There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.

Data Center Energy Consumption Modeling: A Survey

Thank you very much for downloading using mpi portable parallel programming with the message passing interface. As you may know, people have search hundreds times for their chosen novels like this using mpi portable parallel programming with the message passing interface, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their laptop.

Using Mpi Portable Parallel Programming With The Message Passing Interface

In large-scale networked computing systems, component failures become norms instead of exceptions. Failure prediction is a crucial technique for self-managing resource burdens. Failure events in coalition systems exhibit strong correlations in time and space domain. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to describe spatial correlation. We further utilize the information of application allocation to discover more correlations among failure instances. We cluster failure events based on their correlations and predict their future occurrences. We implemented a failure prediction framework, called PREdictor of Failure Events Correlated Temporal-Spatially (hPREFECTs), which explores correlations among failures and forecasts the time-between-failure of future instances. We evaluate the performance of hPREFECTs in both offline prediction of failure by using the Los Alamos HPC traces and online prediction in an institute-wide clusters coalition environment. Experimental results show the system achieves more than 76% accuracy in offline prediction and more than 70% accuracy in online prediction during the time from May 2006 to April 2007.

/pdf/exploring-event-correlation-for-failure-prediction-in-v0na6vk0lt.pdf

Exploring event correlation for failure prediction in coalitions of clusters

To improve our understanding of the role of electromagnetic ion cyclotron (EMIC) waves in radiation belt electron dynamics, we perform a comprehensive analysis of EMIC wave-induced resonant scattering of outer zone relativistic (>0.5 MeV) electrons and resultant electron loss time scales with respect to EMIC wave band, L shell, and wave normal angle model. The results demonstrate that while H+-band EMIC waves dominate the scattering losses of ~1–4 MeV outer zone relativistic electrons, it is He+-band and O+-band waves that prevail over the pitch angle diffusion of ultrarelativistic electrons at higher energies. Given the wave amplitude, EMIC waves at higher L shells tend to resonantly interact with a larger population of outer zone relativistic electrons and drive their pitch angle scattering more efficiently. Obliquity of EMIC waves can reduce the efficiency of wave-induced relativistic electron pitch angle scattering. Compared to the frequently adopted parallel or quasi-parallel model, use of the latitudinally varying wave normal angle model produces the largest decrease in H+-band EMIC wave scattering rates at pitch angles  ~5 MeV. At a representative nominal amplitude of 1 nT, EMIC wave scattering produces the equilibrium state (i.e., the lowest normal mode under which electrons at the same energy but different pitch angles decay exponentially on the same time scale) of outer belt relativistic electrons within several to tens of minutes and the following exponential decay extending to higher pitch angles on time scales from <1 min to ~1 h. The electron loss cone can be either empty as a result of the weak diffusion or heavily/fully filled due to approaching the strong diffusion limit, while the trapped electron population at high pitch angles close to 90° remains intact because of no resonant scattering. In this manner, EMIC wave scattering has the potential to deepen the anisotropic distribution of outer zone relativistic electrons by reshaping their pitch angle profiles to “top-hat.” Overall, H+-band and He+-band EMIC waves are most efficient in producing the pitch angle scattering loss of relativistic electrons at ~1–2 MeV. In contrast, the presence of O+-band EMIC waves, while at a smaller occurrence rate, can dominate the scattering loss of 5–10 MeV electrons in the entire region of the outer zone, which should be considered in future modeling of the outer zone relativistic electron dynamics.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015JA021466

Resonant scattering of outer zone relativistic electrons by multiband EMIC waves and resultant electron loss time scales

Cloud computing has become increasingly popular by obviating the need for users to own and maintain complex computing infrastructures. However, due to their inherent complexity and large scale, production cloud computing systems are prone to various runtime problems caused by hardware and software faults and environmental factors. Autonomic anomaly detection is a crucial technique for understanding emergent, cloud-wide phenomena and self-managing cloud resources for system-level dependability assurance. To detect anomalous cloud behaviors, we need to monitor the cloud execution and collect runtime cloud performance data. These data consist of values of performance metrics for different types of failures, which display different correlations with the performance metrics. In this paper, we present an adaptive anomaly identification mechanism that explores the most relevant principal components of different failure types in cloud computing infrastructures. It integrates the cloud performance metric analysis with filtering techniques to achieve automated, efficient, and accurate anomaly identification. The proposed mechanism adapts itself by recursively learning from the newly verified detection results to refine future detections. We have implemented a prototype of the anomaly identification system and conducted experiments in an on-campus cloud computing environment and by using the Google data center traces. Our experimental results show that our mechanism can achieve more efficient and accurate anomaly detection than other existing schemes.

Adaptive Anomaly Identification by Exploring Metric Subspace in Cloud Computing Infrastructures

Failure-aware resource management for high-availability computing clusters with distributed virtual machines

Autonomous vehicles may make wrong decisions due to inaccurate detection and recognition. Therefore, an intelligent vehicle can combine its own data with that of other vehicles to enhance perceptive ability, and thus improve detection accuracy and driving safety. However, multi-vehicle cooperative perception requires the integration of real world scenes and the traffic of raw sensor data exchange far exceeds the bandwidth of existing vehicular networks. To the best our knowledge, we are the first to conduct a study on raw-data level cooperative perception for enhancing the detection ability of self-driving systems. In this work, relying on LiDAR 3D point clouds, we fuse the sensor data collected from different positions and angles of connected vehicles. A point cloud based 3D object detection method is proposed to work on a diversity of aligned point clouds. Experimental results on KITTI and our collected dataset show that the proposed system outperforms perception by extending sensing area, improving detection accuracy and promoting augmented results. Most importantly, we demonstrate it is possible to transmit point clouds data for cooperative perception via existing vehicular network technologies.

Song Fu

Papers

Exploring event correlation for failure prediction in coalitions of clusters

Resonant scattering of outer zone relativistic electrons by multiband EMIC waves and resultant electron loss time scales

Adaptive Anomaly Identification by Exploring Metric Subspace in Cloud Computing Infrastructures

Failure-aware resource management for high-availability computing clusters with distributed virtual machines

Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds