576 research outputs found
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
Mobile-edge computing (MEC) is an emerging paradigm to meet the
ever-increasing computation demands from mobile applications. By offloading the
computationally intensive workloads to the MEC server, the quality of
computation experience, e.g., the execution latency, could be greatly improved.
Nevertheless, as the on-device battery capacities are limited, computation
would be interrupted when the battery energy runs out. To provide satisfactory
computation performance as well as achieving green computing, it is of
significant importance to seek renewable energy sources to power mobile devices
via energy harvesting (EH) technologies. In this paper, we will investigate a
green MEC system with EH devices and develop an effective computation
offloading strategy. The execution cost, which addresses both the execution
latency and task failure, is adopted as the performance metric. A
low-complexity online algorithm, namely, the Lyapunov optimization-based
dynamic computation offloading (LODCO) algorithm is proposed, which jointly
decides the offloading decision, the CPU-cycle frequencies for mobile
execution, and the transmit power for computation offloading. A unique
advantage of this algorithm is that the decisions depend only on the
instantaneous side information without requiring distribution information of
the computation task request, the wireless channel, and EH processes. The
implementation of the algorithm only requires to solve a deterministic problem
in each time slot, for which the optimal solution can be obtained either in
closed form or by bisection search. Moreover, the proposed algorithm is shown
to be asymptotically optimal via rigorous analysis. Sample simulation results
shall be presented to verify the theoretical analysis as well as validate the
effectiveness of the proposed algorithm.Comment: 33 pages, 11 figures, submitted to IEEE Journal on Selected Areas in
Communication
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
Mobile Edge Computation Offloading Using Game Theory and Reinforcement Learning
Due to the ever-increasing popularity of resource-hungry and
delay-constrained mobile applications, the computation and storage capabilities
of remote cloud has partially migrated towards the mobile edge, giving rise to
the concept known as Mobile Edge Computing (MEC). While MEC servers enjoy the
close proximity to the end-users to provide services at reduced latency and
lower energy costs, they suffer from limitations in computational and radio
resources, which calls for fair efficient resource management in the MEC
servers. The problem is however challenging due to the ultra-high density,
distributed nature, and intrinsic randomness of next generation wireless
networks. In this article, we focus on the application of game theory and
reinforcement learning for efficient distributed resource management in MEC, in
particular, for computation offloading. We briefly review the cutting-edge
research and discuss future challenges. Furthermore, we develop a
game-theoretical model for energy-efficient distributed edge server activation
and study several learning techniques. Numerical results are provided to
illustrate the performance of these distributed learning techniques. Also, open
research issues in the context of resource management in MEC servers are
discussed
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Mobile edge computing (MEC) emerges recently as a promising solution to
relieve resource-limited mobile devices from computation-intensive tasks, which
enables devices to offload workloads to nearby MEC servers and improve the
quality of computation experience. Nevertheless, by considering a MEC system
consisting of multiple mobile users with stochastic task arrivals and wireless
channels in this paper, the design of computation offloading policies is
challenging to minimize the long-term average computation cost in terms of
power consumption and buffering delay. A deep reinforcement learning (DRL)
based decentralized dynamic computation offloading strategy is investigated to
build a scalable MEC system with limited feedback. Specifically, a continuous
action space-based DRL approach named deep deterministic policy gradient (DDPG)
is adopted to learn efficient computation offloading policies independently at
each mobile user. Thus, powers of both local execution and task offloading can
be adaptively allocated by the learned policies from each user's local
observation of the MEC system. Numerical results are illustrated to demonstrate
that efficient policies can be learned at each user, and performance of the
proposed DDPG based decentralized strategy outperforms the conventional deep
Q-network (DQN) based discrete power control strategy and some other greedy
strategies with reduced computation cost. Besides, the power-delay tradeoff is
also analyzed for both the DDPG based and DQN based strategies
Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing
Scavenging the idling computation resources at the enormous number of mobile
devices can provide a powerful platform for local mobile cloud computing. The
vision can be realized by peer-to-peer cooperative computing between edge
devices, referred to as co-computing. This paper considers a co-computing
system where a user offloads computation of input-data to a helper. The helper
controls the offloading process for the objective of minimizing the user's
energy consumption based on a predicted helper's CPU-idling profile that
specifies the amount of available computation resource for co-computing.
Consider the scenario that the user has one-shot input-data arrival and the
helper buffers offloaded bits. The problem for energy-efficient co-computing is
formulated as two sub-problems: the slave problem corresponding to adaptive
offloading and the master one to data partitioning. Given a fixed offloaded
data size, the adaptive offloading aims at minimizing the energy consumption
for offloading by controlling the offloading rate under the deadline and buffer
constraints. By deriving the necessary and sufficient conditions for the
optimal solution, we characterize the structure of the optimal policies and
propose algorithms for computing the policies. Furthermore, we show that the
problem of optimal data partitioning for offloading and local computing at the
user is convex, admitting a simple solution using the sub-gradient method.
