400 research outputs found
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
Offloading Safety- and Mission-Critical Tasks via Unreliable Connections
For many cyber-physical systems, e.g., IoT systems and autonomous vehicles, offloading workload to auxiliary processing units has become crucial. However, since this approach highly depends on network connectivity and responsiveness, typically only non-critical tasks are offloaded, which have less strict timing requirements than critical tasks. In this work, we provide two protocols allowing to offload critical and non-critical tasks likewise, while providing different service levels for non-critical tasks in the event of an unsuccessful offloading operation, depending on the respective system requirements. We analyze the worst-case timing behavior of the local cyber-physical system and, based on these analyses, we provide a sufficient schedulability test for each of the proposed protocols. In the course of comprehensive experiments, we show that our protocols have reasonable acceptance ratios under the provided schedulability tests. Moreover, we demonstrate that the system behavior under our proposed protocols is strongly dependent on probability of unsuccessful offloading operations, the percentage of critical tasks in the system, and the amount of offloaded workload
Age of Information of Multi-user Mobile Edge Computing Systems
In this paper, we analyze the average age of information (AoI) and the
average peak AoI (PAoI) of a multiuser mobile edge computing (MEC) system where
a base station (BS) generates and transmits computation-intensive packets to
user equipments (UEs). In this MEC system, we focus on three computing schemes:
(i) The local computing scheme where all computational tasks are computed by
the local server at the UE, (ii) The edge computing scheme where all
computational tasks are computed by the edge server at the BS, and (iii) The
partial computing scheme where computational tasks are partially allocated at
the edge server and the rest are computed by the local server. Considering
exponentially distributed transmission time and computation time and adopting
the first come first serve (FCFS) queuing policy, we derive closed-form
expressions for the average AoI and average PAoI. To address the complexity of
the average AoI expression, we derive simple upper and lower bounds on the
average AoI, which allow us to explicitly examine the dependence of the optimal
offloading decision on the MEC system parameters. Aided by simulation results,
we verify our analysis and illustrate the impact of system parameters on the
AoI performance
DAG-based Task Orchestration for Edge Computing
As we increase the number of personal computing devices that we carry (mobile
devices, tablets, e-readers, and laptops) and these come equipped with
increasing resources, there is a vast potential computation power that can be
utilized from those devices. Edge computing promises to exploit these
underlying computation resources closer to users to help run latency-sensitive
applications such as augmented reality and video analytics. However, one key
missing piece has been how to incorporate personally owned unmanaged devices
into a usable edge computing system. The primary challenges arise due to the
heterogeneity, lack of interference management, and unpredictable availability
of such devices. In this paper we propose an orchestration framework IBDASH,
which orchestrates application tasks on an edge system that comprises a mix of
commercial and personal edge devices. IBDASH targets reducing both end-to-end
latency of execution and probability of failure for applications that have
dependency among tasks, captured by directed acyclic graphs (DAGs). IBDASH
takes memory constraints of each edge device and network bandwidth into
consideration. To assess the effectiveness of IBDASH, we run real application
tasks on real edge devices with widely varying capabilities.We feed these
measurements into a simulator that runs IBDASH at scale. Compared to three
state-of-the-art edge orchestration schemes, LAVEA, Petrel, and LaTS, and two
intuitive baselines, IBDASH reduces the end-to-end latency and probability of
failure, by 14% and 41% on average respectively. The main takeaway from our
work is that it is feasible to combine personal and commercial devices into a
usable edge computing platform, one that delivers low latency and predictable
and high availability
Offloading Real-Time Tasks in IIoT Environments under Consideration of Networking Uncertainties
Offloading is a popular way to overcome the resource and power constraints of
networked embedded devices, which are increasingly found in industrial
environments. It involves moving resource-intensive computational tasks to a
more powerful device on the network, often in close proximity to enable
wireless communication. However, many Industrial Internet of Things (IIoT)
applications have real-time constraints. Offloading such tasks over a wireless
network with latency uncertainties poses new challenges.
In this paper, we aim to better understand these challenges by proposing a
system architecture and scheduler for real-time task offloading in wireless
IIoT environments. Based on a prototype, we then evaluate different system
configurations and discuss their trade-offs and implications. Our design showed
to prevent deadline misses under high load and network uncertainties and was
able to outperform a reference scheduler in terms of successful task
throughput. Under heavy task load, where the reference scheduler had a success
rate of 5%, our design achieved a success rate of 60%.Comment: 2nd International Workshop on Middleware for the Edge (MiddleWEdge
'23). 2023. AC
Flying mobile edge computing towards 5G and beyond: an overview on current use cases and challenges
The increasing computational capacity of multiple devices, the advent of complex applications, and data generation create new challenges of scalability, ubiquity, and seamless services to meet the most diverse network demands and requirements, such as reliability, latency, battery lifetime. For this reason, the 5th Generation (5G) network comes to mitigate the most diverse challenges inherent to the current dynamic mobile networks and their increasing data rates. Unmanned Aerial Vehicles (UAVs) have also been considered as communication relays or mobile base stations to assist mobile users with limited or no available wireless infrastructure. They can provide connections for mobile users in hard-to-reach areas, replacing damaged or overloaded ground infrastructure and working as mobile clouds, providing low but increasing computational power. However, the feasibility of a Flying Edge Computing requires special attention in terms of resource allocation techniques, cooperation with existing ground units and among multiple UAVs, coordination with user mobility, computation efficiency, collision avoidance, and recharging approaches. Thus, the cooperation among UAVs and the current terrestrial Mobile Edge Computing can be relevant in some cases once the computation power of a single UAV might be insufficient. It is important to understand the feasibility of current proposals and establish new approaches that consider the usage of multiple UAVs and recharging approaches. In this paper we discuss the challenges of a 5G extended network through the help of UAVs. The proposed multi-tier architecture employs UAVs with different mobility models, providing support to ground nodes. Moreover, the support of the UAVs as edge nodes will also be evaluated.publishe
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