We investigate the issue of deploying applications on a set of loosely coupled compute devices, connected through a wireless mesh, typical in community networks. Wireless mesh networks experience significant temporal and spatial variations in link bandwidth. When application components, modeled as a directed acyclic graph, need to be scheduled on such a mesh with bandwidth constraints (and variations), the problem of mapping components to specific compute nodes becomes an instance of bin packing with constraints of CPU, memory, and bandwidth limits within the mesh. To make the scheduling tractable, we propose BASS (Bandwidth Aware Scheduling System), and develop heuristics for scheduling, based on the directed graph topology of the application components. We evaluate BASS on an emulated mesh using bandwidth traces collected from an actual wireless testbed - CityLab. Detailed evaluations show that contemporary orchestration frameworks can plug in BASS to provide better end-to-end performance for the applications deployed on the mesh while reducing resource utilization.

On-demand video contributes a large fraction of the data traffic on mobile networks. This share is expected to increase even more drastically in the coming years. While the cellular infrastructure is continuously evolving to keep pace with this increasing demand, it is necessary to ensure that sufficient bandwidth is reserved for other latency-sensitive realtime applications like video conferencing and multiplayer video games. A tangible approach involves reducing on-demand video load on cellular networks, especially from users on the move. We see an opportunity for cellular load reduction using edge nodes based on two observations: (1) video streaming is mostly a download-only operation with sequential data access; and (2) short-range mmWave links can deliver an extremely high throughput for nearby recipients of data. The knowledge of the user's planned travel route creates opportunities for prescient prefetching and delivering the content as the vehicle passes through just in time, using mmWave devices on en route edge nodes. ClairvoyantEdge is a novel networked system infrastructure that leverages inter-edge node communication and the knowledge of users' trajectories to plan and deliver buffered video segments to the vehicles passing by. To evaluate ClairvoyantEdge, we built a comprehensive end-to-end emulation-based workflow that incorporates in situ field measurements of mmWave links into our own homegrown emulation framework. With a minuscule 0.12% coverage of a 46km2geographical area employing 20 edge nodes distributed in that area providing short-range mmWave access to passing vehicles, we achieve an average reduction of up to 21% in cellular bandwidth usage for video downloads, using a real-world workload comprising 758 vehicles. Our results validate the promise of ClairvoyantEdge for incorporation in future edge infrastructure evolution.

Abstract Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud‐native business models that leverage containerized multi‐tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time‐sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of theReserved Instance Allocation Problem(RIAP), which we first prove to be NP‐hard. We then design a heuristic‐based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic‐based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit‐to‐investment ratio for a broker who seeks to operate a commerce of virtual machines with time‐sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost‐sensitive fulfillment of bare metal provisioning requests in a time‐sensitive manner.

Scheduling computational jobs with data-sets dependencies is an important challenge of edge computing infrastructures. Although several strategies have been proposed, they have been evaluated through ad-hoc simulator extensions that are, when available, usually not maintained. This is a critical problem because it prevents researchers to -easily- perform fair comparisons between different proposals. In this paper, we propose to address this limitation by presenting a simulation engine dedicated to the evaluation and comparison of scheduling and data movement policies for edge computing use-cases. Built upon the Batsim/SimGrid toolkit, our tool includes an injector that allows the simulator to replay a series of events captured in real infrastructures. It also includes a controller that supervises storage entities and data transfers during the simulation, and a plug-in system that allows researchers to add new models to cope with the diversity of edge computing devices. We demonstrate the relevance of such a simulation toolkit by studying two scheduling strategies with four data movement policies on top of a simulated version of the Qarnot Computing platform, a production edge infrastructure based on smart heaters. We chose this use-case as it illustrates the heterogeneity as well as the uncertainties of edge infrastructures. Our ultimate goal is to gather industry and academics around a common simulator so that efforts made by one group can be factorised by others.