Low Earth Orbit (LEO) satellites are an important facet of global connectivity providing high speed Internet, cellular, IoT connectivity and so on. Combined with the rich resource availability on each satellite, LEO satellites represent a new, emerging cloud frontier - the LEO Compute Cloud. However, satellite mobility introduces non-trivial challenges when orchestrating applications for a LEO compute cloud, making it harder to deploy applications without increasing the latency and bandwidth costs. In this paper, we identify the concrete challenges in using state-of-the-art terrestrial orchestrators for a LEO compute cloud. We present Krios - a LEO compute cloud orchestration system that hides the complexities introduced by satellite mobility and enables a practical LEO compute cloud. The design of Krios is centered around a novel LEO zones abstraction that allows application providers to specify where their applications should be available. Krios provides crucial system support to enable the LEO zones abstraction, ensuring uninterrupted availability of applications in LEO zones via proactive and predictive application handovers. Our experimental evaluation of Krios with representative applications demonstrates a practical and efficient LEO compute cloud, without requiring any disruptive changes in applications and with modest system overheads. With Krios, LEO orchestration requires just ~1 application instance at a time to maintain the same availability as what prior work achieves by deploying application instances on all satellites or by performing 6-10 times more frequent expensive handovers.

Effective provisioning and resource management in edge environments are critical for ensuring infrastructure efficiency while providing latency-critical service-level objectives (SLOs). Realizing this requires low-overhead aggregation of fine-grained information regarding workloads' resource demands. Unfortunately, we show that this cannot be adequately achieved with existing solutions used for cloud technologies such as Kubernetes and containers, which are prevalent in edge systems. We propose Colibri, a lightweight and flexible monitoring system for edge computing that characterizes containers across CPU, memory, and network resource usage patterns at millisecond granularity. Colibri can be dispatched dynamically as needed and enables accurate characterization of workload resource usage. We demonstrate experimentally that using Colibri can significantly reduce SLO violations caused when relying on existing tools, while saving resources for representative edge workloads. Colibri provides this while consuming only 2% of the resources used by existing cloud monitoring tools when they operate at the same query granularity, making it an efficient and effective solution for edge computing environments.

Next generation 6G wireless envisions a much higher data rate and a lower latency compared to 5G wireless networks. Directional antennas with narrow beams across high mmWave frequencies hold the key to achieving such high data rates. However, Beam Management (BM), which is the process of finding appropriate transmit and receive beams, offers significant challenges. Dynamic channel variation, user mobility, and narrow beams in high frequency mmWave channels further complicate these challenges. Efficient Machine Learning (ML) strategies can be used to alleviate this overhead. In spite of their underlying differences, sub-6 GHz and high frequency mmWave channels share some similarities in array geometry, number of paths, and surrounding environment. As sub-6 GHz channel characteristics are relatively easier to acquire and learn, we introduce a new machine learning framework, using transformer and LSTM to learn sub-6 GHz channel information over time for efficient beam prediction across high frequency mmWave channels. System Level simulation results point out that when using time-series data-based learning of beam patterns with transformers or LSTMs in sub-6 GHz channels, our proposed scheme achieves up to 99.5% top-5 beam prediction accuracy while reducing the BM overhead by over 50% compared to existing work.

One of the major challenges in serving ML applications is the resource pressure introduced by the underlying ML frameworks. This becomes a bigger problem at resource-constrained, multi-tenant edge server locations, where it is necessary to scale to a larger number of clients with a fixed resource envelope. Naive approaches which simply minimize the resource budget allocation of each application result in performance degradation that voids the benefits expected from operating at the edge.

Demand for distributed computational infrastructure is growing in order to offer low latency connections to end users. The fragmenting infrastructure complicates the resource allocation process. As the number of infrastructure providers grows, points of presence are resource constrained compared to the cloud, they have diverse availability profiles, and diverse connectivity properties. Existing resource allocation approaches require providers share intimate details about their infrastructure to support the placement process, or rely on third party aggregators. Such solutions introduce strong assumptions of trust and collaboration. In this work we present Angler, the first system to allocate resources from dark pools, meaning the capacity and requests of the distributed pool of resources are unknown. Angler leverages cryptographic protocols for secure function evaluation, namely the WRK secure multiparty computation (MPC) protocol [76]. While MPC protocols can have large overheads compared to plaintext function evaluation, an end-to-end approach to the system design subverts the expensive overheads. Specifically, Angler combines a tuned implementation of a maliciously secure MPC protocol, a tailored distributed hash table, and a systematic effort to make the best allocation decision within a response time envelope. Angler is only 2x slower than resource allocation with no privacy when arbitrating among 8 providers, taking less than a second.

