33,839 research outputs found
Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources
One of the most important challenges in smart grid systems is the integration
of renewable energy resources into its design. In this work, two different
techniques to mitigate the time varying and intermittent nature of renewable
energy generation are considered. The first one is the use of storage, which
smooths out the fluctuations in the renewable energy generation across time.
The second technique is the concept of distributed generation combined with
cooperation by exchanging energy among the distributed sources. This technique
averages out the variation in energy production across space. This paper
analyzes the trade-off between these two techniques. The problem is formulated
as a stochastic optimization problem with the objective of minimizing the time
average cost of energy exchange within the grid. First, an analytical model of
the optimal cost is provided by investigating the steady state of the system
for some specific scenarios. Then, an algorithm to solve the cost minimization
problem using the technique of Lyapunov optimization is developed and results
for the performance of the algorithm are provided. These results show that in
the presence of limited storage devices, the grid can benefit greatly from
cooperation, whereas in the presence of large storage capacity, cooperation
does not yield much benefit. Further, it is observed that most of the gains
from cooperation can be obtained by exchanging energy only among a few energy
harvesting sources
Scalable dimensioning of resilient Lambda Grids
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The MDS Queue: Analysing the Latency Performance of Erasure Codes
In order to scale economically, data centers are increasingly evolving their
data storage methods from the use of simple data replication to the use of more
powerful erasure codes, which provide the same level of reliability as
replication but at a significantly lower storage cost. In particular, it is
well known that Maximum-Distance-Separable (MDS) codes, such as Reed-Solomon
codes, provide the maximum storage efficiency. While the use of codes for
providing improved reliability in archival storage systems, where the data is
less frequently accessed (or so-called "cold data"), is well understood, the
role of codes in the storage of more frequently accessed and active "hot data",
where latency is the key metric, is less clear.
In this paper, we study data storage systems based on MDS codes through the
lens of queueing theory, and term this the "MDS queue." We analytically
characterize the (average) latency performance of MDS queues, for which we
present insightful scheduling policies that form upper and lower bounds to
performance, and are observed to be quite tight. Extensive simulations are also
provided and used to validate our theoretical analysis. We also employ the
framework of the MDS queue to analyse different methods of performing so-called
degraded reads (reading of partial data) in distributed data storage
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