26,929 research outputs found
On the Design of Clean-Slate Network Control and Management Plane
We provide a design of clean-slate control and management plane for data networks using the abstraction of 4D architecture, utilizing and extending 4D’s concept of a logically centralized Decision plane that is responsible for managing network-wide resources. In this paper, a scalable protocol and a dynamically adaptable algorithm for assigning Data plane devices to a physically distributed Decision plane are investigated, that enable a network to operate with minimal configuration and human intervention while providing optimal convergence and robustness against failures. Our work is especially relevant in the context of ISPs and large geographically dispersed enterprise networks. We also provide an extensive evaluation of our algorithm using real-world and artificially generated ISP topologies along with an experimental evaluation using ns-2 simulator
Dependable Distributed Computing for the International Telecommunication Union Regional Radio Conference RRC06
The International Telecommunication Union (ITU) Regional Radio Conference
(RRC06) established in 2006 a new frequency plan for the introduction of
digital broadcasting in European, African, Arab, CIS countries and Iran. The
preparation of the plan involved complex calculations under short deadline and
required dependable and efficient computing capability. The ITU designed and
deployed in-situ a dedicated PC farm, in parallel to the European Organization
for Nuclear Research (CERN) which provided and supported a system based on the
EGEE Grid. The planning cycle at the RRC06 required a periodic execution in the
order of 200,000 short jobs, using several hundreds of CPU hours, in a period
of less than 12 hours. The nature of the problem required dynamic
workload-balancing and low-latency access to the computing resources. We
present the strategy and key technical choices that delivered a reliable
service to the RRC06
A network-aware framework for energy-efficient data acquisition in wireless sensor networks
Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN
Co-Scheduling Algorithms for High-Throughput Workload Execution
This paper investigates co-scheduling algorithms for processing a set of
parallel applications. Instead of executing each application one by one, using
a maximum degree of parallelism for each of them, we aim at scheduling several
applications concurrently. We partition the original application set into a
series of packs, which are executed one by one. A pack comprises several
applications, each of them with an assigned number of processors, with the
constraint that the total number of processors assigned within a pack does not
exceed the maximum number of available processors. The objective is to
determine a partition into packs, and an assignment of processors to
applications, that minimize the sum of the execution times of the packs. We
thoroughly study the complexity of this optimization problem, and propose
several heuristics that exhibit very good performance on a variety of
workloads, whose application execution times model profiles of parallel
scientific codes. We show that co-scheduling leads to to faster workload
completion time and to faster response times on average (hence increasing
system throughput and saving energy), for significant benefits over traditional
scheduling from both the user and system perspectives
Teaching about Madrid: A Collaborative Agents-Based Distributed Learning Course
Interactive art courses require a huge amount of computational resources to be running on real time. These computational resources are even bigger if the course has been designed as a Virtual Environment with which students can interact. In this paper, we present an initiative that has been develop in a close collaboration between two Spanish Universities: Universidad Politécnica de Madrid and Universidad Rey Juan Carlos with the aim of join two previous research project: a Collaborative Awareness Model for Task-Balancing-Delivery (CAMT) in clusters and the “Teaching about Madrid” course, which provides a cultural interactive background of the capital of Spain
Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results
Using constraint-based local search, we effectively model and efficiently
solve the problem of balancing the traffic demands on portions of the European
airspace while ensuring that their capacity constraints are satisfied. The
traffic demand of a portion of airspace is the hourly number of flights planned
to enter it, and its capacity is the upper bound on this number under which
air-traffic controllers can work. Currently, the only form of demand-capacity
balancing we allow is ground holding, that is the changing of the take-off
times of not yet airborne flights. Experiments with projected European flight
plans of the year 2030 show that already this first form of demand-capacity
balancing is feasible without incurring too much total delay and that it can
lead to a significantly better demand-capacity balance
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