Extended reality (XR) applications require computationally demanding functionalities with low end-to-end latency and high throughput. To enable XR on commodity devices, a number of distributed systems solutions enable offloading of XR workloads on remote servers. However, they make a priori decisions regarding the offloaded functionalities based on assumptions about operating factors, and their benefits are restricted to specific deployment contexts. To realize the benefits of offloading in various distributed environments, we present a distributed stream processing system, FleXR, which is specialized for real-time and interactive workloads and enables flexible distributions of XR functionalities. In building FleXR, we identified and resolved several issues of presenting XR functionalities as distributed pipelines. FleXR provides a framework for flexible distribution of XR pipelines while streamlining development and deployment phases. We evaluate FleXR with three XR use cases in four different distribution scenarios. In the results, the best-case distribution scenario shows up to 50% less end-to-end latency and 3.9x pipeline throughput compared to alternatives.

Today, the edge computing system stack is built by leveraging the current cloud technologies, such as the containers, Kubernetes, etc., because, like the cloud, the edge is multi-tenant infrastructure. However, edge applications have more latency-critical SLAs and the infrastructure itself resource-constrained. That puts additional burdens on its control plane, which are not addressed by the cloud control plain tools. At the edge, if deployments aren't specified accurately, edge providers will face the dilemma between the waste of resource due to overcommitment vs. SLA violations. However, we observed that it is not feasible to rely on the existing monitoring tools, designed for the cloud, to glean that information from workloads with varying use of resources, at the needed fine granularity. Trying to do that with brute-forcing cloud solutions turns out to be extremely demanding on the resources allocated to the control plane. We present a new control plane tool, Colibri, aimed at addressing those conflicting requirements. Colibri can be dispatched dynamically, when needed, and enables characterization of containers deployed using Kubernetes across CPU, memory and network resource usage patterns at millisecond scale. The preliminary results demonstrate the effectiveness of out approach in reducing SLA violations by up to 98% for representative edge workloads.

Mobile Edge Computing (MEC) creates new infrastructure at the edges of the mobile networks, thus providing transformative opportunities for applications seeking latency benefits by operating closer to end-users and devices. However, the reduced network distance between the application endpoints of the MEC flows causes pattern shifts in the packet bursts exchanged at the network edges. The longer and denser bursts create a new source of contention that is not considered by current solutions. As a result, naively collocating applications onto the MEC tier can negatively affect latency-critical workloads, resulting in up to 73% packets experiencing as much as 3.8x increased latency. This makes it impossible to support latency-centric SLOs in MEC, obviating its expected benefits from MEC. This paper is the first to describe this new contention point in mobile networks and its potentially crippling impact on the achievable latency benefit from MEC. We propose ShapeShifter, a new component in the MEC architecture which solves the MEC latency contention problem through adaptive latency-centric burst management of MEC flows. ShapeShifter is effective - it eliminates SLO violations for latency-critical applications and improves application performance in multi-tenant scenarios by up to 3.8 x – and practical – it can be deployed with minimal disruption to the current mobile network ecosystem.

Mobile edge computing (MEC) can transform mobile networks into a new infrastructure tier for services requiring low response times, such as those providing content to emerging AR/VR, autonomous driving, and other types of applications. To be successful, the CDNs operating in this MEC infrastructure tier MEC-CDNs will need to ensure end user applications gain access to a cache server in a fast and accurate manner. This paper sheds light on the challenges that the current mobile DNS architecture poses toward achieving this goal, and presents ideas on how to re-architect the existing DNS architecture to enable CDNs to provide low-latency content delivery from the edge.

End users wanting to do more and more with mobile apps has led to explosive growth in the number of available apps. This has widened the gap between developers making apps available and end users being able to install all the apps they want on their device. To address this, Google introduced Instant Apps for Android where users can access selective app features on demand without having to download and install entire apps. But this requires developers to refactor apps and limits the apps' functionality.

At the core of the value proposition of edge computing is the ability to put computation close enough to the data sources, on demand. However, the data sources, computational infrastructure and software services needed to come together to power emerging and future edge computing applications are fragmented across different stakeholders, each with their own incentives, policies, and constraints on resources they can afford. This fragmentation limits the ability of edge computing to guarantee to applications and data the edge which will deliver the desired benefit. In this paper, we present our vision for an Edge Exchange, a decentralized directory service for a multi-stakeholder edge, as a path forward to enabling applications to be deployed across the best available edge resources, while still providing each stakeholder with controls regarding their resource use and sharing policies.

End user experiences on mobile devices with their rich sets of sensors are constrained by limited device battery lives and restricted form factors, as well as by the `scope' of the data available locally. The `Personal Cloud' distributed software abstractions address these issues by enhancing the capabilities of a mobile device via seamless use of both nearby and remote cloud resources. In contrast to vendor-specific, middleware-based cloud solutions, Personal Cloud instances are created at hypervisor-level, to create for each end user the federation of networked resources best suited for the current environment and use. Specifically, the Cirrostratus extensions of the Xen hypervisor can federate a user's networked resources to establish a personal execution environment, governed by policies that go beyond evaluating network connectivity to also consider device ownership and access rights, the latter managed in a secure fashion via standard Social Network Services. Experimental evaluations with both Linux- and Android-based devices, and using Facebook as the SNS, show the approach capable of substantially augmenting a device's innate capabilities, improving application performance and the effective functionality seen by end users.