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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Data Mining - Concepts and Techniques.

http://brainimaging.waisman.wisc.edu/~chung/neuro.processing/literature/rubinov.2010.NI.network.pdf

Complex network measures of brain connectivity: uses and interpretations.

Devising an elastic resource allocation controller of data analytical applications in virtualized data-center has received a great attention recently, mainly due to the fact that even a slight performance improvement can translate to huge monetary savings in practical large-scale execution. Apache Flink is among modern streamed data processing run-times that can provide both low latency and high throughput computation in to execute processing pipelines over high-volume and high-velocity data-items under tight latency constraints. However, a yet to be answered challenge in a large-scale platform with tens of worker nodes is how to resolve the run-time violation in the quality of service (QoS) level in a multi-tenant data streaming platforms, particularly when the amount of workload generated by different users fluctuates. Studies showed that a static resource allocation algorithm (round-robin), which is used by default in Apache Flink, suffer from lack of responsiveness to sudden traffic surges happening unpredictably during the run-time. In this paper, we address the problem of resource management in a Flink platform for ensuring different QoS enforcement levels in a platform with shared computing resources. The proposed solution applies theoretical principals borrowed from close-loop control theory to design a CPU and memory adjustment mechanism with the primary goal to fulfill the different QoS levels requested by submitted applications while the resource interference is considered as the critical performance-limiting factor. The performance evaluation is carried out by comparing the proposed resource allocation mechanism with two static heuristics (round robin and class-based weighted fair queuing) in a 80-core cluster under multiple traffic patterns resembling sudden changes in the incoming workloads of low-priory streaming applications. The experimental results confirm the stability of the proposed controller to regulate the underlying platform resources to smoothly follow the target values (QoS violation rates). Particularly, the proposed solution can achieve higher efficiency compared to the other heuristics by reducing the response-time of high priority applications by 53% while maintaining the enforced QoS levels during the burst traffic periods.

A Dynamic Resource Controller for Resolving Quality of Service Issues in Modern Streaming Processing Engines

We consider the problem of non-clairvoyant scheduling on single machine to minimize the total flow time with job size predictions. The existing algorithm achieves 2-consistency to predictions, but no algorithm can simultaneously attain bounded robustness. This work finds a sufficient condition for any algorithm to achieve optimal O(P)-robustness, where P is the maximum ratio of any two job sizes. We give the first algorithm that achieves optimal robustness up to a constant multiplicative factor and optimal consistency using this condition. Finally, for addressing small prediction errors, we present an algorithm that we conjecture to achieve the optimal O(η^2) competitive ratio, where η is the prediction error. Proving the claimed bound is our ongoing work.

Brief Announcement: Towards a More Robust Algorithm for Flow Time Scheduling with Predictions

This chapter proposes a novel Grid-based scheduling algorithm that optimizes both computation and communications costs of workflow applications. Based on a hierarchical two-steps optimization process, a super scheduler first applies a Recursive Convex Clustering Algorithm (RCCA) that efficiently clusters tasks while minimizing communication costs. In the second step, a resource-broker assigns the generated convex sets to resources clusters. Local schedulers then optimize the makespan for the group of tasks assigned to their cluster, using a graphic processing unit (GPU)-based parallel cellular Genetic Algorithm(cGA). The performance improvement brought by this novel two-step scheduling algorithm compared to a hierarchical list-scheduling approach is empirically demonstrated on different real-world workflow applications.

Efficient Hierarchical Task Scheduling on GRIDS Accounting for Computation and Communications

https://dro.deakin.edu.au/eserv/DU:30106152/yu-editorial-2017.pdf

Editorial: special issue on “big data security and privacy”

The revival in learning theory has provided us with improved capabilities for accurate predictions. This work contributes to an emerging research agenda of online scheduling with predictions by studying the makespan minimization in uniformly related machine non-clairvoyant scheduling with job size predictions. Our task is to design online algorithms that effectively use predictions and have performance guarantees with varying prediction quality. We first propose a simple algorithm-independent prediction error measurement to quantify prediction quality. To effectively use the predicted job sizes, we design an offline improved 2-relaxed decision procedure approximating the optimal schedule. With this decision procedure, we propose an online O(min{log eta, log m})-competitive algorithm that assumes a known prediction error. Finally, we extend this algorithm to construct a robust O(min{log eta, log m})-competitive algorithm that does not assume a known error. Both algorithms require only moderate predictions to improve the well-known Omega(log m) lower bound, showing the potential of using predictions in managing uncertainty.

Albert Y. Zomaya

Papers

A Dynamic Resource Controller for Resolving Quality of Service Issues in Modern Streaming Processing Engines

Brief Announcement: Towards a More Robust Algorithm for Flow Time Scheduling with Predictions

Efficient Hierarchical Task Scheduling on GRIDS Accounting for Computation and Communications

Editorial: special issue on “big data security and privacy”

Uniform Machine Scheduling with Predictions