The Gain of Resource Delegation in Distributed Computing Environments

When Evolutionary Algorithms (EAs) are used for Artificial Neural Networks (ANNs) training, the most valuable advantage is the potential for this training to be done in parallel or even using distributed computing. With the capabilities of modern mobile devices, for example their use for distributed computations, they can be used much more extensively for scientific calculations. It is well known that distributed computing systems are limited by their communication bandwidth, because of network latency. In such environment some EAs are pretty suitable for distributed implementation. This is because of their high level of parallelism and relatively less intensive network communication needs. Subset of distributed computing is volunteer computing where users donate some of the computing power provided by devices under their control. This research proposes Android Live Wallpaper volunteer computing implementation of a system used for financial time series prediction. The forecasting module is organized as ANN, which is trained by hybrid combination of Backpropagation and EAs.

OF THE THESIS Pilot based frameworks for Weather Research Forecasting by Dinesh Prasanth Ganapathi Thesis Director: Dr. Shantenu Jha The Weather Research Forecasting (WRF) domain consists of complex workflows that demand the use of Distributed Computing Infrastructure (DCI). Weather forecasting requires that weather researchers use different set of initial conditions and one or a combination of physics models on the same set of input data. For these type of simulations an ensemble based computing approach becomes imperative. Most DCIs have local job-schedulers that have no smart way of dealing with the execution of an ensemble type of computational problem as the job-schedulers are built to cater to the bare essentials of resource allocation. This means the weather scientists have to submit multiple jobs to the job-scheduler. In this dissertation we use Pilot-Job based tools to decouple work-load submission and resource allocation therefore streamlining the complex workflows in Weather Research and Forecasting domain and reduce their overall time to completion. We also achieve location independent job execution, data movement, placement and processing. Next, we create the necessary enablers to run an ensemble of tasks bearing the capability to run on multiple heterogeneous distributed computing resources there by creating the opportunity to minimize the overall time consumed in running the models. Our experiments show that the tools developed exhibit very good, strong and weak scaling characteristics. These results bear the potential to change the way weather researchers are submitting traditional WRF jobs to the

Distributed computing owes its success to the development of infrastructure, middleware, and standards (e.g., CORBA) to support interoperability. The computing community has slowly recognized the need to protect information and has begun to develop commercial security infrastructures and standards. The US Government must protect national security information against unauthorized information flow. To support MLS distributed computing, a MLS infrastructure must be built that enables information sharing among users at different classification levels. This infrastructure should provide MLS services for protection of classified information and use both the emerging distributed computing and commercial security infrastructures, when possible. The resulting infrastructure will enable users to integrate commercial information technology products into their systems. In this paper, we examine the philosophy that has led to successful distributed computing among heterogeneous, autonomous components and propose an analogous approach for MLS distributed computing. We identify some services that are required to support MLS distributed computing, argue that these services are needed regardless of the MLS architecture used, present an approach for designing these services, and provide design guidance for a critical building block of the MLS infrastructure.

Properties of some shuffle/exchange type permutation sets are studied from operational points of view. The permutation sets studied are Omega , Omega /sup -1/, Psi , L, and U; Omega and Omega /sup -1/ are, respectively, the omega and inverse omega permutation sets, Omega identical to ( Omega intersection Omega /sup -1/); and L and U are, respectively, the admissible lower and upper triangular permutation sets. Several intuitive operations are introduced. Based on these operations, important known results relating to these sets are readdressed. The recursive nature of the sets is also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Parallel and distributed computing has been the basis to many emerging areas, such as smart networks, cloud computing, big data analysis, and blockchain technology. Without the development of parallel and distributed computing technologies and various types of systems, it is not possible to meet the requirement on efficiency, accuracy, scalability, and reliability for various critical applications that support our modern economy and society. While parallel and distributed computing played a vital role in modern science, engineering, biology, medicine, pharmacy, astronomy, geology, and archaeology, its application has also been extended to business, finance, economics, management, government, and defense, covering all aspects of our modern society and life. Furthermore, parallel and distributed computing has emerged in recent advances of many hotspot research directions including artificial intelligence, machine learning, Internet of Things, bioinformatics, digital medicine, cybersecurity, and social computing, resulting in numerous ground-breading discoveries that are changing our society and life.

