221,200 research outputs found
Edge-Caching Wireless Networks: Performance Analysis and Optimization
Edge-caching has received much attention as an efficient technique to reduce
delivery latency and network congestion during peak-traffic times by bringing
data closer to end users. Existing works usually design caching algorithms
separately from physical layer design. In this paper, we analyse edge-caching
wireless networks by taking into account the caching capability when designing
the signal transmission. Particularly, we investigate multi-layer caching where
both base station (BS) and users are capable of storing content data in their
local cache and analyse the performance of edge-caching wireless networks under
two notable uncoded and coded caching strategies. Firstly, we propose a coded
caching strategy that is applied to arbitrary values of cache size. The
required backhaul and access rates are derived as a function of the BS and user
cache size. Secondly, closed-form expressions for the system energy efficiency
(EE) corresponding to the two caching methods are derived. Based on the derived
formulas, the system EE is maximized via precoding vectors design and
optimization while satisfying a predefined user request rate. Thirdly, two
optimization problems are proposed to minimize the content delivery time for
the two caching strategies. Finally, numerical results are presented to verify
the effectiveness of the two caching methods.Comment: to appear in IEEE Trans. Wireless Commu
Achlys : Towards a framework for distributed storage and generic computing applications for wireless IoT edge networks with Lasp on GRiSP
Internet of Things (IoT) has gained substantial attention over the past
years. And the main discussion has been how to process the amount of data that
it generates which has lead to the edge computing paradigm. Wether it is called
fog1, edge or mist, the principle remains that cloud services must become
available closer to clients. This documents presents ongoing work on future
edge systems that are built to provide steadfast IoT services to users by
bringing storage and processing power closer to peripheral parts of networks.
Designing such infrastructures is becoming much more challenging as the number
of IoT devices keeps growing. Production grade deployments have to meet very
high performance requirements, and end-to-end solutions involve significant
investments. In this paper, we aim at providing a solution to extend the range
of the edge model to the very farthest nodes in the network. Specifically, we
focus on providing reliable storage and computation capabilities immediately on
wireless IoT sensor nodes. This extended edge model will allow end users to
manage their IoT ecosystem without forcibly relying on gateways or Internet
provider solutions. In this document, we introduce Achlys, a prototype
implementation of an edge node that is a concrete port of the Lasp programming
library on the GRiSP Erlang embedded system. This way, we aim at addressing the
need for a general purpose edge that is both resilient and consistent in terms
of storage and network. Finally, we study example use cases that could take
advantage of integrating the Achlys framework and discuss future work for the
latter.Comment: 7 page
EdgeAISim: A Toolkit for Simulation and Modelling of AI Models in Edge Computing Environments
To meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios
Shortest Path versus Multi-Hub Routing in Networks with Uncertain Demand
We study a class of robust network design problems motivated by the need to
scale core networks to meet increasingly dynamic capacity demands. Past work
has focused on designing the network to support all hose matrices (all matrices
not exceeding marginal bounds at the nodes). This model may be too conservative
if additional information on traffic patterns is available. Another extreme is
the fixed demand model, where one designs the network to support peak
point-to-point demands. We introduce a capped hose model to explore a broader
range of traffic matrices which includes the above two as special cases. It is
known that optimal designs for the hose model are always determined by
single-hub routing, and for the fixed- demand model are based on shortest-path
routing. We shed light on the wider space of capped hose matrices in order to
see which traffic models are more shortest path-like as opposed to hub-like. To
address the space in between, we use hierarchical multi-hub routing templates,
a generalization of hub and tree routing. In particular, we show that by adding
peak capacities into the hose model, the single-hub tree-routing template is no
longer cost-effective. This initiates the study of a class of robust network
design (RND) problems restricted to these templates. Our empirical analysis is
based on a heuristic for this new hierarchical RND problem. We also propose
that it is possible to define a routing indicator that accounts for the
strengths of the marginals and peak demands and use this information to choose
the appropriate routing template. We benchmark our approach against other
well-known routing templates, using representative carrier networks and a
variety of different capped hose traffic demands, parameterized by the relative
importance of their marginals as opposed to their point-to-point peak demands
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