Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE
DOI: 10.1109/glocom.2002.1189026
|View full text |Cite
|
Sign up to set email alerts
|

Modeling object characteristics of dynamic Web content

Abstract: Requests for dynamic and personalized content have increasingly become a significant part of Internet traffic, driven both by a growth in dynamic web services and a "trickle-down" effect stemming from the effectiveness of caches and content-distribution networks at serving static content. To efficiently serve this trend, several server-side and cache-side techniques have recently been proposed. Although such techniques, which exploit different forms of reuse at the sub-document level, appear promising, a signi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Publication Types

Select...
2
2
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 22 publications
0
2
0
Order By: Relevance
“…By doing so, it intends to improve system performance, minimize latency, and strengthen data privacy and security. The fundamental idea of edge computing is to process computing tasks on computing resources close to data sources [1]. The rational task offloading to edge nodes and efficient allocation of limited computing resources to each computing task remain pivotal challenges in edge computing research.…”
Section: Introductionmentioning
confidence: 99%
“…By doing so, it intends to improve system performance, minimize latency, and strengthen data privacy and security. The fundamental idea of edge computing is to process computing tasks on computing resources close to data sources [1]. The rational task offloading to edge nodes and efficient allocation of limited computing resources to each computing task remain pivotal challenges in edge computing research.…”
Section: Introductionmentioning
confidence: 99%
“…K-Means algorith m is a clustering algorithm, By calcu lating the logical distance between the sample data points to determine a samp le data points belonging to the types of clusters, Algorith m u ltimate aim is to used for the samp le data point of the algorith m is assigned to the K-th cluster, Po ints within the clusters have a greater degree of similarity, Po int smaller similarity between clusters [2]. K-Means, K represents the number of cluster center.…”
Section: A K-means Algorithm Introductionmentioning
confidence: 99%