Scheduling Technology Research Articles

Threshold Power Based UE Admittance and Contention Free Resource Allocation for Interference Mitigation in Cognitive Femtocells

Femtocells are aimed at providing strong coverage in the indoor area where typical macrocell coverage is very poor. It has hugely attracted network operators and stakeholders mainly due to its simple plug and play operation and low cost. Femtocells operate on a much lower power compared to macrocell and thus provide a number of benefits including energy efficiency and frequent spatial reuse of the spectrum. Femtocells are overlaid on macrocells and designed to co-exist with them sharing the same spectrum pool. However, since they are deployed by the end user, no pre-deployment resource planning is possible. So, interference among the femtocells as well as between femtocells and macrocells remain a major bottleneck for successful operation of femtocell networks. This paper proposes a novel threshold power based admittance and contention free resource allocation for interference mitigation in cognitive femtocell networks. In our proposed scheme, a Femtocell Access Point with Cognitive radio capability known as Cognitive Femtocells (CF), sets a threshold value on the mutual interference between itself and a close by macrocell user equipment (MUE). To mitigate cross-tier interference, a CF classifies MUEs with higher than threshold interference value as Undesirable MUEs (UMUEs) and subsequently admits it as one of its user equipment. MUEs with lower than threshold interference values are classified as Desirable MUEs (DMUEs). To mitigate co-tier interference, proposed scheme introduces a scheduling engine which employs matching policy attributes and assigns resource blocks (RBs) of unique DMUEs to CFs to avoid any possible contention problems, thus providing improved co-tier interference. System level simulations have been performed to demonstrate effectiveness of scheme and significant performance improvement in terms of SINR, throughput and spectrum efficiency.

Robust gain scheduled longitudinal autopilot design for rockets during the sustaining phase

This paper explores the design of a pitch axis load factor autopilot for a class of rockets during the sustaining phase. The existence of the thrust enhances the time-variant characteristic of the rockets mathematical model which contributes to the difficulty of the autopilot design. According to the longitudinal dynamic model of the 122 mm rocket and the design goal of cruising at a constant height, a global autopilot is designed via robust gain scheduling technology regarding time as the scheduling parameter. In contrast to the full-order and the reduced-order mixed sensitivity [Formula: see text] linear compensators, it is a fixed-structure controller consisting of one proportional-integral, two proportional controllers and one first-order filter. With respect to the flight condition uncertainty throughout the sustaining phase, the robust stability is analyzed based on the structured singular value. Simulation results show that the autopilot is insensitive to measurement noises and that the flight path of the rocket can be held at a constant height in different launching angles with an additional simple height controller.

COURSR2: An Integrated Time Management System for Lifelong Learners

This article presents COURSR2, an intelligent system aiming at managing the personal time of lifelong learners in an integrated way. Lifelong learners usually try to combine learning with their professional and family duties. Taking in advance informative decisions about whether they have the necessary time resources to devote to a learning object is crucial, both for psychological and for financial reasons. COURSR2 encompasses information for all temporal aspects of a learning object. For example, a course may have lectures, with cardinality constraints over attending them, labs, mid-term exams, final exams, homework, projects and home reading. The user can also represent his non-educational activities, using the general purpose ontology of the SELFPLANNER intelligent calendar application. By subscribing to a learning object through COURSR2, the scheduling engine of SELFPLANNER’s is employed to find viable plans, allowing rescheduling of existing activities. Thus, the learner is able to perform what-if analysis about whether a set of learning objects is compatible with each other, as well as with his other commitments. The latest version of COURSR2 has been deployed and evaluated using real users.

Reliable Robust Control for the System with Input Saturation Based on Gain Scheduling

In this paper, a reliable robust discrete gain scheduling controller is designed based on the linear matrix inequalities (LMIs) and gain scheduling technology for the systems with the input saturation, system uncertainty, external disturbance and actuator failures. By the designed discrete gain scheduling controller, we can use a series of nesting ellipsoid invariant sets to strengthen the disturbance attenuation ability as strong as possible. It means that the innermost invariant ellipsoid set needs to be minimized to strengthen the disturbance attenuation ability. The dynamic performance of the closed-loop system is improved by introducing a parameter. By the Lyapunov approach, the existing conditions for the admissible controller can be formulated in the form of LMIs. The numerical simulation illustrates the effectiveness of the proposed method.

