13,805 research outputs found
Live Prefetching for Mobile Computation Offloading
The conventional designs of mobile computation offloading fetch user-specific
data to the cloud prior to computing, called offline prefetching. However, this
approach can potentially result in excessive fetching of large volumes of data
and cause heavy loads on radio-access networks. To solve this problem, the
novel technique of live prefetching is proposed in this paper that seamlessly
integrates the task-level computation prediction and prefetching within the
cloud-computing process of a large program with numerous tasks. The technique
avoids excessive fetching but retains the feature of leveraging prediction to
reduce the program runtime and mobile transmission energy. By modeling the
tasks in an offloaded program as a stochastic sequence, stochastic optimization
is applied to design fetching policies to minimize mobile energy consumption
under a deadline constraint. The policies enable real-time control of the
prefetched-data sizes of candidates for future tasks. For slow fading, the
optimal policy is derived and shown to have a threshold-based structure,
selecting candidate tasks for prefetching and controlling their prefetched data
based on their likelihoods. The result is extended to design close-to-optimal
prefetching policies to fast fading channels. Compared with fetching without
prediction, live prefetching is shown theoretically to always achieve reduction
on mobile energy consumption.Comment: To appear in IEEE Trans. on Wireless Communicatio
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
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
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
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