2,515 research outputs found
Modeling Data-Plane Power Consumption of Future Internet Architectures
With current efforts to design Future Internet Architectures (FIAs), the
evaluation and comparison of different proposals is an interesting research
challenge. Previously, metrics such as bandwidth or latency have commonly been
used to compare FIAs to IP networks. We suggest the use of power consumption as
a metric to compare FIAs. While low power consumption is an important goal in
its own right (as lower energy use translates to smaller environmental impact
as well as lower operating costs), power consumption can also serve as a proxy
for other metrics such as bandwidth and processor load.
Lacking power consumption statistics about either commodity FIA routers or
widely deployed FIA testbeds, we propose models for power consumption of FIA
routers. Based on our models, we simulate scenarios for measuring power
consumption of content delivery in different FIAs. Specifically, we address two
questions: 1) which of the proposed FIA candidates achieves the lowest energy
footprint; and 2) which set of design choices yields a power-efficient network
architecture? Although the lack of real-world data makes numerous assumptions
necessary for our analysis, we explore the uncertainty of our calculations
through sensitivity analysis of input parameters
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
On the Intrinsic Locality Properties of Web Reference Streams
There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged.
In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web.
Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality.
We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi
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