18,781 research outputs found
Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing
Mobile edge computing is a new computing paradigm, which pushes cloud
computing capabilities away from the centralized cloud to the network edge.
However, with the sinking of computing capabilities, the new challenge incurred
by user mobility arises: since end-users typically move erratically, the
services should be dynamically migrated among multiple edges to maintain the
service performance, i.e., user-perceived latency. Tackling this problem is
non-trivial since frequent service migration would greatly increase the
operational cost. To address this challenge in terms of the performance-cost
trade-off, in this paper we study the mobile edge service performance
optimization problem under long-term cost budget constraint. To address user
mobility which is typically unpredictable, we apply Lyapunov optimization to
decompose the long-term optimization problem into a series of real-time
optimization problems which do not require a priori knowledge such as user
mobility. As the decomposed problem is NP-hard, we first design an
approximation algorithm based on Markov approximation to seek a near-optimal
solution. To make our solution scalable and amenable to future 5G application
scenario with large-scale user devices, we further propose a distributed
approximation scheme with greatly reduced time complexity, based on the
technique of best response update. Rigorous theoretical analysis and extensive
evaluations demonstrate the efficacy of the proposed centralized and
distributed schemes.Comment: The paper is accepted by IEEE Journal on Selected Areas in
Communications, Aug. 201
Combining Trajectory Optimization, Supervised Machine Learning, and Model Structure for Mitigating the Curse of Dimensionality in the Control of Bipedal Robots
To overcome the obstructions imposed by high-dimensional bipedal models, we
embed a stable walking motion in an attractive low-dimensional surface of the
system's state space. The process begins with trajectory optimization to design
an open-loop periodic walking motion of the high-dimensional model and then
adding to this solution, a carefully selected set of additional open-loop
trajectories of the model that steer toward the nominal motion. A drawback of
trajectories is that they provide little information on how to respond to a
disturbance. To address this shortcoming, Supervised Machine Learning is used
to extract a low-dimensional state-variable realization of the open-loop
trajectories. The periodic orbit is now an attractor of the low-dimensional
state-variable model but is not attractive in the full-order system. We then
use the special structure of mechanical models associated with bipedal robots
to embed the low-dimensional model in the original model in such a manner that
the desired walking motions are locally exponentially stable. The design
procedure is first developed for ordinary differential equations and
illustrated on a simple model. The methods are subsequently extended to a class
of hybrid models and then realized experimentally on an Atrias-series 3D
bipedal robot.Comment: Paper was submitted to International Journal of Robotics Research
(IJRR) in Nov. 201
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Deep Visual Perception for Dynamic Walking on Discrete Terrain
Dynamic bipedal walking on discrete terrain, like stepping stones, is a
challenging problem requiring feedback controllers to enforce safety-critical
constraints. To enforce such constraints in real-world experiments, fast and
accurate perception for foothold detection and estimation is needed. In this
work, a deep visual perception model is designed to accurately estimate step
length of the next step, which serves as input to the feedback controller to
enable vision-in-the-loop dynamic walking on discrete terrain. In particular, a
custom convolutional neural network architecture is designed and trained to
predict step length to the next foothold using a sampled image preview of the
upcoming terrain at foot impact. The visual input is offered only at the
beginning of each step and is shown to be sufficient for the job of dynamically
stepping onto discrete footholds. Through extensive numerical studies, we show
that the robot is able to successfully autonomously walk for over 100 steps
without failure on a discrete terrain with footholds randomly positioned within
a step length range of 45-85 centimeters.Comment: Presented at Humanoids 201
Reinforcement Learning for Robotics and Control with Active Uncertainty Reduction
Model-free reinforcement learning based methods such as Proximal Policy
Optimization, or Q-learning typically require thousands of interactions with
the environment to approximate the optimum controller which may not always be
feasible in robotics due to safety and time consumption. Model-based methods
such as PILCO or BlackDrops, while data-efficient, provide solutions with
limited robustness and complexity. To address this tradeoff, we introduce
active uncertainty reduction-based virtual environments, which are formed
through limited trials conducted in the original environment. We provide an
efficient method for uncertainty management, which is used as a metric for
self-improvement by identification of the points with maximum expected
improvement through adaptive sampling. Capturing the uncertainty also allows
for better mimicking of the reward responses of the original system. Our
approach enables the use of complex policy structures and reward functions
through a unique combination of model-based and model-free methods, while still
retaining the data efficiency. We demonstrate the validity of our method on
several classic reinforcement learning problems in OpenAI gym. We prove that
our approach offers a better modeling capacity for complex system dynamics as
compared to established methods
A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
This paper presents a review of the literature on State Estimation (SE) in
power systems. While covering some works related to SE in transmission systems,
the main focus of this paper is Distribution System State Estimation (DSSE).
