage abstraction, which supports DAS and other duplication policies across diverse layers of a cloud system (e.g., network, storage, etc.). The D-Stage abstraction decouples the duplication policy from the mechanism, and facilitates working with legacy layers of a system. Using this abstraction, we evaluate the benefits of DAS for two data parallel applications (HDFS, an in-memory workload generator) and a network function (Snort-based IDS cluster). Our experiments on the public cloud and Emulab show that DAS is safe to use, and the tail latency improvement holds across a wide range of workloads.

Existing flow scheduling schemes for data center networks optimize for a specific workload and performance metric. In this paper, we present 2D, a new scheduling policy that offers robustness across performance metrics and changing workloads - a ground existing scheduling policies are unable to cover. 2D combines basic scheduling building blocks of multiplexing and serialization in a principled way, ensuring tail optimal performance across workloads while also improving the average (and lower percentiles) completion times.

Datacenter networks are often structured as multi-rooted trees to provide high bisection bandwidth at low cost. To utilize the available bisection bandwidth, an efficient load balancing algorithm is required. Packet Spraying is known to perform well in symmetric topologies as it provides per-packet load balancing over equal cost paths. However, packet spraying performs poorly in asymmetric topologies. In this paper we ask, “How can we make packet spraying effective in asymmetric topologies while retaining its simplicity?” Towards this end, we propose SAPS, “Symmetric Adaptive Packet Spraying”, an SDN-based scheme that uses packet spraying over symmetric virtual topologies. SAPS is based on the key insight that if we provide each flow with a symmetric view of the network fabric, then packet spraying can produce near-optimal performance. We evaluate SAPS using simulations and testbed experiments. Our results indicate that SAPS performs well for a variety of application workloads and asymmetric network scenarios.

Many popular cloud applications use inter-data center paths; yet, little is known about the characteristics of these ``cloud paths''. Over an eighteen month period, we measure the inter-continental cloud paths of three providers (Amazon, Google, and Microsoft) using client side (VM-to-VM) measurements. We find that cloud paths are more predictable compared to public Internet paths, with an order of magnitude lower loss rate and jitter at the tail (95th percentile and beyond) compared to public Internet paths. We also investigate the nature of packet losses on these paths (e.g., random vs. bursty) and potential reasons why these paths may be better in quality. Based on our insights, we consider how we can further improve the quality of these paths with the help of existing loss mitigation techniques. We demonstrate that using the cloud path in conjunction with a detour path can mask most of the cloud losses, resulting in up to five 9's of network availability for applications.

Several data center transport protocols have been proposed in recent years (e.g., DCTCP, PDQ, and pFabric). In this paper, we first identify the underlying strategies used by the existing data center transports, namely, in-network Prioritization (used in pFabric), Arbitration (used in PDQ), and Self-adjusting at Endpoints (PASE) (used in DCTCP). We show that these strategies are complimentary to each other, rather than substitutes, as they have different strengths and can address each other's limitations. Unfortunately, prior data center transports use only one of these strategies. As a result, they either achieve near-optimal performance or deployment friendliness (i.e., require no changes to the data plane) but not both. Based on this insight, we design a data center transport protocol called PASE, which carefully synthesizes these strategies by assigning different transport responsibilities to each strategy. The key advantage of PASE over prior art is that it achieves both near-optimal performance as well as deployment friendliness. PASE does not require any changes in network switches (hardware or software); yet, it achieves comparable, or even better, performance than the state-of-the-art protocols (such as pFabric) that require changes to network elements. Our evaluation results show that the PASE performs well for a wide range of application workloads and network settings.

In this paper, we make a case for a redundancy-aware network stack (RANS) for data centers. In RANS, applications expose information about replicas to the network, which in turn, uses duplicate requests to improve performance of typical applications by enabling them to effectively avoid stragglers. At the heart of RANS is the use of duplicate-aware scheduling, which ensures that duplicate-requests do not overload the system and disturb any primary requests. We highlight the challenges and opportunities present at different layers of RANS, from new interfaces that capture replicas and their semantics, to in-network mechanisms that deal with duplicates. Our preliminary evaluation shows the promise of duplicate-aware scheduling in improving performance of typical data center applications.

