158 research outputs found
Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks
The proliferation of innovative mobile services such as augmented reality,
networked gaming, and autonomous driving has spurred a growing need for
low-latency access to computing resources that cannot be met solely by existing
centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an
effective solution to meet the demand for low-latency services by enabling the
execution of computing tasks at the network-periphery, in proximity to
end-users. While a number of recent studies have addressed the problem of
determining the execution of service tasks and the routing of user requests to
corresponding edge servers, the focus has primarily been on the efficient
utilization of computing resources, neglecting the fact that non-trivial
amounts of data need to be stored to enable service execution, and that many
emerging services exhibit asymmetric bandwidth requirements. To fill this gap,
we study the joint optimization of service placement and request routing in
MEC-enabled multi-cell networks with multidimensional
(storage-computation-communication) constraints. We show that this problem
generalizes several problems in literature and propose an algorithm that
achieves close-to-optimal performance using randomized rounding. Evaluation
results demonstrate that our approach can effectively utilize the available
resources to maximize the number of requests served by low-latency edge cloud
servers.Comment: IEEE Infocom 201
Hyperprofile-based Computation Offloading for Mobile Edge Networks
In recent studies, researchers have developed various computation offloading
frameworks for bringing cloud services closer to the user via edge networks.
Specifically, an edge device needs to offload computationally intensive tasks
because of energy and processing constraints. These constraints present the
challenge of identifying which edge nodes should receive tasks to reduce
overall resource consumption. We propose a unique solution to this problem
which incorporates elements from Knowledge-Defined Networking (KDN) to make
intelligent predictions about offloading costs based on historical data. Each
server instance can be represented in a multidimensional feature space where
each dimension corresponds to a predicted metric. We compute features for a
"hyperprofile" and position nodes based on the predicted costs of offloading a
particular task. We then perform a k-Nearest Neighbor (kNN) query within the
hyperprofile to select nodes for offloading computation. This paper formalizes
our hyperprofile-based solution and explores the viability of using machine
learning (ML) techniques to predict metrics useful for computation offloading.
We also investigate the effects of using different distance metrics for the
queries. Our results show various network metrics can be modeled accurately
with regression, and there are circumstances where kNN queries using Euclidean
distance as opposed to rectilinear distance is more favorable.Comment: 5 pages, NSF REU Site publicatio
POEM: Pricing Longer for Edge Computing in the Device Cloud
Multiple access mobile edge computing has been proposed as a promising
technology to bring computation services close to end users, by making good use
of edge cloud servers. In mobile device clouds (MDC), idle end devices may act
as edge servers to offer computation services for busy end devices. Most
existing auction based incentive mechanisms in MDC focus on only one round
auction without considering the time correlation. Moreover, although existing
single round auctions can also be used for multiple times, users should trade
with higher bids to get more resources in the cascading rounds of auctions,
then their budgets will run out too early to participate in the next auction,
leading to auction failures and the whole benefit may suffer. In this paper, we
formulate the computation offloading problem as a social welfare optimization
problem with given budgets of mobile devices, and consider pricing longer of
mobile devices. This problem is a multiple-choice multi-dimensional 0-1
knapsack problem, which is a NP-hard problem. We propose an auction framework
named MAFL for long-term benefits that runs a single round resource auction in
each round. Extensive simulation results show that the proposed auction
mechanism outperforms the single round by about 55.6% on the revenue on average
and MAFL outperforms existing double auction by about 68.6% in terms of the
revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on
Algorithms and Architectures for Parallel Processing (ICA3PP
A Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing
The In-Network Computing (COIN) paradigm is a promising solution that
leverages unused network resources to perform some tasks to meet up with
computation-demanding applications, such as metaverse. In this vein, we
consider the metaverse partial computation offloading problem for multiple
subtasks in a COIN environment to minimise energy consumption and delay while
dynamically adjusting the offloading policy based on the changing computation
resources status. We prove that the problem is NP and thus transformed it into
two subproblems: task splitting problem (TSP) on the user side and task
offloading problem (TOP) on the COIN side. We modelled the TSP as an ordinal
potential game (OPG) and proposed a decentralised algorithm to obtain its Nash
Equilibrium (NE). Then, we model the TOP as Markov Decision Process (MDP)
proposed double deep Q-network (DDQN) to solve for the optimal offloading
policy. Unlike the conventional DDQN algorithm, where intelligent agents sample
offloading decisions randomly within a certain probability, our COIN agent
explores the NE of the TSP and the deep neural network. Finally, simulation
results show that our proposed model approach allows the COIN agent to update
its policies and make more informed decisions, leading to improved performance
over time compared to the traditional baseline.Comment: 14 pages, 9 figure
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