Gregor von Laszewski

Cloud Computing: a Perspective Study

The Cloud computing emerges as a new computing paradigm which aims to provide reliable, customized and QoS guaranteed dynamic computing environments for end-users. In this paper, we study the Cloud computing paradigm from various aspects, such as definitions, distinct features, and enabling technologies. This paper brings an introductional review on the Cloud computing and provides the state-of-the-art of Cloud computing technologies.

QoS guided min-min heuristic for grid task scheduling

Task scheduling is an integrated component of computing. With the emergence of Grid and ubiquitous computing, new challenges appear in task scheduling based on properties such as security, quality of service, and lack of central control within distributed administrative domains. A Grid task scheduling framework must be able to deal with these issues. One of the goals of Grid task scheduling is to achieve high system throughput while matching applications with the available computing resources. This matching of resources in a non-deterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS). In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced. The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance. The algorithm is evaluated within a simulated Grid environment. The experimental results show that the new QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications. The approach is compared with others based on the quality of the prediction formulated by inaccurate information.

A Java commodity grid kit

Developing advanced applications for the emerging national‐scale ‘Computational Grid’ infrastructures is still a difficult task. Although Grid services are available that assist the application developers in authentication, remote access to computers, resource management, and infrastructure discovery, they provide a challenge because these services may not be compatible with the commodity distributed‐computing technologies and frameworks used previously.

Power-aware scheduling of virtual machines in DVFS-enabled clusters

With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2 emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.

Efficient resource management for Cloud computing environments

The notion of Cloud computing has not only reshaped the field of distributed systems but also fundamentally changed how businesses utilize computing today. While Cloud computing provides many advanced features, it still has some shortcomings such as the relatively high operating cost for both public and private Clouds. The area of Green computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. In this paper a new framework is presented that provides efficient green enhancements within a scalable Cloud computing architecture. Using power-aware scheduling techniques, variable resource management, live migration, and a minimal virtual machine design, overall system efficiency will be vastly improved in a data center based Cloud with minimal performance overhead.

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task’s execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

Analysis of Virtualization Technologies for High Performance Computing Environments

As Cloud computing emerges as a dominant paradigm in distributed systems, it is important to fully understand the underlying technologies that make Clouds possible. One technology, and perhaps the most important, is virtualization. Recently virtualization, through the use of hyper visors, has become widely used and well understood by many. However, there are a large spread of different hyper visors, each with their own advantages and disadvantages. This paper provides an in-depth analysis of some of todays commonly accepted virtualization technologies from feature comparison to performance analysis, focusing on the applicability to High Performance Computing environments using Future Grid resources. The results indicate virtualization sometimes introduces slight performance impacts depending on the hyper visor type, however the benefits of such technologies are profound and not all virtualization technologies are equal. From our experience, the KVM hyper visor is the optimal choice for supporting HPC applications within a Cloud infrastructure.

A high-throughput x-ray microtomography system at the Advanced Photon Source

~Received 14 November 2000; accepted for publication 23 January 2001!A third-generation synchrotron radiation source provides enough brilliance to acquire completetomographic data sets at 100 nm or better resolution in a few minutes. To take advantage of suchhigh-brilliance sources at the Advanced Photon Source, we have constructed a pipelined dataacquisition and reconstruction system that combines a fast detector system, high-speed datanetworks, and massively parallel computers to rapidly acquire the projection data and perform thereconstruction and rendering calculations. With the current setup, a data set can be obtained andreconstructed in tens of minutes. A specialized visualization computer makes renderedthree-dimensional~3D! images available to the beamline users minutes after the data acquisition iscompleted. This system is capable of examining a large number of samples at sub-mm 3D resolutionor studying the full 3D structure of a dynamically evolving sample on a 10 min temporal scale. Inthe near future, we expect to increase the spatial resolution to below 100 nm by using zone-platex-ray focusing optics and to improve the time resolution by the use of a broadband x-raymonochromator and a faster detector system.

Active Thermochemical Tables: thermochemistry for the 21st century

Active Thermochemical Tables (ATcT) are a good example of a significant breakthrough in chemical science that is directly enabled by the US DOE SciDAC initiative. ATcT is a new paradigm of how to obtain accurate, reliable, and internally consistent thermochemistry and overcome the limitations that are intrinsic to the traditional sequential approach to thermochemistry. The availability of high-quality consistent thermochemical values is critical in many areas of chemistry, including the development of realistic predictive models of complex chemical environments such as combustion or the atmosphere, or development and improvement of sophisticated high-fidelity electronic structure computational treatments. As opposed to the traditional sequential evolution of thermochemical values for the chemical species of interest, ATcT utilizes the Thermochemical Network (TN) approach. This approach explicitly exposes the maze of inherent interdependencies normally ignored by the conventional treatment, and allows, inter alia, a statistical analysis of the individual measurements that define the TN. The end result is the extraction of the best possible thermochemistry, based on optimal use of all the currently available knowledge, hence making conventional tabulations of thermochemical values obsolete. Moreover, ATcT offer a number of additional features that are neither present nor possible in the traditional approach. With ATcT, new knowledge can be painlessly propagated through all affected thermochemical values. ATcT also allows hypothesis testing and evaluation, as well as discovery of weak links in the TN. The latter provides pointers to new experimental or theoretical determinations that can most efficiently improve the underlying thermochemical body of knowledge.

Analysis and Provision of QoS for Distributed Grid Applications

Abstract Grid computing provides the infrastructure necessary to access and use distributed resources as part of virtual organizations. When used in this way, Grid computing makes it possible for users to participate in collaborative and distributed applications such as tele-immersion, visualization, and computational simulation. Some of these applications operate in a collaborative mode, requiring data to be stored and delivered in a timely manner. This class of applications must adhere to stringent real-time constraints and Quality-of-Service (QoS) requirements. A QoS management approach is therefore required to orchestrate and guarantee the timely interaction between such applications and services. We discuss the design and a prototype implementation of a QoS system, and demonstrate how we enable Grid applications to become QoS compliant. We validate this approach through a case study of an image processing task derived from a nanoscale structures application.