We make a case for judicious use of cloud infrastructure -- as an overlay to aid IP's best effort service. As an example, we propose ReWAN, a packet recovery service for real time, wide area communication. ReWAN uses cloud-based edge proxies that exchange "recovery" packets, which are used only in case of packet loss. ReWAN leverages the cloud provider's well-connected, global data center network, and its ability to handle many concurrent users. To minimize bandwidth cost, it uses coding to generate a small number of recovery packets that are sent across the inter data center network. The recovery packets use both FEC, which is applied within a user stream, and network coding, which is applied across user streams. If a small fraction of packets within the user stream are lost, ReWAN uses the FEC packets for recovery; for other losses, ReWAN recovers them with the help of other receivers and the network coded packets. Preliminary measurements show the promise of ReWAN in providing a fast and cost effective packet recovery service.

Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user's news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation -- rather than as part of a task -- and therefore only optimize flow-level metrics.

Many data center transports have been proposed in recent times (e.g., DCTCP, PDQ, pFabric, etc). Contrary to the common perception that they are competitors (i.e., protocol A vs. protocol B), we claim that the underlying strategies used in these protocols are, in fact, complementary. Based on this insight, we design PASE, a transport framework that synthesizes existing transport strategies, namely, self-adjusting endpoints (used in TCP style protocols), innetwork prioritization (used in pFabric), and arbitration (used in PDQ). PASE is deployment friendly: it does not require any changes to the network fabric; yet, its performance is comparable to, or better than, the state-of-the-art protocols that require changes to network elements (e.g., pFabric). We evaluate PASE using simulations and testbed experiments. Our results show that PASE performs well for a wide range of application workloads and network settings.

The end-to-end nature of today's transport protocols is increasingly being questioned by the growing heterogeneity of networks and devices, and the need to support in-network services. To address these challenges, we present Tapa, a transport architecture that systematically combines two concepts. First, it unbundles today's transport such that network specific functions (e.g., congestion control) are implemented on a per-segment basis, where a segment spans a part of the end-to-end path that is homogeneous (e.g., wired Internet or an access network) while functions that relate to application semantics (e.g., data ordering) are still implemented end-to-end. Second, it has an explicit notion of in-network services (e.g., caching, opportunistic content retrieval, etc) that can be supported while maintaining precise end-to-end application semantics. In this paper, we present the basic design, implementation and evaluation of Tapa. We also present diverse case studies that show how Tapa can easily support opportunistic content retrieval in online social networks, various mobile and wireless optimizations, and an in-network energy saving service that improves battery life of mobile devices.

With the availability of higher network bandwidths, greater computing power, and a shift to more content centric use of the internet, researchers are revisiting the Internet architecture. One such effort is the Content Centric Networking (CCN) project where content driven data, rather than nodes, is primarily used for enabling communications. CCN include state maintenance at the intermediate network nodes and allow multiple paths to the content; two aspects absent in the current Internet architecture. In this paper, we focus on the problem of path selection in CCN. Our contribution is threefold - we propose: (i) a technique for downloading content from multiple sources simultaneously, maximizing aggregate bandwidth, (ii) a method for determining the highest bandwidth path to a single source for high bandwidth applications, and (iii) an approach for minimizing per packet delay for real time traffic. Compared to our approach, random forwarding gives only 63% of attainable bandwidth and twice the average per packet delay.

Motivated by limitations in today's host-based IP network architecture, recent studies have proposed clean-slate network architectures centered around alternative first-class principals, such as content, services, or users. However, much like the host-centric IP design, elevating one principal type above others hinders communication between other principals and inhibits the network's capability to evolve. Our work presents the eXpressive Internet Architecture (XIA), an architecture with native support for multiple principals and the ability to evolve its functionality to accommodate new, as yet unforeseen, principals over time. XIA also provides intrinsic security: communicating entities validate that their underlying intent was satisfied correctly without relying on external databases or configuration.