The paper discusses a few critical topics of DSSE, including mathematical
problem formulation, application of pseudo-measurements, metering instrument
placement, network topology issues, impacts of renewable penetration, and
cyber-security. Both conventional and modern data-driven and probabilistic
techniques have been reviewed. This paper can provide researchers and utility
engineers with insights into the technical achievements, barriers, and future
research directions of DSSE
Learning Manipulation Skills Via Hierarchical Spatial Attention
Learning generalizable skills in robotic manipulation has long been
challenging due to real-world sized observation and action spaces. One method
for addressing this problem is attention focus -- the robot learns where to
attend its sensors and irrelevant details are ignored. However, these methods
have largely not caught on due to the difficulty of learning a good attention
policy and the added partial observability induced by a narrowed window of
focus. This article addresses the first issue by constraining gazes to a
spatial hierarchy. For the second issue, we identify a case where the partial
observability induced by attention does not prevent Q-learning from finding an
optimal policy. We conclude with real-robot experiments on challenging
pick-place tasks demonstrating the applicability of the approach.Comment: IEEE Transactions on Robotics, March 2020. Video:
https://youtu.be/4dZ6WiDX3-s . Source code:
https://github.com/mgualti/Seq6DofMani
The Wireless Control Plane: An Overview and Directions for Future Research
Software-defined networking (SDN), which has been successfully deployed in
the management of complex data centers, has recently been incorporated into a
myriad of 5G networks to intelligently manage a wide range of heterogeneous
wireless devices, software systems, and wireless access technologies. Thus, the
SDN control plane needs to communicate wirelessly with the wireless data plane
either directly or indirectly. The uncertainties in the wireless SDN control
plane (WCP) make its design challenging. Both WCP schemes (direct WCP, D-WCP,
and indirect WCP, I-WCP) have been incorporated into recent 5G networks;
however, a discussion of their design principles and their design limitations
is missing. This paper introduces an overview of the WCP design (I-WCP and
D-WCP) and discusses its intricacies by reviewing its deployment in recent 5G
networks. Furthermore, to facilitate synthesizing a robust WCP, this paper
proposes a generic WCP framework using deep reinforcement learning (DRL)
principles and presents a roadmap for future research.Comment: This paper has been accepted to appear in Elsevier Journal of
Networks and Computer Applications. It has 34 pages, 8 figures, and two
table
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
Heterogeneous MacroTasking (HeMT) for Parallel Processing in the Public Cloud
Using tiny, equal-sized tasks (Homogeneous microTasking, HomT) has long been
regarded an effective way of load balancing in parallel computing systems. When
combined with nodes pulling in work upon becoming idle, HomT has the desirable
property of automatically adapting its load distribution to the processing
capacities of participating nodes - more powerful nodes finish their work
sooner and, therefore, pull in additional work faster. As a result, HomT is
deemed especially desirable in settings with heterogeneous (and possibly
possessing dynamically changing) processing capacities. However, HomT does have
additional scheduling and I/O overheads that might make this load balancing
scheme costly in some scenarios. In this paper, we first analyze these
advantages and disadvantages of HomT. We then propose an alternative load
balancing scheme - Heterogeneous MacroTasking (HeMT) - wherein workload is
intentionally partitioned according to nodes' processing capacity. Our goal is
to study when HeMT is able to overcome the performance disadvantages of HomT.
We implement a prototype of HeMT within the Apache Spark application framework
with complementary enhancements to the Apache Mesos cluster manager. Spark's
built-in scheduler, when parameterized appropriately, implements HomT. Our
experimental results show that HeMT out-performs HomT when accurate
workload-specific estimates of nodes' processing capacities are learned. As
representative results, Spark with HeMT offers about 10% better average
completion times for realistic data processing workloads over the default
system
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