15,091 research outputs found
Autonomic Resource Management in Virtual Networks
Virtualization enables the building of multiple virtual networks over a
shared substrate. One of the challenges to virtualisation is efficient resource
allocation. This problem has been found to be NP hard. Therefore, most
approaches to it have not only proposed static solutions, but have also made
many assumptions to simplify it. In this paper, we propose a distributed,
autonomic and artificial intelligence based solution to resource allocation.
Our aim is to obtain self-configuring, selfoptimizing, self-healing and context
aware virtual networksComment: Short Paper, 4 Pages, Summer School, PhD Work In Progress Workshop.
Scalable and Adaptive Internet Solutions (SAIL). June 201
A Dynamic Service-Migration Mechanism in Edge Cognitive Computing
Driven by the vision of edge computing and the success of rich cognitive
services based on artificial intelligence, a new computing paradigm, edge
cognitive computing (ECC), is a promising approach that applies cognitive
computing at the edge of the network. ECC has the potential to provide the
cognition of users and network environmental information, and further to
provide elastic cognitive computing services to achieve a higher energy
efficiency and a higher Quality of Experience (QoE) compared to edge computing.
This paper firstly introduces our architecture of the ECC and then describes
its design issues in detail. Moreover, we propose an ECC-based dynamic service
migration mechanism to provide an insight into how cognitive computing is
combined with edge computing. In order to evaluate the proposed mechanism, a
practical platform for dynamic service migration is built up, where the
services are migrated based on the behavioral cognition of a mobile user. The
experimental results show that the proposed ECC architecture has ultra-low
latency and a high user experience, while providing better service to the user,
saving computing resources, and achieving a high energy efficiency
Learning-based Prediction, Rendering and Association Optimization for MEC-enabled Wireless Virtual Reality (VR) Network
Wireless-connected Virtual Reality (VR) provides immersive experience for VR
users from any-where at anytime. However, providing wireless VR users with
seamless connectivity and real-time VR video with high quality is challenging
due to its requirements in high Quality of Experience (QoE) and low VR
interaction latency under limited computation capability of VR device. To
address these issues,we propose a MEC-enabled wireless VR network, where the
field of view (FoV) of each VR user can be real-time predicted using Recurrent
Neural Network (RNN), and the rendering of VR content is moved from VR device
to MEC server with rendering model migration capability. Taking into account
the geographical and FoV request correlation, we propose centralized and
distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize
the long-term QoE of VR users under the VR interaction latency constraint.
Simulation results show that our proposed MEC rendering schemes and DRL
algorithms substantially improve the long-term QoE of VR users and reduce the
VR interaction latency compared to rendering at VR device
Reinforcement Learning-based Application Autoscaling in the Cloud: A Survey
Reinforcement Learning (RL) has demonstrated a great potential for
automatically solving decision-making problems in complex uncertain
environments. RL proposes a computational approach that allows learning through
interaction in an environment with stochastic behavior, where agents take
actions to maximize some cumulative short-term and long-term rewards. Some of
the most impressive results have been shown in Game Theory where agents
exhibited superhuman performance in games like Go or Starcraft 2, which led to
its gradual adoption in many other domains, including Cloud Computing.
Therefore, RL appears as a promising approach for Autoscaling in Cloud since it
is possible to learn transparent (with no human intervention), dynamic (no
static plans), and adaptable (constantly updated) resource management policies
to execute applications. These are three important distinctive aspects to
consider in comparison with other widely used autoscaling policies that are
defined in an ad-hoc way or statically computed as in solutions based on
meta-heuristics. Autoscaling exploits the Cloud elasticity to optimize the
execution of applications according to given optimization criteria, which
demands to decide when and how to scale-up/down computational resources, and
how to assign them to the upcoming processing workload. Such actions have to be
taken considering that the Cloud is a dynamic and uncertain environment.
Motivated by this, many works apply RL to the autoscaling problem in the Cloud.
In this work, we survey exhaustively those proposals from major venues, and
uniformly compare them based on a set of proposed taxonomies. We also discuss
open problems and prospective research in the area.Comment: 40 pages, 9 figure
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning in communications and networking. Modern networks, e.g.,
Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become
more decentralized and autonomous. In such networks, network entities need to
make decisions locally to maximize the network performance under uncertainty of
network environment. Reinforcement learning has been efficiently used to enable
the network entities to obtain the optimal policy including, e.g., decisions or
actions, given their states when the state and action spaces are small.
However, in complex and large-scale networks, the state and action spaces are
usually large, and the reinforcement learning may not be able to find the
optimal policy in reasonable time. Therefore, deep reinforcement learning, a
combination of reinforcement learning with deep learning, has been developed to
overcome the shortcomings. In this survey, we first give a tutorial of deep
reinforcement learning from fundamental concepts to advanced models. Then, we
review deep reinforcement learning approaches proposed to address emerging
issues in communications and networking. The issues include dynamic network
access, data rate control, wireless caching, data offloading, network security,
and connectivity preservation which are all important to next generation
networks such as 5G and beyond. Furthermore, we present applications of deep
reinforcement learning for traffic routing, resource sharing, and data
collection. Finally, we highlight important challenges, open issues, and future
research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper
Performance-Aware Management of Cloud Resources: A Taxonomy and Future Directions
Dynamic nature of the cloud environment has made distributed resource
management process a challenge for cloud service providers. The importance of
maintaining the quality of service in accordance with customer expectations as
well as the highly dynamic nature of cloud-hosted applications add new levels
of complexity to the process. Advances to the big data learning approaches have
shifted conventional static capacity planning solutions to complex
performance-aware resource management methods. It is shown that the process of
decision making for resource adjustment is closely related to the behaviour of
the system including the utilization of resources and application components.