Energy management is a critical issue for mobile devices, with network activity often consuming a significant portion of the total system energy. In this paper, we propose Catnap, a system that reduces energy consumption of mobile devices by allowing them to sleep during data transfers. Catnap exploits high bandwidth wireless interfaces -- which offer significantly higher bandwidth compared to available bandwidth across the Internet -- by combining small gaps between packets into meaningful sleep intervals, thereby allowing the NIC as well as the device to doze off. Catnap targets data oriented applications, such as web and file transfers, which can afford delay of individual packets as long as the overall transfer times do not increase. Our evaluation shows that for small transfers (128kB to 5MB), Catnap allows the NIC to sleep for up to 70% of the total transfer time and for larger transfers, it allows the whole device to sleep for a significant fraction of the total transfer time. This results in battery life improvement of up to 2-5x for real devices like Nokia N810 and Thinkpad T60.

This paper presents the design, implementation, and evaluation of Ditto, a system that opportunistically caches overheard data to improve subsequent transfer throughput in wireless mesh networks. While mesh networks have been proposed as a way to provide cheap, easily deployable Internet access, they must maintain high transfer throughput to be able to compete with other last-mile technologies. Unfortunately, doing so is difficult because multi-hop wireless transmissions interfere with each other, reducing the available capacity on the network. This problem is particularly severe in common gateway-based scenarios in which nearly all transmissions go through one or a few gateways from the mesh network to the Internet.Ditto exploits on-path as well as opportunistic caching based on overhearing to improve the throughput of data transfers and to reduce load on the gateways. It uses content-based naming to provide application independent caching at the granularity of small chunks, a feature that is key to being able to cache partially overheard data transfers. Our evaluation of Ditto shows that it can achieve significant performance gains for cached data, increasing throughput by up to 7x over simpler on-path caching schemes, and by up to an order of magnitude over no caching.

Ethernet is a low-cost, flexible and high-speed transport technology, which has traditionally seen success in local area networks and is rapidly gaining popularity in metro networks. However, its control plane, primarily based on spanning tree protocol, is not well-suited for the metro and core networks. For such networks, the IETF is evaluating a proposed framework called GELS which uses GMPLS as the control plane for Ethernet data plane. In this paper, we provide a quantitative assessment of GELS for service provider networks. In particular, we perform simulations using COST239 and COST266 networks to evaluate the performance of GELS under normal network conditions, as well as under failure conditions. Under normal network conditions, we find that the use of GELS results in placement of up to 46.4% more bandwidth when compared with native Ethernet control such as RSTP. In terms of LSP acceptance, GELS shows a 45.5% improvement over RSTP. Similarly, average link utilization using GELS is significantly better than the average link utilization when the native Ethernet control plane is used. When considering single element failures, RSTP recovers by converging to a potentially new spanning tree, which may take unacceptably long. In contrast, well-known restoration and protection mechanisms of GMPLS control plane result in much faster recovery. We And that the convergence times exhibited by GELS after single element failures are orders of magnitude better than those obtained when RSTP is used.

We address the problem of connection preemption in a multi-class network environment. Our objective is: (i) to minimize the number of preempted connections, and (ii) to minimize the total preempted bandwidth, in that order. We show that this problem is NP-complete by reducing it to a well-known NP complete problem - the subset sum problem. Therefore, a known polynomial time algorithm, such as Minn.Conn [1], to solve this problem is suboptimal. We present an optimal algorithm with exponential complexity that can be used when the network load is light. We also present a fully polynomial time approximation algorithm that performs within a bounded factor from the optimal, and can be used in large networks having thousands of connections. We compare the performance of exact and approximate algorithms in a practical scenario by conducting simulations on a network representing twenty largest metros in the U.S. The simulations show that, on average, the approximate algorithm preempts bandwidth which is only a small fraction more as compared to that preempted by the exact algorithm, but is an order of magnitude more efficient in terms of execution time.