425 research outputs found
Socially Trusted Collaborative Edge Computing in Ultra Dense Networks
Small cell base stations (SBSs) endowed with cloud-like computing
capabilities are considered as a key enabler of edge computing (EC), which
provides ultra-low latency and location-awareness for a variety of emerging
mobile applications and the Internet of Things. However, due to the limited
computation resources of an individual SBS, providing computation services of
high quality to its users faces significant challenges when it is overloaded
with an excessive amount of computation workload. In this paper, we propose
collaborative edge computing among SBSs by forming SBS coalitions to share
computation resources with each other, thereby accommodating more computation
workload in the edge system and reducing reliance on the remote cloud. A novel
SBS coalition formation algorithm is developed based on the coalitional game
theory to cope with various new challenges in small-cell-based edge systems,
including the co-provisioning of radio access and computing services,
cooperation incentives, and potential security risks. To address these
challenges, the proposed method (1) allows collaboration at both the user-SBS
association stage and the SBS peer offloading stage by exploiting the ultra
dense deployment of SBSs, (2) develops a payment-based incentive mechanism that
implements proportionally fair utility division to form stable SBS coalitions,
and (3) builds a social trust network for managing security risks among SBSs
due to collaboration. Systematic simulations in practical scenarios are carried
out to evaluate the efficacy and performance of the proposed method, which
shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author
Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning
Shared edge computing platforms, which enable Application Service Providers
(ASPs) to deploy applications in close proximity to mobile users are providing
ultra-low latency and location-awareness to a rich portfolio of services.
Though ubiquitous edge service provisioning, i.e., deploying the application at
all possible edge sites, is always preferable, it is impractical due to often
limited operational budget of ASPs. In this case, an ASP has to cautiously
decide where to deploy the edge service and how much budget it is willing to
use. A central issue here is that the service demand received by each edge
site, which is the key factor of deploying benefit, is unknown to ASPs a
priori. What's more complicated is that this demand pattern varies temporally
and spatially across geographically distributed edge sites. In this paper, we
investigate an edge resource rental problem where the ASP learns service demand
patterns for individual edge sites while renting computation resource at these
sites to host its applications for edge service provisioning. An online
algorithm, called Context-aware Online Edge Resource Rental (COERR), is
proposed based on the framework of Contextual Combinatorial Multi-armed Bandit
(CC-MAB). COERR observes side-information (context) to learn the demand
patterns of edge sites and decides rental decisions (including where to rent
the computation resource and how much to rent) to maximize ASP's utility given
a limited budget. COERR provides a provable performance achieving sublinear
regret compared to an Oracle algorithm that knows exactly the expected service
demand of edge sites. Experiments are carried out on a real-world dataset and
the results show that COERR significantly outperforms other benchmarks
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Mobile edge computing (a.k.a. fog computing) has recently emerged to enable
in-situ processing of delay-sensitive applications at the edge of mobile
networks. Providing grid power supply in support of mobile edge computing,
however, is costly and even infeasible (in certain rugged or under-developed
areas), thus mandating on-site renewable energy as a major or even sole power
supply in increasingly many scenarios. Nonetheless, the high intermittency and
unpredictability of renewable energy make it very challenging to deliver a high
quality of service to users in energy harvesting mobile edge computing systems.
In this paper, we address the challenge of incorporating renewables into mobile
edge computing and propose an efficient reinforcement learning-based resource
management algorithm, which learns on-the-fly the optimal policy of dynamic
workload offloading (to the centralized cloud) and edge server provisioning to
minimize the long-term system cost (including both service delay and
operational cost). Our online learning algorithm uses a decomposition of the
(offline) value iteration and (online) reinforcement learning, thus achieving a
significant improvement of learning rate and run-time performance when compared
to standard reinforcement learning algorithms such as Q-learning. We prove the
convergence of the proposed algorithm and analytically show that the learned
policy has a simple monotone structure amenable to practical implementation.
