36 research outputs found
A parallel algorithm to calculate the costrank of a network
We developed analogous parallel algorithms to implement CostRank for distributed memory parallel computers using multi processors. Our intent is to make CostRank calculations for the growing number of hosts in a fast and a scalable way. In the same way we intent to secure large scale networks that require fast and reliable computing to calculate the ranking of enormous graphs with thousands of vertices (states) and millions or arcs (links). In our proposed approach we focus on a parallel CostRank computational architecture on a cluster of PCs networked via Gigabit Ethernet LAN to evaluate the performance and scalability of our implementation. In particular, a partitioning of input data, graph files, and ranking vectors with load balancing technique can improve the runtime and scalability of large-scale parallel computations. An application case study of analogous Cost Rank computation is presented. Applying parallel environment models for one-dimensional sparse matrix partitioning on a modified research page, results in a significant reduction in communication overhead and in per-iteration runtime. We provide an analytical discussion of analogous algorithms performance in terms of I/O and synchronization cost, as well as of memory usage
Local dependency in networks
Many real world data and processes have a network structure and can usefully be represented as graphs. Network analysis focuses on the relations among the nodes exploring the properties of each network. We introduce a method for measuring the strength of the relationship between two nodes of a network and for their ranking. This method is applicable to all kinds of networks, including directed and weighted networks. The approach extracts dependency relations among the network's nodes from the structure in local surroundings of individual nodes. For the tasks we deal with in this article, the key technical parameter is locality. Since only the surroundings of the examined nodes are used in computations, there is no need to analyze the entire network. This allows the application of our approach in the area of large-scale networks. We present several experiments using small networks as well as large-scale artificial and real world networks. The results of the experiments show high effectiveness due to the locality of our approach and also high quality node ranking comparable to PageRank.Web of Science25229328
Optimization of energy efficiency in data and WEB hosting centers
MenciĂłn Internacional en el tĂtulo de doctorThis thesis tackles the optimization of energy efficiency in data centers in terms of network
and server utilization.
For what concerns networking utilization the work focuses on Energy Efficient Ethernet
(EEE) - IEEE 802.3az standard - which is the energy-aware alternative to legacy Ethernet, and an
important component of current and future green data centers. More specifically the first contribution
of this thesis consists in deriving and analytical model of gigabit EEE links with coalescing
using M/G/1 queues with sleep and wake-up periods. Packet coalescing has been proposed to save
energy by extending the sojourn in the Low Power Idle state of EEE. The model presented in this
thesis approximates with a good accuracy both the energy saving and the average packet delay by
using a few significant traffic descriptors. While coalescing improves by far the energy efficiency
of EEE, it is still far from achieving energy consumption proportional to traffic. Moreover, coalescing
can introduce high delays. To this extend, by using sensitivity analysis the thesis evaluates
the impact of coalescing timers and buffer sizes, and sheds light on the delay incurred by adopting
coalescing schemes. Accordingly, the design and study of a first family of dynamic algorithms,
namely measurement-based coalescing control (MBCC), is proposed. MBCC schemes tune the
coalescing parameters on-the-fly, according to the instantaneous load and the coalescing delay
experienced by the packets. The thesis also discusses a second family of dynamic algorithms,
namely NT-policy coalescing control (NTCC), that adjusts the coalescing parameters based on
the sole occurrence of timeouts and buffer fill-ups. Furthermore, the performance of static as well
as dynamic coalescing schemes is investigated using real traffic traces. The results reported in this
work show that, by relying on run-time delay measurements, simple and practical MBCC adaptive
coalescing schemes outperform traditional static and dynamic coalescing while the adoption
of NTCC coalescing schemes has practically no advantages with respect to static coalescing when
delay guarantees have to be provided. Notably, MBCC schemes double the energy saving benefit
of legacy EEE coalescing and allow to control the coalescing delay.
For what concerns server utilization, the thesis presents an exhaustive empirical characterization
of the power requirements of multiple components of data center servers. The characterization
is the second key contribution of this thesis, and is achieved by devising different experiments
to stress server components, taking into account the multiple available CPU frequencies and the
presence of multicore servers. The described experiments, allow to measure energy consumption of server components and identify their optimal operational points. The study proves that the
curve defining the minimal CPU power utilization, as a function of the load expressed in Active
Cycles Per Second, is neither concave nor purely convex. Instead, it definitively shows a superlinear
dependence on the load. The results illustrate how to improve the efficiency of network
cards and disks. Finally, the accuracy of the model derived from the server components consumption
characterization is validated by comparing the real energy consumed by two Hadoop
applications - PageRank and WordCount - with the estimation from the model, obtaining errors
below 4:1%, on average.This work has been partially supported by IMDEA Networks Institute and the Greek State Scholarships
FoundationPrograma Oficial de Doctorado en IngenierĂa TelemáticaPresidente: Marco Giuseppe Ajmone Marsan.- Secretario: Jose Luis Ayala Rodrigo.- Vocal: Gianluca Antonio Rizz
Exploiting links and text structure on the Web : a quantitative approach to improving search quality
[no abstract