27,379 research outputs found
A Superstabilizing -Approximation Algorithm for Dynamic Steiner Trees
In this paper we design and prove correct a fully dynamic distributed
algorithm for maintaining an approximate Steiner tree that connects via a
minimum-weight spanning tree a subset of nodes of a network (referred as
Steiner members or Steiner group) . Steiner trees are good candidates to
efficiently implement communication primitives such as publish/subscribe or
multicast, essential building blocks for the new emergent networks (e.g. P2P,
sensor or adhoc networks). The cost of the solution returned by our algorithm
is at most times the cost of an optimal solution, where is the
group of members. Our algorithm improves over existing solutions in several
ways. First, it tolerates the dynamism of both the group members and the
network. Next, our algorithm is self-stabilizing, that is, it copes with nodes
memory corruption. Last but not least, our algorithm is
\emph{superstabilizing}. That is, while converging to a correct configuration
(i.e., a Steiner tree) after a modification of the network, it keeps offering
the Steiner tree service during the stabilization time to all members that have
not been affected by this modification
Distributed Optimization With Local Domains: Applications in MPC and Network Flows
In this paper we consider a network with nodes, where each node has
exclusive access to a local cost function. Our contribution is a
communication-efficient distributed algorithm that finds a vector
minimizing the sum of all the functions. We make the additional assumption that
the functions have intersecting local domains, i.e., each function depends only
on some components of the variable. Consequently, each node is interested in
knowing only some components of , not the entire vector. This allows
for improvement in communication-efficiency. We apply our algorithm to model
predictive control (MPC) and to network flow problems and show, through
experiments on large networks, that our proposed algorithm requires less
communications to converge than prior algorithms.Comment: Submitted to IEEE Trans. Aut. Contro
DCCast: Efficient Point to Multipoint Transfers Across Datacenters
Using multiple datacenters allows for higher availability, load balancing and
reduced latency to customers of cloud services. To distribute multiple copies
of data, cloud providers depend on inter-datacenter WANs that ought to be used
efficiently considering their limited capacity and the ever-increasing data
demands. In this paper, we focus on applications that transfer objects from one
datacenter to several datacenters over dedicated inter-datacenter networks. We
present DCCast, a centralized Point to Multi-Point (P2MP) algorithm that uses
forwarding trees to efficiently deliver an object from a source datacenter to
required destination datacenters. With low computational overhead, DCCast
selects forwarding trees that minimize bandwidth usage and balance load across
all links. With simulation experiments on Google's GScale network, we show that
DCCast can reduce total bandwidth usage and tail Transfer Completion Times
(TCT) by up to compared to delivering the same objects via independent
point-to-point (P2P) transfers.Comment: 9th USENIX Workshop on Hot Topics in Cloud Computing,
https://www.usenix.org/conference/hotcloud17/program/presentation/noormohammadpou
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