123 research outputs found
Cascading Failures in Power Grids - Analysis and Algorithms
This paper focuses on cascading line failures in the transmission system of
the power grid. Recent large-scale power outages demonstrated the limitations
of percolation- and epid- emic-based tools in modeling cascades. Hence, we
study cascades by using computational tools and a linearized power flow model.
We first obtain results regarding the Moore-Penrose pseudo-inverse of the power
grid admittance matrix. Based on these results, we study the impact of a single
line failure on the flows on other lines. We also illustrate via simulation the
impact of the distance and resistance distance on the flow increase following a
failure, and discuss the difference from the epidemic models. We then study the
cascade properties, considering metrics such as the distance between failures
and the fraction of demand (load) satisfied after the cascade (yield). We use
the pseudo-inverse of admittance matrix to develop an efficient algorithm to
identify the cascading failure evolution, which can be a building block for
cascade mitigation. Finally, we show that finding the set of lines whose
removal has the most significant impact (under various metrics) is NP-Hard and
introduce a simple heuristic for the minimum yield problem. Overall, the
results demonstrate that using the resistance distance and the pseudo-inverse
of admittance matrix provides important insights and can support the
development of efficient algorithms
A Fast Distributed Stateless Algorithm for -Fair Packing Problems
Over the past two decades, fair resource allocation problems have received
considerable attention in a variety of application areas. However, little
progress has been made in the design of distributed algorithms with convergence
guarantees for general and commonly used -fair allocations. In this
paper, we study weighted -fair packing problems, that is, the problems
of maximizing the objective functions (i) when , and (ii) when , over linear constraints , ,
where are positive weights and and are non-negative. We consider
the distributed computation model that was used for packing linear programs and
network utility maximization problems. Under this model, we provide a
distributed algorithm for general that converges to an
approximate solution in time (number of distributed iterations)
that has an inverse polynomial dependence on the approximation parameter
and poly-logarithmic dependence on the problem size. This is the
first distributed algorithm for weighted fair packing with
poly-logarithmic convergence in the input size. The algorithm uses simple local
update rules and is stateless (namely, it allows asynchronous updates, is
self-stabilizing, and allows incremental and local adjustments). We also obtain
a number of structural results that characterize fair allocations as
the value of is varied. These results deepen our understanding of
fairness guarantees in fair packing allocations, and also provide
insight into the behavior of fair allocations in the asymptotic cases
, , and .Comment: Added structural results for asymptotic cases of \alpha-fairness
(\alpha approaching 0, 1, or infinity), improved presentation, and revised
throughou
Experimental Evaluation of Large Scale WiFi Multicast Rate Control
WiFi multicast to very large groups has gained attention as a solution for
multimedia delivery in crowded areas. Yet, most recently proposed schemes do
not provide performance guarantees and none have been tested at scale. To
address the issue of providing high multicast throughput with performance
guarantees, we present the design and experimental evaluation of the Multicast
Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to
channel conditions and stability, which is essential for multimedia
applications. MuDRA relies on feedback from some nodes collected via a
light-weight protocol and dynamically adjusts the rate adaptation response
time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150
nodes shows that MuDRA outperforms other schemes and supports high throughput
multicast flows to hundreds of receivers while meeting quality requirements.
MuDRA can support multiple high quality video streams, where 90% of the nodes
report excellent or very good video quality
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