The fully integrated multichannel broadcast system: harder than it looks

: The Range Commanders Council (RCC), specifically the Data Reduction and Computing Group (DR&CG), recognized the need for an integrated Range Resource Management and Scheduling capability that would facilitate support of all phases of test or training events. Such a tool would span the initial planning phases, the execution and evaluation of an exercise, and finally, the accounting and billing process. Currently, each range not only performs its functions differently, but the tools, systems, and steps followed are also unique to each facility. This study was initiated with the goal of providing the ranges with a document describing the areas to consider when designing and implementing such an integrated tool. A three-step approach was used in the conduct of this study First, documentation was solicited from the different RCC members who represent the DoD ranges. Information was gathered that focused on the functionality and capability of current range resource management and scheduling systems. Interviews with subject matter experts complemented the study when actual written material was not available. Second, the information from each individual range was analyzed, organized, and then compared with the other ranges. Actual requirements were identified from systems documentation or from interviews and generalized in order to present a broader definition of required capabilities. These requirements include the processes, types of data, design documentation, system architecture, and user interfaces in current use. Finally, a recommended approach for use in developing a resource management and scheduling tool was defined that takes into consideration future requirements as well as current systems development and design practices.

Performance evaluation of grid resource management and scheduling system is of key importance to the success of the grid. In this paper, we introduce a generalized stochastic Petri net (GSPN) model for such purpose. The model is simple with clarity and can be applied to other research issues on grid to provide them with performance evaluation capabilities.

In order to resolve resource management and scheduling problem in Manufacturing Grid (MG) – an application of Grid technology, we develop a resource management and scheduling system with the interaction of Manufacturing Grid Information Service (MGIS) and the Manufacturing Grid Resource Scheduler (MGRS). The former, MGIS, provides fundamental mechanisms for remote resource encapsulating, registration, and monitoring, and the latter, MGRS, performs scheduling roles as Global Process Planning (GPP) analyzing, resource discovery, resource selection, and resource mapping.

In global supply chain, the scheduling problems of large products such as ships, airplanes, space shuttles, assembled constructions, and/or automobiles are complicated by nature. New scheduling systems are often developed in order to reduce inherent computational complexity. As a result, a problem can be decomposed into small sub-problems, problems that contain independently small scheduling systems integrating into the initial problem. As one of the authors experienced, DAS (Daewoo Shipbuilding Scheduling System) has adopted a two-layered hierarchical architecture. In the hierarchical architecture, individual scheduling systems composed of a high-level dock scheduler, DAS-ERECT and low-level assembly plant schedulers, DAS-PBS, DAS-3DS, DAS-NPS, and DAS-A7 try to search the best schedules under their own constraints. Moreover, the steep growth of communication technology and logistics enables it to introduce distributed multi-nation production plants by which different parts are produced by designated plants. Therefore vertical and lateral coordination among decomposed scheduling systems is necessary. No standard coordination mechanism of multiple scheduling systems exists, even though there are various scheduling systems existing in the area of scheduling research. Previous research regarding the coordination mechanism has mainly focused on external conversation without capacity model. Prior research has heavily focuses on agent-based coordination in the area of agent research. Yet, no scheduling domain has been developed. Previous research regarding the agent-based scheduling has paid its ample attention to internal coordination of scheduling process, a process that has not been efficient. In this study, we suggest a general framework for agent-based coordination of multiple scheduling systems in global supply chain. The purpose of this study was to design a standard coordination mechanism. To do so, we first define an individual scheduling agent responsible for their own plants and a meta-level coordination agent involved with each individual scheduling agent. We then suggest variables and values describing the individual scheduling agent and meta-level coordination agent. These variables and values are represented by Backus-Naur Form. Second, we suggest scheduling agent communication protocols for each scheduling agent topology classified into the system architectures, existence or nonexistence of coordinator, and directions of coordination. If there was a coordinating agent, an individual scheduling agent could communicate with another individual agent indirectly through the coordinator. On the other hand, if there was not any coordinating agent existing, an individual scheduling agent should communicate with another individual agent directly. To apply agent communication language specifically to the scheduling coordination domain, we had to additionally define an inner language, a language that suitably expresses scheduling coordination. A scheduling agent communication language is devised for the communication among agents independent of domain. We adopt three message layers which are ACL layer, scheduling coordination layer, and industry-specific layer. The ACL layer is a domain independent outer language layer. The scheduling coordination layer has terms necessary for scheduling coordination. The industry-specific layer expresses the industry specification. Third, in order to improve the efficiency of communication among scheduling agents and avoid possible infinite loops, we suggest a look-ahead load balancing model which supports to monitor participating agents and to analyze the status of the agents. To build the look-ahead load balancing model, the status of participating agents should be monitored. Most of all, the amount of sharing information should be considered. If complete information is collected, updating and maintenance cost of sharing information will be increasing although the frequency of communication will be decreasing. Therefore the level of detail and updating period of sharing information should be decided contingently. By means of this standard coordination mechanism, we can easily model coordination processes of multiple scheduling systems into supply chain. Finally, we apply this mechanism to shipbuilding domain and develop a prototype system which consists of a dock-scheduling agent, four assembly- plant-scheduling agents, and a meta-level coordination agent. A series of experiments using the real world data are used to empirically examine this mechanism. The results of this study show that the effect of agent-based platform on coordinated scheduling is evident in terms of the number of tardy jobs, tardiness, and makespan.