Technology for Production Scheduling of Jobs for Open Innovation and Sustainability with Fixed Processing Property on Parallel Machines

In this paper, a technology for production scheduling is addressed for the sustainability and open innovation in a manufacturing business. Methodologies for scheduling jobs on parallel machines with the fixed processing property are devised. The fixed processing property, in which a group of specific jobs can be processed on the predetermined machine, can be found in most manufacturing systems due to the quality issues. Usually, even though parallel machines can process various types of jobs, the fixed processing is preferred as to not deteriorate products’ quality in real manufacturing systems. To minimize makespan of jobs, which is defined as the final completion time of all jobs, technology for production scheduling is developed. Several heuristic algorithms are devised for solving the problem and to evaluate performance of the suggested algorithms, a series of computational experiments is performed. Results show that better solutions are obtained by the suggested algorithms in a reasonable amount of computation time. That is, if the proposed technology is applied to the production scheduling system of a real manufacturing business, it can be expected that quantity and quality of the product will be enhanced since they are influenced by the production scheduling.

BANIAN: COLLABORATIVE QUERY SYSTEM FOR STRUCTURED BIG DATA

Increased development in structural data created new technological challenges in department of research in big data and relational database. In recent survey the submission of efficient system in maintaining and analyzing PB level structured data is otherwise said to be Banian. Storage structure limits in relational database has been crossed higher level compared storage structure. It effectively integrates interactive query was in a higher scale in the storage management . It has a same query interface to the cross platform dataset . It has two importance such as compatibility and scalability . The important stages in the Banian system architecture are: (1) Storage layer with help of HDFS for the massive data where storage is distributed; (2) for parallel database using splitting and scheduling technology we can perform scheduling and execution layer; (3) standard SQL provides cross platform query interface for application layer. Query optimization to a higher range 30times improved in Banian. To provide and enhance the performance and parallel execution.

A novel modeling approach for the “end-to-end” analysis of marine ecosystems

There is a growing demand for “end-to-end” models, which are modeling tools used to analyze and understand the fundamental complexities of marine ecosystems and processes emerging from the interaction of individuals from different trophic groups with respect to the physical environment and, even, human activity. These models are valuable quantitative tools for ecosystem-based management. To explore potential answers to complex questions regarding ecosystems using these models, it is necessary to incorporate classical ontogenic changes through the life cycle of target individuals, in addition to inherited behavioral strategies, as an additional differentiating aspect, particularly when the behavior has a direct impact on the ecosystem phenomena under study. However, it is difficult to combine different fine scale time and spatial granularities to infer animal behavior and ontogenic development. This complexity has kept these two levels of analysis separated, because most current tools do not have the required computational resources and advanced software architecture. To address this issue, we propose an individual-based modeling framework that is capable of handling and unifying the two experimental categories with a comprehensive biological and behavioral model that strictly adheres to the physiological functions of ingestion, growth, and metabolism of organisms. In addition, this model incorporates the exchange and transfer of mass and energy through local interactions at all trophic levels (lower to higher), the physical environment, and anthropogenic activity. For the framework to model short time events, such as classical predator–prey interactions, while also generating long-term ecosystem emergent properties, a special interleaving scheduling engine and physical space computer model was devised, which optimizes memory and processing resources. The framework was tested through several experiments with a three-population ecosystem containing up to 40 thousand organisms evolving inside a 200,000m2 simulation environment during 12,000 model-hours; yet, requiring only a few hours of program execution on a regular personal computer. The model included various environmental physical elements, such as several hundred shelters, the number of which can be easily modified in each experiment to simulate substrate degradation and its impact on populations. With the aid of the quantitative and qualitative tools provided by the model, it was possible to observe a coupling between prey and predator population dynamics. In conclusion, we confirmed that the end-to-end model developed here could successfully generate detailed specific hypotheses about fish behavior and quantify impacts on population dynamics.