Our simulation results validate the efficacy of our algorithm, which
significantly improves the edge computing performance compared to fixed or
myopic optimization schemes and conventional reinforcement learning algorithms.Comment: arXiv admin note: text overlap with arXiv:1701.01090 by other author
Spatio-temporal Edge Service Placement: A Bandit Learning Approach
Shared edge computing platforms deployed at the radio access network are
expected to significantly improve quality of service delivered by Application
Service Providers (ASPs) in a flexible and economic way. However, placing edge
service in every possible edge site by an ASP is practically infeasible due to
the ASP's prohibitive budget requirement. In this paper, we investigate the
edge service placement problem of an ASP under a limited budget, where the ASP
dynamically rents computing/storage resources in edge sites to host its
applications in close proximity to end users. Since the benefit of placing edge
service in a specific site is usually unknown to the ASP a priori, optimal
placement decisions must be made while learning this benefit. We pose this
problem as a novel combinatorial contextual bandit learning problem. It is
"combinatorial" because only a limited number of edge sites can be rented to
provide the edge service given the ASP's budget. It is "contextual" because we
utilize user context information to enable finer-grained learning and decision
making. To solve this problem and optimize the edge computing performance, we
propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore,
SEEN is extended to scenarios with overlapping service coverage by
incorporating a disjunctively constrained knapsack problem. In both cases, we
prove that our algorithm achieves a sublinear regret bound when it is compared
to an oracle algorithm that knows the exact benefit information. Simulations
are carried out on a real-world dataset, whose results show that SEEN
significantly outperforms benchmark solutions
Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks
Mobile Edge Computing (MEC) pushes computing functionalities away from the
centralized cloud to the network edge, thereby meeting the latency requirements
of many emerging mobile applications and saving backhaul network bandwidth.
Although many existing works have studied computation offloading policies,
service caching is an equally, if not more important, design topic of MEC, yet
receives much less attention. Service caching refers to caching application
services and their related databases/libraries in the edge server (e.g.
MEC-enabled BS), thereby enabling corresponding computation tasks to be
executed. Because only a small number of application services can be cached in
resource-limited edge server at the same time, which services to cache has to
be judiciously decided to maximize the edge computing performance. In this
paper, we investigate the extremely compelling but much less studied problem of
dynamic service caching in MEC-enabled dense cellular networks. We propose an
efficient online algorithm, called OREO, which jointly optimizes dynamic
service caching and task offloading to address a number of key challenges in
MEC systems, including service heterogeneity, unknown system dynamics, spatial
demand coupling and decentralized coordination. Our algorithm is developed
based on Lyapunov optimization and Gibbs sampling, works online without
requiring future information, and achieves provable close-to-optimal
performance. Simulation results show that our algorithm can effectively reduce
computation latency for end users while keeping energy consumption low
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
This paper studies a federated learning (FL) system, where \textit{multiple}
FL services co-exist in a wireless network and share common wireless resources.
It fills the void of wireless resource allocation for multiple simultaneous FL
services in the existing literature. Our method designs a two-level resource
allocation framework comprising \emph{intra-service} resource allocation and
\emph{inter-service} resource allocation. The intra-service resource allocation
problem aims to minimize the length of FL rounds by optimizing the bandwidth
allocation among the clients of each FL service. Based on this, an
inter-service resource allocation problem is further considered, which
distributes bandwidth resources among multiple simultaneous FL services. We
consider both cooperative and selfish providers of the FL services. For
cooperative FL service providers, we design a distributed bandwidth allocation
algorithm to optimize the overall performance of multiple FL services,
meanwhile cater to the fairness among FL services and the privacy of clients.
For selfish FL service providers, a new auction scheme is designed with the FL
service owners as the bidders and the network provider as the auctioneer. The
designed auction scheme strikes a balance between the overall FL performance
and fairness. Our simulation results show that the proposed algorithms
outperform other benchmarks under various network conditions
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