The U.S. Army Medical Command, lead by the Brooke Army Medical Center, has embarked on a futuristic project which will revolutionize the practice of radiology in the DoD. The U.S. Army Virtual Radiology Environment (USAVRE) is a CONUS-based network that connects all the Army's major medical centers and Regional Medical Commands (RMC). The purpose of the USAVRE is to improve the quality, access, and cost of radiology services in the Army via the use of state-of-the-art medical imaging, computer, and networking technologies. The USAVRE contains multimedia-viewing workstations for static and dynamic modality cases. The storage and archiving systems are based on a distributed computing environment using Common Object Request Broker Architecture (CORBA) middleware protocols. Collaboration between archive centers and viewing workstations are managed by CORBA functions and multimedia object streams. The underlying Telecommunications network is an ATM based backbone network that connects to the RMC regional networks and PACS local networks at medical centers and RMC clinics. The U.S. Army Information Systems Engineering Command (USAISEC) at Ft. Huachuca, AZ is responsible for the ATM backbone network to the RMC sites. The virtual Radiology services in a USAVRE must be applied to several radiology modalities in a virtual network environment. In this discussion, we assume the existence of several PACS networks within a USAVRE environment that have a need to exchange multimedia images and patient information. We define a multimedia collaborative distributed computing environment (DCE) in medical imaging and radiology as a collection of collaborating PACS networks with workstations and image archive systems for the purposes of acquiring and exchanging patient static and video sequence images; storage, retrieval, and archival of those images; performing image analysis and multimedia consultation on patient cases; operation and management of the network to optimize its resources; and to improve the quality and access of the radiology services to the patients. This paper describes the open systems architecture for the USAVRE, including the PACS and Global PACS user equipment. This project is a collaborative effort between military, university, and industry centers with expertise in Teleradiology and Global PACS applications.

With the rapid development of network information technology, manufacturing presents a development trend of networking, virtualization, intelligence and servitization.Cloud manufacturing (CMg) with a new type of service-oriented manufacturing model is put forward and excellently satisfies the development requirements of modern manufacturing enterprises.With considering the complex characteristics of cloud manufacturing, firstly, we construct Cloud manufacturing resource scheduling system architecture to schedule service task and allocate manufacturing resource, which includes user requirements, task plan, global task scheduling, service collaboration allocation, virtualization and cloud manufacturing resource.Secondly, we propose a comprehensive resource allocation model based on auctions theory to satisfy different users' request, and then the greedy method is used to search the optimal bid in the bid set.Finally, we implement some simulation experiments, and simulation results show that the solution algorithm of CARA's winner determination problem gets the same solution as the M-HEU algorithm with less time complexity, and it is more applicable, efficient and effective for online multi-resource allocation.

Cloud datacenters consume enormous energy and generate heat, which affects the environment. Hence, there must be proper management of resources in the datacenter for optimum usage of energy. Virtualization enabled computing improves the performance of the datacenters in terms of these parameters. Therefore, Virtual Machines (VMs) management is a required activity in the datacenter, which selects the VMs from the overloaded host for migration, VM migration from the underutilized host, and VM placement in the suitable host. In this paper, a method (SMA-LinR) has been developed using the Simple Moving Average (SMA) integrated with Linear Regression (LinR), which predicts the CPU utilization and determines the overloading of the host. Further, this predicted value is used to place the VMs in the appropriate PM. The main aim of this research is to reduce energy consumption (EC) and service level agreement violations (SLAV). Extensive simulations have been performed on real workload data, and simulation results indicate that SMA-LinR provides better EC and service quality improvements.

A complete comparison is made between a rig-source vertical seismic profile (VSP) recorded in a deviated well performed with conventional geophones data and with distributed acoustic sensing (DAS) fiber optics. The DAS acquisition was performed using a hybrid wireline cable during the logging descent of an ultrasonic tool for casing evaluation. This rig-source VSP project offers two rich data sets for which a one-to-one comparison of the most common quality indicators in borehole seismic can be made. The objective is to highlight commonalities, advantages, and potential shortcomings of both acquisitions when performed in a real oilfield operating environment. The comparison herein presented can be of interest to the industry, specifically looking at the overall cost reduction for gathering the well seismic information, versus the value of the information that can be achieved with both technologies.