Last, the developed design approach for co-computing is extended to the
scenario of bursty data arrivals at the user accounting for data causality
constraints. Simulation results verify the effectiveness of the proposed
algorithms.Comment: Submitted to possible journa
Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions
Besides enabling an enhanced mobile broadband, next generation of mobile
networks (5G) are envisioned for the support of massive connectivity of
heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large
number of use-cases including smart cities, environment monitoring, smart
vehicles, etc. Unfortunately, most IoTs have very limited computing and storage
capabilities and need cloud services. Hence, connecting these devices through
5G systems requires huge spectrum resources in addition to handling the massive
connectivity and improved security. This article discusses the challenges
facing the support of IoTs through 5G systems. The focus is devoted to
discussing physical layer limitations in terms of spectrum resources and radio
access channel connectivity. We show how sparsity can be exploited for
addressing these challenges especially in terms of enabling wideband spectrum
management and handling the connectivity by exploiting device-to-device
communications and edge-cloud. Moreover, we identify major open problems and
research directions that need to be explored towards enabling the support of
massive heterogeneous IoTs through 5G systems.Comment: Accepted for publication in IEEE Wireless Communications Magazin
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (extended version)
Achieving long battery lives or even self sustainability has been a long
standing challenge for designing mobile devices. This paper presents a novel
solution that seamlessly integrates two technologies, mobile cloud computing
and microwave power transfer (MPT), to enable computation in passive
low-complexity devices such as sensors and wearable computing devices.
Specifically, considering a single-user system, a base station (BS) either
transfers power to or offloads computation from a mobile to the cloud; the
mobile uses harvested energy to compute given data either locally or by
offloading. A framework for energy efficient computing is proposed that
comprises a set of policies for controlling CPU cycles for the mode of local
computing, time division between MPT and offloading for the other mode of
offloading, and mode selection. Given the CPU-cycle statistics information and
channel state information (CSI), the policies aim at maximizing the probability
of successfully computing given data, called computing probability, under the
energy harvesting and deadline constraints. The policy optimization is
translated into the equivalent problems of minimizing the mobile energy
consumption for local computing and maximizing the mobile energy savings for
offloading which are solved using convex optimization theory. The structures of
the resultant policies are characterized in closed form. Furthermore, given
non-causal CSI, the said analytical framework is further developed to support
computation load allocation over multiple channel realizations, which further
increases computing probability. Last, simulation demonstrates the feasibility
of wirelessly powered mobile cloud computing and the gain of its optimal
control.Comment: double colum
Multiuser Computation Offloading and Downloading for Edge Computing with Virtualization
Mobile-edge computing (MEC) is an emerging technology for enhancing the
computational capabilities of mobile devices and reducing their energy
consumption via offloading complex computation tasks to the nearby servers.
Multiuser MEC at servers is widely realized via parallel computing based on
virtualization. Due to finite shared I/O resources, interference between
virtual machines (VMs), called I/O interference, degrades the computation
performance. In this paper, we study the problem of joint radio-and-computation
resource allocation (RCRA) in multiuser MEC systems in the presence of I/O
interference. Specifically, offloading scheduling algorithms are designed
targeting two system performance metrics: sum offloading throughput
maximization and sum mobile energy consumption minimization. Their designs are
formulated as non-convex mixed-integer programming problems, which account for
latency due to offloading, result downloading and parallel computing. A set of
low-complexity algorithms are designed based on a decomposition approach and
leveraging classic techniques from combinatorial optimization. The resultant
algorithms jointly schedule offloading users, control their offloading sizes,
and divide time for communication (offloading and downloading) and computation.
They are either optimal or can achieve close-to-optimality as shown by
simulation. Comprehensive simulation results demonstrate considering of I/O
interference can endow on an offloading controller robustness against the
performance-degradation factor
Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading (Extended Version)
Mobile-edge computation offloading (MECO) offloads intensive mobile
computation to clouds located at the edges of cellular networks. Thereby, MECO
is envisioned as a promising technique for prolonging the battery lives and
enhancing the computation capacities of mobiles. In this paper, we study
resource allocation for a multiuser MECO system based on time-division multiple
access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First,
for the TDMA MECO system with infinite or finite computation capacity, the
optimal resource allocation is formulated as a convex optimization problem for
minimizing the weighted sum mobile energy consumption under the constraint on
computation latency. The optimal policy is proved to have a threshold-based
structure with respect to a derived offloading priority function, which yields
priorities for users according to their channel gains and local computing
energy consumption. As a result, users with priorities above and below a given
threshold perform complete and minimum offloading, respectively. Moreover, for
the cloud with finite capacity, a sub-optimal resource-allocation algorithm is
proposed to reduce the computation complexity for computing the threshold.
Next, we consider the OFDMA MECO system, for which the optimal resource
allocation is formulated as a non-convex mixed-integer problem. To solve this
challenging problem and characterize its policy structure, a sub-optimal
low-complexity algorithm is proposed by transforming the OFDMA problem to its
TDMA counterpart. The corresponding resource allocation is derived by defining
an average offloading priority function and shown to have close-to-optimal
performance by simulation.Comment: Accepted to IEEE Trans. on Wireless Communicatio
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