Development of a Portable Electro-Mechanical Educational Model for Variable Rate Center Pivot Irrigation Technology

Center pivot irrigation systems usually apply a relatively uniform amount of water to fields that are often inherently variable, which could lead to significant waste of water and energy. To address this issue, our team is now developing an Intelligent Center Pivot (ICP) by integrating sensor-based irrigation scheduling with variable rate irrigation technology. However, before this technology can be applied in commercial production, it is necessary to educate growers about its practicality and potential benefits. The objective of this study was to develop a portable tabletop intelligent center pivot model (ICPDemo) to demonstrate and promote adoption of the ICP technology. This paper describes an ICPDemo constructed in 2014, including the design specifications, electro-mechanical design, control strategy, and performance. The ICPDemo has performed according to design specifications and is successfully being used to demonstrate the benefits and effectiveness of ICP technology for irrigation scheduling.

Maintenance Scheduling of Volt-VAR Control Assets in Smart Distribution Networks Using Advanced Metering Infrastructure

This paper investigates a novel approach for maintenance scheduling of volt-VAR control components (VVCCs) of distribution networks with the aid of new generation of volt-VAR optimization (VVO) solutions called quasi-real-time VVO. The new quasi-real-time VVO technique optimizes distribution network using advanced metering infrastructure (AMI) data of each quasi-real-time stage. As this VVO performs automatically and online, it is necessary for VVCCs to undergo maintenance without disturbing VVO performance. Moreover, the lost benefits that could be gained by online VVO have to be minimized. Hence, this paper proposes an AMI-based VVO consisting of a VVO engine and a maintenance scheduling engine (MSE) that operate in tandem to optimize distribution network and find the optimal maintenance scheduling of different VVCCs. To test the accuracy and the applicability of the proposed solution, a 33-node distribution feeder is employed. Furthermore, five different maintenance scenarios are investigated to check the proposed VVO performance. The results prove that the integration of VVO with MSE could be a reliable approach that can solve maintenance scheduling of VVCCs without interrupting and/or resetting VVO.

Research on Resource Scheduling Technology Based on LB Algorithm and Belief Model for Cloud Computing

Research on Resource Scheduling Technology Based on LB Algorithm and Belief Model for Cloud Computing

Decision-making model to generate novel emergency response plans for improving coordination during large-scale emergencies

Developing joint emergency response plans is an effective method to coordinate multi-agency response endeavors. This study presents a novel emergency response plan structure that considers emergency command operation requirements, such as explicitly expressing the incident objective decomposition structure, formalizing decisions in a context-sensitive manner, supporting the synchronization of responding activities with variable interval, encoding complex temporal constraints, and providing temporal flexibility. A decision-making model is developed to generate these domain-specific action plans automatically based on integrating hierarchical task network (HTN) planning and scheduling technologies. This model presents several valuable contributions to existing state-based forwarding HTN planning paradigms. First, an enhanced HTN is designed to record traversed HTN exploration space for constructing of incident objective decomposition structure and decision-making contexts. Second, the model generates temporal flexible action plans that enable the handling of temporal uncertainty in the emergency response domain. A novel concurrency controlling mechanism to ensure the parallelism of response activities with variable intervals is also proposed based on the temporally enhanced planning state that represents a dynamic emergency situation. Finally, the proposed model explicitly represents the starting and ending time of all tasks in the task network to provide complete temporal flexibility. In particular, a dedicated temporal management method taking full advantage of the decomposition structure induced by the HTN planning process is proposed for propagating time constraints on the underling Simple Temporal Network (STN) incrementally. An empirical study on typhoon evacuation demonstrates that the presented model is suitable for solving real-world problems. Therefore, the decision-making model can be applied as a computational model for the development process of emergency response plans, and will be embedded as a reasoning logic in an emergency command decision support system.

