A Cache Model for Modern Processors

Modern processors use high-performance cache replacement policies that outperform traditional alternatives like least-recently used (LRU). Unfortunately, current cache models use stack distances to predict LRU or its variants, and cannot capture these high-performance policies. Accurate predictions of cache performance enable many optimizations in multicore systems. For example, cache partitioning uses these predictions to divide capacity among applications in order to maximize performance, guarantee quality of service, or achieve other system objectives. Without an accurate model for high-performance replacement policies, these optimizations are unavailable to modern processors. We present a new probabilistic cache model designed for high-performance replacement policies. This model uses absolute reuse distances instead of stack distances, which makes it applicable to arbitrary age-based replacement policies. We thoroughly validate our model on several high-performance policies on synthetic and real benchmarks, where its median error is less than 1%. Finally, we present two case studies showing how to use the model to improve shared and single-stream cache performance

An Approach to Integrate Distributed Systems of Medical Devices in High Acuity Environments

This paper presents a comprehensive solution to build a distributed system of medical devices in high acuity environments. It is based on the concept of a Service Oriented Medical Device Architecture. It uses the Devices Profile for Web Services as a transport layer protocol and enhances it to the Medical Devices Profile for Web Service (MDPWS) to meet medical requirements. By applying the ISO/IEEE 11073 Domain Information Model, device data can be semantically described and exchanged by means of a generic service interface. Data model and service interface are subsumed under the Basic Integrated Clinical Environment Specification (BICEPS). MDPWS and BICEPS are implemented as part of the publically available openSDC stack. Performance measurements and a real world setup prove that openSDC is feasible to be deployed in distributed systems of medical devices

UncertWeb processing service:making models easer to access on the web

Models are central tools for modern scientists and decision makers, and there are many existing frameworks to support their creation, execution and composition. Many frameworks are based on proprietary interfaces, and do not lend themselves to the integration of models from diverse disciplines. Web based systems, or systems based on web services, such as Taverna and Kepler, allow composition of models based on standard web service technologies. At the same time the Open Geospatial Consortium has been developing their own service stack, which includes the Web Processing Service, designed to facilitate the executing of geospatial processing - including complex environmental models. The current Open Geospatial Consortium service stack employs Extensible Markup Language as a default data exchange standard, and widely-used encodings such as JavaScript Object Notation can often only be used when incorporated with Extensible Markup Language. Similarly, no successful engagement of the Web Processing Service standard with the well-supported technologies of Simple Object Access Protocol and Web Services Description Language has been seen. In this paper we propose a pure Simple Object Access Protocol/Web Services Description Language processing service which addresses some of the issues with the Web Processing Service specication and brings us closer to achieving a degree of interoperability between geospatial models, and thus realising the vision of a useful 'model web'

Analisis Kerentanan Dan Kehandalan Layanan Jaringan Cloud Berbasis Platform Eucalyptus

Cloud computing is a computing paradigm that evolves from existing technology, such as grid computing, virtualization and the Internet. Cloud computing provides an illusion of unlimited computing resources, which can be accessed from anywhere, anytime. Despite the potential gains achieved from the cloud computing, the model security is still questionable which hindered adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. Eucalyptus-based cloud network services widely deployed as private cloud infrastructure. Experiment on this paper focused on finding potential denial-of-service (DOS) and the impact on ability to provide services during attack. We observe an increase on response time up to 2863.22% during attack to the web-based management service. Reducing average system load to an acceptable level, help prevents disruption of the service, by implementing rate control and rate limit on cloud controller

Cloud-Based Deep Learning: End-To-End Full-Stack Handwritten Digit Recognition

Herein, we present Stratus, an end-to-end full-stack deep learning application deployed on the cloud. The rise of productionized deep learning necessitates infrastructure in the cloud that can provide such service (IaaS). In this paper, we explore the use of modern cloud infrastructure and micro-services to deliver accurate and high-speed predictions to an end-user, using a Deep Neural Network (DNN) to predict handwritten digit input, interfaced via a full-stack application. We survey tooling from Spark ML, Apache Kafka, Chameleon Cloud, Ansible, Vagrant, Python Flask, Docker, and Kubernetes in order to realize this machine learning pipeline. Through our cloud-based approach, we are able to demonstrate benchmark performance on the MNIST dataset with a deep learning model