1,479 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
A Survey on UAV-enabled Edge Computing: Resource Management Perspective
Edge computing facilitates low-latency services at the network's edge by
distributing computation, communication, and storage resources within the
geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent
advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new
opportunities for edge computing in military operations, disaster response, or
remote areas where traditional terrestrial networks are limited or unavailable.
In such environments, UAVs can be deployed as aerial edge servers or relays to
facilitate edge computing services. This form of computing is also known as
UAV-enabled Edge Computing (UEC), which offers several unique benefits such as
mobility, line-of-sight, flexibility, computational capability, and
cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices
are typically very limited in the context of UEC. Efficient resource management
is, therefore, a critical research challenge in UEC. In this article, we
present a survey on the existing research in UEC from the resource management
perspective. We identify a conceptual architecture, different types of
collaborations, wireless communication models, research directions, key
techniques and performance indicators for resource management in UEC. We also
present a taxonomy of resource management in UEC. Finally, we identify and
discuss some open research challenges that can stimulate future research
directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU
Computation Offloading and Scheduling in Edge-Fog Cloud Computing
Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
With the rapid development of Mobile Edge Computing (MEC), various real-time
applications have been deployed to benefit people's daily lives. The
performance of these applications relies heavily on the freshness of collected
environmental information, which can be quantified by its Age of Information
(AoI). In the traditional definition of AoI, it is assumed that the status
information can be actively sampled and directly used. However, for many
MEC-enabled applications, the desired status information is updated in an
event-driven manner and necessitates data processing. To better serve these
applications, we propose a new definition of AoI and, based on the redefined
AoI, we formulate an online AoI minimization problem for MEC systems. Notably,
the problem can be interpreted as a Markov Decision Process (MDP), thus
enabling its solution through Reinforcement Learning (RL) algorithms.
Nevertheless, the traditional RL algorithms are designed for MDPs with
completely unknown system dynamics and hence usually suffer long convergence
times. To accelerate the learning process, we introduce Post-Decision States
(PDSs) to exploit the partial knowledge of the system's dynamics. We also
combine PDSs with deep RL to further improve the algorithm's applicability,
scalability, and robustness. Numerical results demonstrate that our algorithm
outperforms the benchmarks under various scenarios
Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach
Exploiting the computational heterogeneity of mobile devices and edge nodes,
mobile edge computation (MEC) provides an efficient approach to achieving
real-time applications that are sensitive to information freshness, by
offloading tasks from mobile devices to edge nodes. We use the metric
Age-of-Information (AoI) to evaluate information freshness. An efficient
solution to minimize the AoI for the MEC system with multiple users is
non-trivial to obtain due to the random computing time. In this paper, we
consider multiple users offloading tasks to heterogeneous edge servers in a MEC
system. We first reformulate the problem as a Restless Multi-Arm-Bandit (RMAB)
problem and establish a hierarchical Markov Decision Process (MDP) to
characterize the updating of AoI for the MEC system. Based on the hierarchical
MDP, we propose a nested index framework and design a nested index policy with
provably asymptotic optimality. Finally, the closed form of the nested index is
obtained, which enables the performance tradeoffs between computation
complexity and accuracy. Our algorithm leads to an optimality gap reduction of
up to 40%, compared to benchmarks. Our algorithm asymptotically approximates
the lower bound as the system scalar gets large enough
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