Virtual machine-based simulation platform for mobile ad-hoc network-based cyber infrastructure

In modeling and simulating complex systems, such as mobile ad-hoc networks (MANETs), in defense communications, it is a major challenge to reconcile multiple important considerations: the rapidity of unavoidable changes to the software (network layers and applications), the difficulty of modeling the critical, implementation-dependent behavioral effects, the need to sustain larger scale scenarios, and the desire for faster simulations. Here we present our approach in successfully reconciling them using a virtual time-synchronized virtual machine (VM)-based parallel execution framework that accurately lifts both the devices, as well as the network communications, to a virtual time plane while retaining full fidelity. At the core of our framework is a scheduling engine that operates at the level of a hypervisor scheduler, offering a unique ability to execute multi-core guest nodes over multi-core host nodes in an accurate, virtual time-synchronized manner. In contrast to other related approaches that suffer from either speed or accuracy issues, our framework provides MANET node-wise scalability, high fidelity of software behaviors, and time-ordering accuracy. The design and development of this framework is presented, and an actual implementation based on the widely used Xen hypervisor system is described. Benchmarks with synthetic and actual applications are used to identify the benefits of our approach. The time inaccuracy of traditional emulation methods is demonstrated, in comparison with the accurate execution of our framework verified by theoretically correct results expected from analytical models of the same scenarios. In the largest high-fidelity tests, we are able to perform virtual time-synchronized simulation of 64-node VM-based full-stack, actual software behaviors of MANETs containing a mix of static and mobile (unmanned airborne vehicle) nodes, hosted on a 32-core host, with full fidelity of unmodified ad-hoc routing protocols, unmodified application executables, and user-controllable physical layer effects, including inter-device wireless signal strength, reachability, and connectivity.

Water pricing and irrigation across Europe: opportunities and constraints for adopting irrigation scheduling decision support systems

Despite the plethora of irrigation scheduling decision support systems that have been developed over the past decades, there is little evidence of widespread adoption by farmers. This paper investigates the structural, institutional and political rigidities that affect the adoption of irrigation scheduling technologies in southern European countries and highlights the corresponding opportunities. The recent implementation of water pricing policies, as required under the European Water Framework Directive, could motivate farmers to invest in technologies for improving water management. A review of irrigation water prices in southern Europe found a large range of prices both within and between countries, from 0.054–0.645 €/m3 (Greece) to 0.23–1.50 €/m3 (France). However, inadequate monitoring infrastructure and a lack of political will to impose the new water prices are giving a mixed signal to farmers. An ageing and poorly trained farm population, small farm size and low level of farm investment also impede the uptake of irrigation technologies. Within this context, European-funded research needs to consider these constraints and pay closer attention to the conversion of knowledge and innovation into successful commercial products.

A hybrid backtracking search algorithm for permutation flow-shop scheduling problem

The Permutation Flow-shop Scheduling Problem (PFSP) which is an NP-complete problem widely exists in many industrial manufacturing systems, such as motor industry, semiconductor industry, and appliance industry. Therefore, how to obtain the optimal schedule for PFSP is very important for these manufacturing systems. Many attentions from researchers and engineers have been paid to solve this problem, but the developments of more effective and efficient scheduling technologies and methods are never end. In this paper, based on a new evolutionary algorithm – Backtracking Search Algorithm (BSA), a hybrid BSA (HBSA) is proposed for PFSP with the objective to minimize the makespan. To make original BSA suitable for discrete problems, some improvements and relative techniques about original BSA, such as crossover and mutation strategies, simulated annealing (SA) mechanism used to avoid premature and random insertion local search, are presented. 29 famous benchmark problems have been used to evaluate the performance of the proposed HBSA. And several comparisons between HBSA and some other classical algorithms are conducted. The results show the effectiveness of proposed HBSA.

Energy Consumption Analysis Satisfying Time–Energy–Probability Constraints for Modern DVFS Microprocessor

