7,006 research outputs found
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things
Internet of Things (IoT) requires a new processing paradigm that inherits the
scalability of the cloud while minimizing network latency using resources
closer to the network edge. Building up such flexibility within the
edge-to-cloud continuum consisting of a distributed networked ecosystem of
heterogeneous computing resources is challenging. Load-balancing for fog
computing becomes a cornerstone for cost-effective system management and
operations. This paper studies two optimization objectives and formulates a
decentralized load-balancing problem for IoT service placement: (global) IoT
workload balance and (local) quality of service, in terms of minimizing the
cost of deadline violation, service deployment, and unhosted services. The
proposed solution, EPOS Fog, introduces a decentralized multiagent system for
collective learning that utilizes edge-to-cloud nodes to jointly balance the
input workload across the network and minimize the costs involved in service
execution. The agents locally generate possible assignments of requests to
resources and then cooperatively select an assignment such that their
combination maximizes edge utilization while minimizes service execution cost.
Extensive experimental evaluation with realistic Google cluster workloads on
various networks demonstrates the superior performance of EPOS Fog in terms of
workload balance and quality of service, compared to approaches such as First
Fit and exclusively Cloud-based. The findings demonstrate how distributed
computational resources on the edge can be utilized more cost-effectively by
harvesting collective intelligence.Comment: 16 pages and 15 figure
Reconfigurable Wireless Networks
Driven by the advent of sophisticated and ubiquitous applications, and the
ever-growing need for information, wireless networks are without a doubt
steadily evolving into profoundly more complex and dynamic systems. The user
demands are progressively rampant, while application requirements continue to
expand in both range and diversity. Future wireless networks, therefore, must
be equipped with the ability to handle numerous, albeit challenging
requirements. Network reconfiguration, considered as a prominent network
paradigm, is envisioned to play a key role in leveraging future network
performance and considerably advancing current user experiences. This paper
presents a comprehensive overview of reconfigurable wireless networks and an
in-depth analysis of reconfiguration at all layers of the protocol stack. Such
networks characteristically possess the ability to reconfigure and adapt their
hardware and software components and architectures, thus enabling flexible
delivery of broad services, as well as sustaining robust operation under highly
dynamic conditions. The paper offers a unifying framework for research in
reconfigurable wireless networks. This should provide the reader with a
holistic view of concepts, methods, and strategies in reconfigurable wireless
networks. Focus is given to reconfigurable systems in relatively new and
emerging research areas such as cognitive radio networks, cross-layer
reconfiguration and software-defined networks. In addition, modern networks
have to be intelligent and capable of self-organization. Thus, this paper
discusses the concept of network intelligence as a means to enable
reconfiguration in highly complex and dynamic networks. Finally, the paper is
supported with several examples and case studies showing the tremendous impact
of reconfiguration on wireless networks.Comment: 28 pages, 26 figures; Submitted to the Proceedings of the IEEE (a
special issue on Reconfigurable Systems
soCloud: A service-oriented component-based PaaS for managing portability, provisioning, elasticity, and high availability across multiple clouds
Multi-cloud computing is a promising paradigm to support very large scale
world wide distributed applications. Multi-cloud computing is the usage of
multiple, independent cloud environments, which assumed no priori agreement
between cloud providers or third party. However, multi-cloud computing has to
face several key challenges such as portability, provisioning, elasticity, and
high availability. Developers will not only have to deploy applications to a
specific cloud, but will also have to consider application portability from one
cloud to another, and to deploy distributed applications spanning multiple
clouds. This article presents soCloud a service-oriented component-based
Platform as a Service (PaaS) for managing portability, elasticity,
provisioning, and high availability across multiple clouds. soCloud is based on
the OASIS Service Component Architecture (SCA) standard in order to address
portability. soCloud provides services for managing provisioning, elasticity,
and high availability across multiple clouds. soCloud has been deployed and
evaluated on top of ten existing cloud providers: Windows Azure, DELL KACE,
Amazon EC2, CloudBees, OpenShift, dotCloud, Jelastic, Heroku, Appfog, and an
Eucalyptus private cloud
Recommended from our members
Multimedia delivery in the future internet
The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio
and video are produced, distributed, shared, managed and consumed on-line through various networks,
like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white
paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked
challenges of the Networked Media in the transition to the Future of the Internet.
Internet has evolved and changed the way we work and live. End users of the Internet have been confronted
with a bewildering range of media, services and applications and of technological innovations concerning
media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace
of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more
than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so
regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected
to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising
to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged
that in a near- to mid-term future, the Internet will provide the means to share and distribute (new)
multimedia content and services with superior quality and striking flexibility, in a trusted and personalized
way, improving citizens’ quality of life, working conditions, edutainment and safety.
In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe
network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as
community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of
interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and
innovative applications “on the move”, like virtual collaboration environments, personalised services/
media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content
combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P
networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to
contribute towards such a vision.
Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6)
and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily
contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way
ahead in the area of Content Aware media delivery platforms
A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units
Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations
Analysis of Various Decentralized Load Balancing Techniques with Node Duplication
Experience in parallel computing is an increasingly necessary skill for today’s upcoming computer scientists as processors are hitting a serial execution performance barrier and turning to parallel execution for continued gains. The uniprocessor system has now reached its maximum speed limit and, there is very less scope to improve the speed of such type of system. To solve this problem multiprocessor system is used, which have more than one processor. Multiprocessor system improves the speed of the system but it again faces some problems like data dependency, control dependency, resource dependency and improper load balancing. So this paper presents a detailed analysis of various decentralized load balancing techniques with node duplication to reduce the proper execution time
A Survey on the Contributions of Software-Defined Networking to Traffic Engineering
Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.European Commission through the Horizon 2020 Research and Innovation Programme (GN4) under Grant 691567
Spanish Ministry of Economy and Competitiveness under the Secure Deployment of Services Over SDN and NFV-based Networks Project S&NSEC under Grant TEC2013-47960-C4-3-
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