Vision Transformers (ViTs) have outperformed traditional Convolutional Neural Network architectures and achieved state-of-the-art results in various computer vision tasks. Since ViTs are computationally expensive, the models either have to be pruned to run on resource-limited edge devices only or have to be executed on remote cloud servers after receiving the raw data transmitted over fluctuating networks. The resulting degraded performance or high latency all hinder their widespread applications. In this paper, we present Janus, the first framework for low-latency cloud-device collaborative Vision Transformer inference over dynamic networks. Janus overcomes the intrinsic model limitations of ViTs and realizes collaboratively executing ViT models on both cloud and edge devices, achieving low latency, high accuracy, and low communication overhead. Specifically, Janus judiciously combines token pruning techniques with a carefully designed fine-to-coarse model splitting policy and non-static mixed pruning policy. It attains a balance between accuracy and latency by dynamically selecting the optimal pruning level and split point. Experimental results across various tasks demonstrate that Janus enhances throughput by up to 5.15x and reduces latency violation ratios by up to 98.7% when compared with baseline approaches under various network environments.

We propose Roaming-in-Place (RinP), a technique for dynamically sharing capacity across mobile network operators. RinP is a new form of infrastructure sharing that expands the traditional notion of roaming in cellular networks such that users may roam between operators with overlapping coverage areas based on load and performance conditions. Using simulation and small-scale experiments, we show that deploying RinP would allow operators to run their networks at higher utilization and provide users with higher availability and performance, while achieving 30–40% infrastructure savings in our typical evaluation scenarios. We present a design for RinP that can be incrementally deployed with modest changes to existing cellular infrastructure. We build a prototype RinP testbed, and show that our proposed design can be realized feasibly with modest changes to existing cellular infrastructure, requires no change to current protocol standards, and adds minimal latency overheads.

Microservices are increasingly used in modern applications, leading to a growing need for effective service composition solutions. However, we argue that traditional API-centric composition mechanisms (e.g., RPC, REST, and Pub/Sub) hamper the modularity of microservices. These mechanisms introduce rigid code-level coupling, scatter composition logic, and hinder visibility into cross-service data exchanges. Ultimately, these limitations complicate the maintenance and evolution of microservice-based applications. In this paper, we propose a rethinking of service composition and present Knactor, a new data-centric composition framework to restore the modularity that microservices were intended to offer. Knactor decouples service composition from service development, allowing composition to be implemented as explicit data exchanges among multiple services. Our initial case study suggests that this approach not only simplifies service composition but also opens up opportunities for data-driven policies and optimizations.

Events that take 10s to 100s of ns like cache misses increasingly cause CPU stalls. However, hiding the latency of these events is challenging: hardware mechanisms suffer from the lack of flexibility, whereas prior software mechanisms fall short due to large overhead and limited event visibility. In this paper, we argue that with a combination of two emerging techniques - light-weight coroutines and sample-based profiling, hiding these events in software is within reach.

Digibox is a prototyping environment for IoT applications. It enables a novel scene-centric prototyping where developers can program an ensemble of simulated devices to capture not only their individual but also their coordinated behaviors, making it possible to test, debug, and evaluate the behaviors of an IoT application. Using Digibox, developers can download and reuse existing scenes, customize, and repurpose them towards developing new applications; or replicate others' experiment results from scientific research. Digibox's Kubernetes-based runtime further allows developers to easily scale the prototyping environment from a single laptop to a cluster running simulated devices and scenes at a scale appropriate to the application.

Markets in which competition thrives are good for both consumers and innovation but, unfortunately, competition is not thriving in the increasingly important cellular market. We propose CellBricks, a novel cellular architecture that lowers the barrier to entry for new operators by enabling users to consume access on-demand from any available cellular operator — small or large, trusted or untrusted. CellBricks achieves this by moving support for mobility and user management (authentication and billing) out of the network and into end hosts. These changes, we believe, bring valuable benefits beyond enabling competition: they lead to a cellular infrastructure that is simpler and more efficient.

We present dSpace, an open and modular programming framework that aims to simplify and accelerate the development of smart space applications. To achieve this, dSpace provides two key building blocks~digivices that implement device control and actuation and digidata that process IoT data to generate events and insights. In addition, dSpace introduces novel abstractions - mount, yield, and pipe - via which digivices and digidata can be composed into higher-level abstractions. We apply dSpace to home automation systems and show how developers can easily and flexibly leverage these abstractions to support a wide range of home automation scenarios. Finally, we show how the dSpace concepts can be realized using a microservices-based architecture and implement dSpace as a Kubernetes-compatible framework.

Despite that IaaS users are busy scaling up/out their cloud instances to meet the ever-increasing demands, the dynamics of their demands, as well as the coarse-grained billing options offered by leading cloud providers, have led to substantial instance underutilization in both temporal and spatial domains. This paper theoretically examines an instance subletting service, where underutilized instances are leased to others within user-specified periods. Serving as a secondary market that complements the existing instance market of IaaS providers,we specifically identify the theoretical challenges in instance subletting services, and design an online auction mechanism tomake allocation and pricing decisions for the instances to besublet. Our mechanism guarantees truthfulness and individualrationality with the best possible competitive ratio. Extensivetrace-driven simulations show that our proposed mechanismachieves significant performance gains in both cost and socialwelfare.

In the public cloud market, there has been a constant battle over the billing options of the cloud instances between their providers and their users. The users generally have to pay for the entire billing cycle even on fractional usage. Ideally, the residual life-cycles should be resalable by the users, which demands efficient resource consolidation and multiplexing; otherwise, the revenue and use cases are confined by the transient nature of the instances. This paper presents HARV, a novel cloud service that facilitates the management and trade of cloud instances through a third-party platform to run buyers' tasks. The platform relies on hybrid virtualization, an infrastructure layout integrating both the hypervisor-based virtualization and lightweight containerization. It further incorporates a truthful online auction mechanism for instance trading and resource allocation. Our design achieves efficient resource consolidation with no need for provider-level support, and we have deployed a prototype of HARV on the Amazon EC2 public cloud. Our evaluations on both micro-benchmarks and real-life workloads reveal that applications experience negligible performance overhead when hosted on HARV. Trace-driven simulations further show that HARV can achieve substantial cost savings.

Combining advanced sensors and powerful processing capabilities smart-phone based augmented reality (AR) is becoming increasingly prolific. The increase in prominence of these resource hungry AR applications poses significant challenges to energy constrained environments such as mobile-phones.; [email protected] that end we present a platform for offloading AR applications to powerful cloud servers. We implement this system using a thin-client design and explore its performance using the real world application Pokemon Go as a case study. We show that with careful design a thin client is capable of offloading much of the AR processing to a cloud server, with the results being streamed back. Our initial experiments show substantial energy savings, low latency and excellent image quality even at relatively low bit-rates.