ABSTRACTDynamic voltage and frequency scaling (DVFS) is a popular technique to save energy for modern microprocessor. DVFS-based energy-aware scheduling technique is a critical energy saving technology for multi-task execution on modern DVFS processor. Many DVFS-based energy-aware scheduling technologies are unsatisfying for the trade-off between optimizing scheduling length and saving energy, and most of these technologies do not consider task execution time with probability distribution in real world. In this paper, we propose an energy-aware probabilistic scheduling approach to balancing between optimizing scheduling length and saving energy in a time–energy–probability constrained multi-task uniprocessor system, and we consider that each task execution time follows a probability distribution. We first construct system models, including DVFS operating environment, task model with each task execution time following a probability distribution, and energy model. In order to balance between task scheduling length and energy consumption, we propose time–energy–probability constraints scheduling (TEPcs) problem for the DVFS processor system. To solve TEPcs problem, we use a probabilistic weighted timed automaton to model the running behaviours of the processor system. Then, we devise a polynomial time heuristic algorithm through the probabilistic weighted timed automaton to find the optimal energy path as the solution of TEPcs problem. A case study and repeated experimental analysis demonstrate that our method is feasible and effective.

Banian: A cross-platform interactive query system for structured big data

The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB level structured data called Banian. Banian overcomes the storage structure limitation of relational database and effectively integrates interactive query with large-scale storage management. It provides a uniform query interface for cross-platform datasets and thus shows favorable compatibility and scalability. Banian's system architecture mainly includes three layers: (1) a storage layer using HDFS for the distributed storage of massive data; (2) a scheduling and execution layer employing the splitting and scheduling technology of parallel database; and (3) an application layer providing a cross-platform query interface and supporting standard SQL. We evaluate Banian using PB level Internet data and the TPC-H benchmark. The results show that when compared with Hive, Banian improves the query performance to a maximum of 30 times and achieves better scalability and concurrency.

Locomotive Schedule Optimization for Da-qin Heavy Haul Railway

The main difference between locomotive schedule of heavy haul railways and that of regular rail transportation is the number of locomotives utilized for one train. One heavy-loaded train usually has more than one locomotive, but a regular train only has one. This paper develops an optimization model for the multilocomotive scheduling problem (MLSP) through analyzing the current locomotive schedule of Da-qin Railway. The objective function of our paper is to minimize the total number of utilized locomotives. The MLSP is nondeterministic polynomial (NP) hard. Therefore, we convert the multilocomotive traction problem into a single-locomotive traction problem. Then, the single-locomotive traction problem (SLTP) can be converted into an assignment problem. The Hungarian algorithm is applied to solve the model and obtain the optimal locomotive schedule. We use the variance of detention time of locomotives at stations to evaluate the stability of locomotive schedule. In order to evaluate the effectiveness of the proposed optimization model, case studies for 20 kt and 30 kt heavy-loaded combined trains on Da-qin Railway are both conducted. Compared to the current schedules, the optimal schedules from the proposed models can save 62 and 47 locomotives for 20 kt and 30 kt heavy-loaded combined trains, respectively. Therefore, the effectiveness of the proposed model and its solution algorithm are both valid.

Predictable CPU Architecture Designed for Small Real-Time Application - Concept and Theory of Operation

The purpose of this paper is to describe an predictable CPU architecture, based on the five stage pipeline assembly line and a hardware scheduler engine. We aim at developing a fine-grained multithreading implementation, named nMPRA-MT. The new proposed architecture uses replication and remapping techniques for the program counter, the register file, and the pipeline registers and is implemented with a FPGA device. An original implementation of a MIPS processor with thread interleaved pipeline is obtained, using dynamic scheduling of hard real-time tasks and interrupts. In terms of interrupts handling, the architecture uses a particular method consisting of assigning interrupts to tasks, which insures an efficient control for both the context switch, and the system real-time behavior. The originality of the approach resides in the predictability and spatial isolation of the hard real-time tasks, executed every two clock cycles. The nMPRA-MT architecture is enabled by an innovative scheme of predictable scheduling algorithm, without stalling the pipeline assembly line.

Dispatching Technology Application in India Large Scale Blackout

Scheduling technology plays an important role in avoiding the occurrence of large scope blackout. India "7.30", "7.31" blackout indicates dispatching mechanism is not reasonable. It is more important that it violates scheduling and supervision direction. The network defense mechanism is not perfect. It is lack of dynamic analysis and safety evaluation capacity. Dispatching technology can be a basis for China's high-voltage power grid construction, dispatching management and establishing a perfect accident recovery plan.

Better scheduling technology leads to better patient care.

Better scheduling technology leads to better patient care.