Currently, the scheduling mode used by most TT&C resource scheduling system was based on the mission requirements offered by users. After the scheduling plan is formulated, the resource redundancy has been basically determined, without the advance planning of the resources. This article analyzed the data of the scheduling system, by dividing the requirements of resources into two types of “period” and “peak”. Through analyzing the periodic use of resource historical data, the periodic resource demand will be predicted and guided resources for time-dividing and domain-planning. By analyzing the allocation data of dynamic resource scheduling, the peak resource will be reserved to enable and recycle dynamically in actual. Finally the strategy was verified by simulation, which provided a different approach to improve the utilization rate of the entire network resources.KeywordsData miningPeriodic resourcePeak resource

This paper makes that many types of data change frequently over time, makes it much more difficult to use a paper card that is structured in a simple way. The rapid retrieval of information, the preservation of their relevance today can only be guaranteed by an automated system. In this article we will talk about the general architecture of an automated system, in which modern GIS, that is, has a large number of graphical and thematic databases, is combined with modeling and computational functions that are able to perform work on the basis of a database, convert spatial data into a cartographic form, make various decisions and carry out monitoring.

The practical approach and multi-agent platform development for adaptive scheduling systems for real time resource management are considered. The approach is based on concept of demand and resource networks (DRN) where agents of demands and resources operate on virtual market and continuously trying to improve their individual values of satisfaction functions that reflects given multi-criteria objectives. To achieve the best possible results agents use the virtual money account that regulates their behavior and can increase by getting bonuses or decrease by penalties depending of their individual cost functions. The key rule of designed virtual market is that any agent that is searching for new better position in schedule must compensate losses for those conflicting agents who are able and agree to change their allocations to other resources after the initial agent request, with required amount of compensation determined in the process of re-allocations. This approach allows to balance many criteria for getting consensus between agents and adaptation of the schedules “on the fly” by events without any stop and restart of the system. The developed platform includes key classes of DRN agents and protocols of their negotiations and other components that help to develop the solution manage data and visualize results of scheduling. The platform provides rapid prototyping of multi-agent systems for real time resource management and helps to reduce man-efforts and time of development. The platform was applied for developing of multi-agent scheduling systems for managing resources in aircraft jet production, load balancing in computer grid networks and energy production in power-, gas- and heating networks.

Accumulating research has shown that some cognitive deficits in recently abstinent alcoholics (e.g., cognitive flexibility, acquisition of novel skills) improve only with remediation in contrast to the spontaneous, time-dependent rebound seen for other tasks. In principle, this facilitated or experience-dependent recovery should enhance acquisition of the content of alcoholism treatment programs, but this relationship has yet to be tested empirically; previous research assessed recovery using only neuropsychological tasks presented by an experimenter. The current investigation focused on treatment-relevant remediation (acquisition of the content of a relapse-prevention [RP] program) using tasks administered by self-guided workbooks. Four groups of male alcoholics received pre- and posttesting. Between the two testing sessions, the groups received neuropsychological remediation tasks (n = 15), ecologically relevant tasks (n = 15), attention-placebo tasks (n = 16), or no intervention (n = 15). Results showed that exposure to both types of remediation produced significant cognitive recovery, with skills transferring to posttest neuropsychological measures and RP acquisition. Hence, cognitive remediation may facilitate alcoholism treatment.

Clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large‐scale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide‐area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers' or owners' requirements, and continuously adapt to changes in resource availability.The management of resources and scheduling of applications in such large‐scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java‐based discrete‐event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time‐ and space‐shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod‐G like Grid resource broker and evaluated the performance of deadline and budget constrained cost‐ and time‐minimization scheduling algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

Efficient scheduling is crucial for time and cost-effective utilization of compute resources especially for high end systems. A variety of factors need to be considered during the scheduling decisions. Power variation across the compute resources of homogeneous large-scale systems has not been considered so far. This paper discusses the impact of the power variation for parallel application scheduling. It addresses the problem of finding the optimal resource configuration for a given application that will minimize the amount of consumed energy, under pre-defined constraints on application execution time and instantaneous average power consumption. This paper presents an efficient algorithm to do so, which also considers the existing power diversity among the compute nodes (modified also at different operating CPU frequencies) of a given homogeneous High Performance Computing system. Based on this algorithm, the paper presents a plug-in, referred to as Configuration Adviser, which operates on top of a given resource management and scheduling system to advise on energy-wise optimal resource configuration for a given application, execution using which, will adhere to the specified execution time and power consumption constraints. The main goal of this plug-in is to enhance the current resource management and scheduling tools for the support of power capping for future Exascale systems, where a data center might not be able to provide cooling or electrical power for system peak consumption but only for the expected power bands.

Deep learning (DL) is gaining popularity in various fields, such as computer vision, speech identification, and self-driving vehicles. Blackwell graphical processing units (GPUs) and Grace central processing units (CPUs) are widely utilized in computing platforms, including portable gadgets and supercomputers. Optimizing performance for multiple-programming workloads on such devices is crucial, as a single application cannot typically utilize all available resources. Developing effective GPU resource administration and scheduling systems is crucial for effective distribution among several clients. Nevertheless, current versions have several flaws. Clients often provide wrong task resource requirements, resulting in cluster resource low utilization. DL-based resource scheduling without considering cluster network features leads to poor operational efficiency. To address these difficulties, this study develops a distributed data center (DDC) framework using Blackwell GPUs and Grace CPUs for resource scheduling and accelerated computing with DL. This study offers a regression model-based technique for estimating task resource requirements, preventing clients from overallocating computer resources. This paper demonstrates smart cluster network-effective scheduling approaches for both prompt and batch forms based on the resource need assessment methodologies described. Study findings demonstrate that the suggested system can improve task computation by 19% overall and reduce mean job completion duration (JCD) by 22% when evaluated against modern approaches. Furthermore, the presented optimization approaches are useful in a variety of contexts.

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.

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 & Sons, Ltd.

The processes at the construction stages are characterized by considerable complexity due to the large amount of information contained in the design and construction documentation, and its interaction. Information management requires engineering data management systems and an object information model. Technology of building information modeling is based on the development and use of a virtual model of a construction object in the form of a three-dimensional information model and related documents. The article analyzes the Polterovich-Tonis evolutionary model as applied to information modeling processes in Russia. The functional approach to the modeling of complex project management systems in construction and visual modeling of construction objects is considered. Links between various design and calculation systems, estimated systems, scheduling and resource management systems are revealed. A formal description of the conceptual model of an integration information system for design options in construction and a strategy of data exchange between the system for automation of accounting and control and the BIM-system of visual presentation was developed.

This paper discusses the implementation of a knowledge-based system for the dynamic scheduling of a two-stage production process. It is an interactive, real-time, menu-driven system written in Prolog. The system architecture is delineated and the rules and problem-solving logic to be used under various dynamic situations are described. Results of a sample system test session are included to illustrate its use.