110 research outputs found
A Case for Peering of Content Delivery Networks
The proliferation of Content Delivery Networks (CDN) reveals that existing
content networks are owned and operated by individual companies. As a
consequence, closed delivery networks are evolved which do not cooperate with
other CDNs and in practice, islands of CDNs are formed. Moreover, the logical
separation between contents and services in this context results in two content
networking domains. But present trends in content networks and content
networking capabilities give rise to the interest in interconnecting content
networks. Finding ways for distinct content networks to coordinate and
cooperate with other content networks is necessary for better overall service.
In addition to that, meeting the QoS requirements of users according to the
negotiated Service Level Agreements between the user and the content network is
a burning issue in this perspective. In this article, we present an open,
scalable and Service-Oriented Architecture based system to assist the creation
of open Content and Service Delivery Networks (CSDN) that scale and support
sharing of resources with other CSDNs.Comment: Short Article (Submitted in DS Online as Work in Progress
CliqueStream: an efficient and fault-resilient live streaming network on a clustered peer-to-peer overlay
Several overlay-based live multimedia streaming platforms have been proposed
in the recent peer-to-peer streaming literature. In most of the cases, the
overlay neighbors are chosen randomly for robustness of the overlay. However,
this causes nodes that are distant in terms of proximity in the underlying
physical network to become neighbors, and thus data travels unnecessary
distances before reaching the destination. For efficiency of bulk data
transmission like multimedia streaming, the overlay neighborhood should
resemble the proximity in the underlying network. In this paper, we exploit the
proximity and redundancy properties of a recently proposed clique-based
clustered overlay network, named eQuus, to build efficient as well as robust
overlays for multimedia stream dissemination. To combine the efficiency of
content pushing over tree structured overlays and the robustness of data-driven
mesh overlays, higher capacity stable nodes are organized in tree structure to
carry the long haul traffic and less stable nodes with intermittent presence
are organized in localized meshes. The overlay construction and fault-recovery
procedures are explained in details. Simulation study demonstrates the good
locality properties of the platform. The outage time and control overhead
induced by the failure recovery mechanism are minimal as demonstrated by the
analysis.Comment: 10 page
Adaptive Replication in Distributed Content Delivery Networks
We address the problem of content replication in large distributed content
delivery networks, composed of a data center assisted by many small servers
with limited capabilities and located at the edge of the network. The objective
is to optimize the placement of contents on the servers to offload as much as
possible the data center. We model the system constituted by the small servers
as a loss network, each loss corresponding to a request to the data center.
Based on large system / storage behavior, we obtain an asymptotic formula for
the optimal replication of contents and propose adaptive schemes related to
those encountered in cache networks but reacting here to loss events, and
faster algorithms generating virtual events at higher rate while keeping the
same target replication. We show through simulations that our adaptive schemes
outperform significantly standard replication strategies both in terms of loss
rates and adaptation speed.Comment: 10 pages, 5 figure
Active Learning of Multiple Source Multiple Destination Topologies
We consider the problem of inferring the topology of a network with
sources and receivers (hereafter referred to as an -by- network), by
sending probes between the sources and receivers. Prior work has shown that
this problem can be decomposed into two parts: first, infer smaller subnetwork
components (i.e., -by-'s or -by-'s) and then merge these components
to identify the -by- topology. In this paper, we focus on the second
part, which had previously received less attention in the literature. In
particular, we assume that a -by- topology is given and that all
-by- components can be queried and learned using end-to-end probes. The
problem is which -by-'s to query and how to merge them with the given
-by-, so as to exactly identify the -by- topology, and optimize a
number of performance metrics, including the number of queries (which directly
translates into measurement bandwidth), time complexity, and memory usage. We
provide a lower bound, , on the number of
-by-'s required by any active learning algorithm and propose two greedy
algorithms. The first algorithm follows the framework of multiple hypothesis
testing, in particular Generalized Binary Search (GBS), since our problem is
one of active learning, from -by- queries. The second algorithm is called
the Receiver Elimination Algorithm (REA) and follows a bottom-up approach: at
every step, it selects two receivers, queries the corresponding -by-, and
merges it with the given -by-; it requires exactly steps, which is
much less than all possible -by-'s. Simulation results
over synthetic and realistic topologies demonstrate that both algorithms
correctly identify the -by- topology and are near-optimal, but REA is
more efficient in practice
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