404,957 research outputs found
Semantic Communications in Networked Systems
We present our vision for a departure from the established way of
architecting and assessing communication networks, by incorporating the
semantics of information for communications and control in networked systems.
We define semantics of information, not as the meaning of the messages, but as
their significance, possibly within a real time constraint, relative to the
purpose of the data exchange. We argue that research efforts must focus on
laying the theoretical foundations of a redesign of the entire process of
information generation, transmission and usage in unison by developing:
advanced semantic metrics for communications and control systems; an optimal
sampling theory combining signal sparsity and semantics, for real-time
prediction, reconstruction and control under communication constraints and
delays; semantic compressed sensing techniques for decision making and
inference directly in the compressed domain; semantic-aware data generation,
channel coding, feedback, multiple and random access schemes that reduce the
volume of data and the energy consumption, increasing the number of supportable
devices.Comment: 9 pages, 6 figures, 1500 word
Enabling Social Applications via Decentralized Social Data Management
An unprecedented information wealth produced by online social networks,
further augmented by location/collocation data, is currently fragmented across
different proprietary services. Combined, it can accurately represent the
social world and enable novel socially-aware applications. We present
Prometheus, a socially-aware peer-to-peer service that collects social
information from multiple sources into a multigraph managed in a decentralized
fashion on user-contributed nodes, and exposes it through an interface
implementing non-trivial social inferences while complying with user-defined
access policies. Simulations and experiments on PlanetLab with emulated
application workloads show the system exhibits good end-to-end response time,
low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of
Foundations of Social Computing, ACM Transactions on Internet Technolog
Distributed SIR-Aware Opportunistic Access Control for D2D Underlaid Cellular Networks
In this paper, we propose a distributed interference and channel-aware
opportunistic access control technique for D2D underlaid cellular networks, in
which each potential D2D link is active whenever its estimated
signal-to-interference ratio (SIR) is above a predetermined threshold so as to
maximize the D2D area spectral efficiency. The objective of our SIR-aware
opportunistic access scheme is to provide sufficient coverage probability and
to increase the aggregate rate of D2D links by harnessing interference caused
by dense underlaid D2D users using an adaptive decision activation threshold.
We determine the optimum D2D activation probability and threshold, building on
analytical expressions for the coverage probabilities and area spectral
efficiency of D2D links derived using stochastic geometry. Specifically, we
provide two expressions for the optimal SIR threshold, which can be applied in
a decentralized way on each D2D link, so as to maximize the D2D area spectral
efficiency derived using the unconditional and conditional D2D success
probability respectively. Simulation results in different network settings show
the performance gains of both SIR-aware threshold scheduling methods in terms
of D2D link coverage probability, area spectral efficiency, and average sum
rate compared to existing channel-aware access schemes.Comment: 6 pages, 6 figures, to be presented at IEEE GLOBECOM 201
Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments
Providing resource allocation with performance
predictability guarantees is increasingly important in cloud
platforms, especially for data-intensive applications, in which
performance depends greatly on the available rates of data
transfer between the various computing/storage hosts underlying
the virtualized resources assigned to the application. Existing
resource allocation solutions either assume that applications
manage their data transfer between their virtualized resources, or
that cloud providers manage their internal networking resources.
With the increased prevalence of brokerage services in cloud
platforms, there is a need for resource allocation solutions that
provides predictability guarantees in settings, in which neither
application scheduling nor cloud provider resources can be
managed/controlled by the broker. This paper addresses this
problem, as we define the Network-Constrained Packing (NCP)
problem of finding the optimal mapping of brokered resources
to applications with guaranteed performance predictability. We
prove that NCP is NP-hard, and we define two special instances
of the problem, for which exact solutions can be found efficiently.
We develop a greedy heuristic to solve the general instance of the
NCP problem , and we evaluate its efficiency using simulations
on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE
Progressive feature transmission for split classification at the wireless edge
We consider the scenario of inference at the wire-less edge , in which devices are connected to an edge server and ask the server to carry out remote classification, that is, classify data samples available at edge devices. This requires the edge devices to upload high-dimensional features of samples over resource-constrained wireless channels, which creates a communication bottleneck. The conventional feature pruning solution would require the device to have access to the inference model, which is not available in the current split inference scenario. To address this issue, we propose the progressive feature transmission (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. A control policy is proposed to accelerate inference, comprising two key operations: importance-aware feature selection at the server and transmission-termination control . For the former, it is shown that selecting the most important features, characterized by the largest discriminant gains of the corresponding feature dimensions, achieves a sub-optimal performance. For the latter, the proposed policy is shown to exhibit a threshold structure. Specifically, the transmission is stopped when the incremental uncertainty reduction by further feature transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The control policy is first derived for the tractable case of linear classification, and then extended to the more complex case of classification using a convolutional neural network . Both Gaussian and fading channels are considered. Experimental results are obtained for both a statistical data model and a real dataset. It is shown that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission strategies
Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems
We consider the problem of allocating radio resources over wireless
communication links to control a series of independent wireless control
systems. Low-latency transmissions are necessary in enabling time-sensitive
control systems to operate over wireless links with high reliability. Achieving
fast data rates over wireless links thus comes at the cost of reliability in
the form of high packet error rates compared to wired links due to channel
noise and interference. However, the effect of the communication link errors on
the control system performance depends dynamically on the control system state.
We propose a novel control-communication co-design approach to the low-latency
resource allocation problem. We incorporate control and channel state
information to make scheduling decisions over time on frequency, bandwidth and
data rates across the next-generation Wi-Fi based wireless communication links
that close the control loops. Control systems that are closer to instability or
further from a desired range in a given control cycle are given higher packet
delivery rate targets to meet. Rather than a simple priority ranking, we derive
precise packet error rate targets for each system needed to satisfy stability
targets and make scheduling decisions to meet such targets while reducing total
transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS)
method is tested in numerous simulation experiments that demonstrate its
effectiveness in meeting control-based goals under tight latency constraints
relative to control-agnostic scheduling
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