27 research outputs found
Derandomized Distributed Multi-resource Allocation with Little Communication Overhead
We study a class of distributed optimization problems for multiple shared
resource allocation in Internet-connected devices. We propose a derandomized
version of an existing stochastic additive-increase and multiplicative-decrease
(AIMD) algorithm. The proposed solution uses one bit feedback signal for each
resource between the system and the Internet-connected devices and does not
require inter-device communication. Additionally, the Internet-connected
devices do not compromise their privacy and the solution does not dependent on
the number of participating devices. In the system, each Internet-connected
device has private cost functions which are strictly convex, twice continuously
differentiable and increasing. We show empirically that the long-term average
allocations of multiple shared resources converge to optimal allocations and
the system achieves minimum social cost. Furthermore, we show that the proposed
derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm
and both the approaches provide approximately same solutions
MEC vs MCC: performance analysis of interactive and real-time applications
A evolução das redes de telecomunicações tem promovido o desenvolvimento de novas aplicações para dispositivos móveis. Algumas destas aplicações exigem requisitos computacionais e energéticos que vão para além das capacidades dos dispositivos móveis. Neste contexto, pode ser utilizada a arquitetura Mobile Cloud Computing (MCC), que permite executar as aplicações em datacenters na cloud e aliviar o processamento nos dispositivos móveis. No entanto, algumas aplicações mais exigentes, e.g. interativas e de tempo real, são mais sensíveis ao atraso no processamento e comunicação da informação. Para estas aplicações, a arquitetura Mobile Edge Computing (MEC) pode ser utilizada como uma tecnologia intermédia que disponibiliza recursos computacionais e de armazenamento a partir da periferia da rede. Este artigo apresenta um estudo que avalia o desempenho das arquiteturas MCC e MEC na execução de duas aplicações tomadas como representativas do espectro das aplicações interativas, de tempo real e de processamento intensivo: o Fluid e o FaceSwap. São apresentados resultados que permitem quantificar o desempenho destas arquiteturas em diferentes circunstâncias.Telecommunication networks evolution is driving the development of new applications for mobile devices. Some of these applications are resource-intensive and push computational and energy demands of mobile devices beyond the mobile hardware capabilities. In this context, Mobile Cloud Computing (MCC) architecture emerges as a solution for offloading mobile devices that allows to execute these applications in cloud datacenters thus reducing the processing demand in mobile devices. However, more demanding applications, e.g. interactive and real-time applications, are sensitive to processing and communications delay. For these applications, Mobile Edge Computing (MEC) can be used as an intermediary technology, providing computing and storage resources in the network edge. This paper presents a study carried out to evaluate the performance of MEC and MCC architectures when executing two applications, Fluid and FaceSwap, representative of real time and computing intensive applications. A set of scenarios were designed to quantify the performance of these architectures in different settings.info:eu-repo/semantics/publishedVersio
Coordinated Container Migration and Base Station Handover in Mobile Edge Computing
Offloading computationally intensive tasks from mobile users (MUs) to a
virtualized environment such as containers on a nearby edge server, can
significantly reduce processing time and hence end-to-end (E2E) delay. However,
when users are mobile, such containers need to be migrated to other edge
servers located closer to the MUs to keep the E2E delay low. Meanwhile, the
mobility of MUs necessitates handover among base stations in order to keep the
wireless connections between MUs and base stations uninterrupted. In this
paper, we address the joint problem of container migration and base-station
handover by proposing a coordinated migration-handover mechanism, with the
objective of achieving low E2E delay and minimizing service interruption. The
mechanism determines the optimal destinations and time for migration and
handover in a coordinated manner, along with a delta checkpoint technique that
we propose. We implement a testbed edge computing system with our proposed
coordinated migration-handover mechanism, and evaluate the performance using
real-world applications implemented with Docker container (an
industry-standard). The results demonstrate that our mechanism achieves 30%-40%
lower service downtime and 13%-22% lower E2E delay as compared to other
mechanisms. Our work is instrumental in offering smooth user experience in
mobile edge computing.Comment: 6 pages. Accepted for presentation at the IEEE Global Communications
Conference (Globecom), Taipei, Taiwan, Dec. 202