In a distributed heterogeneous computing environment, users' tasks are allocated resources to simultaneously satisfy, to varying degrees, the tasks' different, and possibly conflicting, quality of service (QoS) requirements. When the total demand placed on system resources by the tasks, for a given interval of time, exceeds the resources available, some tasks will receive degraded service or no service at all. One part of a measure to quantify the success of a resource management system (RMS) in such a distributed environment is the collective value of the tasks completed during an interval of time, as perceived by the user, application, or policy maker. The flexible integrated system capability (FISC) ratio introduced here is a measure for quantifying this collective value. The FISC ratio is a multi-dimensional measure, and may include priorities, versions of a task or data, deadlines, situational mode, security, application- and domain-specific QoS, and dependencies. In addition to being used for evaluating and comparing RMS, the FISC ratio can be incorporated as part of the objective function in a system's scheduling heuristics.

In this chapter, we give more detailed review of on-going research activities in virtual computing environment. The projects to be reviewed are not by no means a comprehensive list, but can be considered as a representative of the main project in this area.

Predictive Analytics is of great interest when it comes to enhancing Business Intelligence. Businesses have already started to use Big Data Analytics, particularly predictive and prescriptive analytics, to strengthen and increase their business yields. Not only has analytics resulted in business growth, but has also provided a significant competitive edge over others. The voluminous data generated from various resources is highly unstructured in nature and adding a structure to it would leverage the actual potential of the data. New techniques and frameworks should serve as human aids in automatically and intelligently analyzing large datasets in order to acquire useful information. In this paper, we attempt to perform Big Data Analytics on data from one of the most important and growing sources, namely, Telecommunication. To keep pace with the growing telecommunication market and ever increasing demands of the consumers for quality service, the telecom service providers are required to observe and estimate various trends in customer's usage to plan future upgrades and deployments driven by real data. We have attempted to use several data mining techniques to find hidden and interesting patterns from the telecom data generated by Telecoms Italia cellular network for the city of Milano, Italy. K-means clustering is used to categorize the usage statistics while several machine learning algorithms like Decision Tree, Random Forest, Logistic Regression and SVM are used for predicting the usage of telecom services. In the end, a performance comparison matrix is generated to rate the performance of these algorithms for the given dataset. All these experiments are performed on the big data environment set up at the supercomputing infrastructure of C-DAC. Given such a matrix, the result can be applied to similar dataset pertaining to other domains as well.

Clusters of computers have emerged as mainstream parallel and distributed platforms for high‐performance, high‐throughput and high‐availability computing. To enable effective resource management on clusters, numerous cluster management systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users' quality of service requirements. It is intended to work as an add‐on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the Portable Batch System. The scheduler offers market‐based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user‐perceived value (utility), determined by their budget and deadline rather than system performance considerations. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared with system‐centric scheduling strategies. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications.

As data centers become essential large-scale infrastructures for data processing and intelligent computing, the efficiency of their internal scheduling systems is critical for both service quality and energy consumption. The performance of these scheduling systems significantly impacts the quality of computing services and overall energy usage. However, the rapid increase in task volume, coupled with the diversity of computing resources, poses substantial challenges to traditional scheduling approaches. Conventional container scheduling approaches typically focus on either minimizing task execution time or reducing energy consumption independently, often neglecting the importance of balancing these two objectives simultaneously. In this study, a container scheduling algorithm based on the Soft Actor–Critic framework, called SAC-CS, is proposed. This algorithm aims to enhance container execution efficiency while concurrently reducing energy consumption in data centers. It employs a maximum entropy reinforcement learning approach, enabling a flexible trade-off between energy use and task completion times. Experimental evaluations on both synthetic workloads and Alibaba cluster datasets demonstrate that the SAC-CS algorithm effectively achieves joint optimization of efficiency and energy consumption, outperforming heuristic methods and alternative reinforcement learning techniques.