Therefore, a continuous monitoring of system attributes and performance metrics
provide the raw data for the analysis of problems affecting the performance of
the application. Data analytic methods such as statistical and machine learning
approaches offer the required concepts, models and tools to dig into the data,
find general rules, patterns and characteristics that define the functionality
of the system. Obtained knowledge form the data analysis process helps to find
out about the changes in the workloads, faulty components or problems that can
cause system performance to degrade. A timely reaction to performance
degradations can avoid violations of the service level agreements by performing
proper corrective actions including auto-scaling or other resource adjustment
solutions. In this paper, we investigate the main requirements and limitations
in cloud resource management including a study of the approaches in workload
and anomaly analysis in the context of the performance management in the cloud.
A taxonomy of the works on this problem is presented which identifies the main
approaches in existing researches from data analysis side to resource
adjustment techniques
HyMER: A Hybrid Machine Learning Framework for Energy Efficient Routing in SDN
Software-defined networks (SDN) with programmable data plane and machine
learning for discovering patterns are utilized in security, traffic
classification, quality of services prediction, and network performance, that
has increasing research attention. Addressing the significance of energy
efficiency in networks, we propose a novel hybrid machine learning-based
framework named HyMER that combines the capabilities of SDN and machine
learning for traffic-aware energy efficient routing. To the best of our
knowledge, HyMER is the first that utilizes a hybrid machine learning solution
with supervised and reinforcement learning components for energy efficiency and
network performance in SDN. The supervised learning component consists of
feature extraction, training, and testing. The reinforcement learning component
learns from existing data or from scratch by iteratively interacting with the
network environment. The HyMER framework is developed on POX controller and is
evaluated on Mininet using real-world topologies and dynamic traffic traces.
Experimental results show that the supervised component achieves up to 70%
feature size reduction and more than 80\% accuracy in parameter prediction. We
demonstrate that combining the supervised and reinforcement methods not only
does capture the dynamic change more efficiently but also increases the
convergence speed. As compared to state-of-the-art utility based energy saving
approaches, HyMER heuristics has shown up to 50% link saving, and also exhibits
up to 14.7 watts less power consumption for realistic network topology and
traffic traces.Comment: Double column 12 pages, 13 figures, 6 table
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
Traffic-aware Threshold Adjustment for NFV Scaling using DDPG
Current solutions mostly focus on how to predict traffic, rather than
observing traffic characteristics in a specific NFV scenario. So, most of them
use a uniform threshold to scale in/out. In real NFV scenario, each VNF may
serve the one or more flows, and the characteristics of these flows are
completely different, a uniform threshold used in this scenario is not
suitable, because each VNF has a distinct processing logic depending on
incident network traffic and events. Even if certain VNFs share packet
processing functionality such as packet header analysis, the differences in
upper-layer processing and implementation can exhibit unique resource usage
patterns.
We proposes a dynamic threshold scaling mechanism that can tailor thresholds
according to each VNF's characteristic. As setting thresholds is a per-VNF
task, and requires a deep understanding of workload trends and the diversity of
each VNF, so we have added tailor-made features to the traditional dynamic
mechanism. Besides, we also reserve resources by predicting workload and add an
emergency module to cope with anomaly traffic, that is to say we develop a
hybrid scaling policy combining proactive and reactive scaling together.
Moreover, the sharp rise of network traffic not only can be caused by large
amount of new incoming flows, but also can be induced by the growing of
existing flows. If the traffic arises mainly due to the growing of existing
flows, then only rerouting new flows can not alleviate the overload quickly and
SLAs may be violated \cite{zhang2016co}. The only method to avoid SLA
violations is to migrate flows and associated NF internal states quickly and
safely from existing instances to new scaled instances, so state migration is
an important part of the scaling procedure. We achieved the flow migration in
scaling process on openNF to guarantee the accuracy and timeline of scaling
Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
The Internet of Things (IoT) paradigm is being rapidly adopted for the
creation of smart environments in various domains. The IoT-enabled
Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry
4.0 and Agtech handle a huge volume of data and require data processing
services from different types of applications in real-time. The Cloud-centric
execution of IoT applications barely meets such requirements as the Cloud
datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog
computing}, an extension of Cloud at the edge network, can execute these
applications closer to data sources. Thus, Fog computing can improve
application service delivery time and resist network congestion. However, the
Fog nodes are highly distributed, heterogeneous and most of them are
constrained in resources and spatial sharing. Therefore, efficient management
of applications is necessary to fully exploit the capabilities of Fog nodes. In
this work, we investigate the existing application management strategies in Fog
computing and review them in terms of architecture, placement and maintenance.
Additionally, we propose a comprehensive taxonomy and highlight the research
gaps in Fog-based application management. We also discuss a perspective model
and provide future research directions for further improvement of application
management in Fog computing
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