7,961 research outputs found
Merlin: A Language for Provisioning Network Resources
This paper presents Merlin, a new framework for managing resources in
software-defined networks. With Merlin, administrators express high-level
policies using programs in a declarative language. The language includes
logical predicates to identify sets of packets, regular expressions to encode
forwarding paths, and arithmetic formulas to specify bandwidth constraints. The
Merlin compiler uses a combination of advanced techniques to translate these
policies into code that can be executed on network elements including a
constraint solver that allocates bandwidth using parameterizable heuristics. To
facilitate dynamic adaptation, Merlin provides mechanisms for delegating
control of sub-policies and for verifying that modifications made to
sub-policies do not violate global constraints. Experiments demonstrate the
expressiveness and scalability of Merlin on real-world topologies and
applications. Overall, Merlin simplifies network administration by providing
high-level abstractions for specifying network policies and scalable
infrastructure for enforcing them
An Efficient Data Structure for Dynamic Two-Dimensional Reconfiguration
In the presence of dynamic insertions and deletions into a partially
reconfigurable FPGA, fragmentation is unavoidable. This poses the challenge of
developing efficient approaches to dynamic defragmentation and reallocation.
One key aspect is to develop efficient algorithms and data structures that
exploit the two-dimensional geometry of a chip, instead of just one. We propose
a new method for this task, based on the fractal structure of a quadtree, which
allows dynamic segmentation of the chip area, along with dynamically adjusting
the necessary communication infrastructure. We describe a number of algorithmic
aspects, and present different solutions. We also provide a number of basic
simulations that indicate that the theoretical worst-case bound may be
pessimistic.Comment: 11 pages, 12 figures; full version of extended abstract that appeared
in ARCS 201
Feedback Allocation For OFDMA Systems With Slow Frequency-domain Scheduling
We study the problem of allocating limited feedback resources across multiple
users in an orthogonal-frequency-division-multiple-access downlink system with
slow frequency-domain scheduling. Many flavors of slow frequency-domain
scheduling (e.g., persistent scheduling, semi-persistent scheduling), that
adapt user-sub-band assignments on a slower time-scale, are being considered in
standards such as 3GPP Long-Term Evolution. In this paper, we develop a
feedback allocation algorithm that operates in conjunction with any arbitrary
slow frequency-domain scheduler with the goal of improving the throughput of
the system. Given a user-sub-band assignment chosen by the scheduler, the
feedback allocation algorithm involves solving a weighted sum-rate maximization
at each (slow) scheduling instant. We first develop an optimal
dynamic-programming-based algorithm to solve the feedback allocation problem
with pseudo-polynomial complexity in the number of users and in the total
feedback bit budget. We then propose two approximation algorithms with
complexity further reduced, for scenarios where the problem exhibits additional
structure.Comment: Accepted to IEEE Transactions on Signal Processin
Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints
We consider allocation problems that arise in the context of service
allocation in Clouds. More specifically, we assume on the one part that each
computing resource is associated to a capacity constraint, that can be chosen
using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability
of failure. On the other hand, we assume that the service runs as a set of
independent instances of identical Virtual Machines. Moreover, there exists a
Service Level Agreement (SLA) between the Cloud provider and the client that
can be expressed as follows: the client comes with a minimal number of service
instances which must be alive at the end of the day, and the Cloud provider
offers a list of pairs (price,compensation), this compensation being paid by
the Cloud provider if it fails to keep alive the required number of services.
On the Cloud provider side, each pair corresponds actually to a guaranteed
success probability of fulfilling the constraint on the minimal number of
instances. In this context, given a minimal number of instances and a
probability of success, the question for the Cloud provider is to find the
number of necessary resources, their clock frequency and an allocation of the
instances (possibly using replication) onto machines. This solution should
satisfy all types of constraints during a given time period while minimizing
the energy consumption of used resources. We consider two energy consumption
models based on DVFS techniques, where the clock frequency of physical
resources can be changed. For each allocation problem and each energy model, we
prove deterministic approximation ratios on the consumed energy for algorithms
that provide guaranteed probability failures, as well as an efficient
heuristic, whose energy ratio is not guaranteed
Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things
Numerous energy harvesting wireless devices that will serve as building
blocks for the Internet of Things (IoT) are currently under development.
However, there is still only limited understanding of the properties of various
energy sources and their impact on energy harvesting adaptive algorithms.
Hence, we focus on characterizing the kinetic (motion) energy that can be
harvested by a wireless node with an IoT form factor and on developing energy
allocation algorithms for such nodes. In this paper, we describe methods for
estimating harvested energy from acceleration traces. To characterize the
energy availability associated with specific human activities (e.g., relaxing,
walking, cycling), we analyze a motion dataset with over 40 participants. Based
on acceleration measurements that we collected for over 200 hours, we study
energy generation processes associated with day-long human routines. We also
briefly summarize our experiments with moving objects. We develop energy
allocation algorithms that take into account practical IoT node design
considerations, and evaluate the algorithms using the collected measurements.
Our observations provide insights into the design of motion energy harvesters,
IoT nodes, and energy harvesting adaptive algorithms.Comment: 15 pages, 11 figure
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