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.

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.

Petascale plasma physics simulations have recently entered the regime of simulating trillions of particles. These unprecedented simulations generate massive amounts of data, posing significant challenges in storage, analysis, and visualization. In this paper, we present parallel I/O, analysis, and visualization results from a VPIC trillion particle simulation running on 120,000 cores, which produces ~30TB of data for a single timestep. We demonstrate the successful application of H5Part, a particle data extension of parallel HDF5, for writing the dataset at a significant fraction of system peak I/O rates. To enable efficient analysis, we develop hybrid parallel FastQuery to index and query data using multi-core CPUs on distributed memory hardware. We show good scalability results for the FastQuery implementation using up to 10,000 cores. Finally, we apply this indexing/query-driven approach to facilitate the first-ever analysis and visualization of the trillion particle dataset.