Scheduling Module Research Articles

Ant Colony Optimization Algorithm to Dynamic Energy Management in Cloud Data Center

With the wide deployment of cloud computing data centers, the problems of power consumption have become increasingly prominent. The dynamic energy management problem in pursuit of energy‐efficiency in cloud data centers is investigated. Specifically, a dynamic energy management system model for cloud data centers is built, and this system is composed of DVS Management Module, Load Balancing Module, and Task Scheduling Module. According to Task Scheduling Module, the scheduling process is analyzed by Stochastic Petri Net, and a task‐oriented resource allocation method (LET‐ACO) is proposed, which optimizes the running time of the system and the energy consumption by scheduling tasks. Simulation studies confirm the effectiveness of the proposed system model. And the simulation results also show that, compared to ACO, Min‐Min, and RR scheduling strategy, the proposed LET‐ACO method can save up to 28%, 31%, and 40% energy consumption while meeting performance constraints.

Diagnostic utility of the autism diagnostic observation schedule in a clinical sample of adolescents and adults

BackgroundThe Autism Diagnostic Observation Schedule (ADOS) Module 4 was investigated in an independent clinical sample of highfunctioning adolescent and adult males and females with suspected autism spectrum disorder. Special attention was paid to the performance of the ADOS in the subgroups autism spectrum disorders (ASD), personality disorders and disorders with onset usually occurring in childhood and adolescence. MethodThe recently revised algorithm and severity scores for Module 4 were used to analyze predictive values compared to the original algorithm in a sample of N=356, 21% females, age range 12–68 yrs. ResultsBoth algorithms have good sensitivity and specificity, with slightly better results for the revised algorithm. Diagnostic accuracy is lower for females, older individuals, and individuals with personality disorders or higher intellectual abilities. ConclusionThe revised algorithm has good utility when used as part of a comprehensive assessment procedure conducted by an experienced examiner and including information on developmental history.

A scheduling procedure for a flow shop–like knowledgeable manufacturing cell with self-evolutionary features

As a component of knowledgeable manufacturing systems, the structure of flow shop–like knowledgeable manufacturing cells is similar to that of a flow shop, thus representing an NP-hard issue. Here, we propose a self-evolutionary algorithm that exhibits learning ability and is composed of learning and scheduling modules. Unlike traditional scheduling algorithms, whose performances remain unchanged when the procedure is coded, the performance of the algorithm proposed in this study gradually improves as the learning process continues. The self-evolutionary ability is realized through the training of a hybrid kernel support vector machine. The hybrid kernel support vector machine was designed to approximate the value of the Q-function to select the appropriate action for the scheduling module and thus to obtain the optimal solution. An iterative process of value based on the Q-learning was adopted to train the hybrid kernel support vector machine to gradually enhance the algorithm’s efficiency and accuracy. The extracted state features of the flow shop–like knowledgeable manufacturing cells serve as inputs to hybrid kernel support vector machine for easy generalization of the learning results. The action exerted on a feasible solution is also defined as the input of the hybrid kernel support vector machine. The computational results show that the performance of the proposed procedure improves as the learning process progresses. Data from the computation and comparisons with other algorithms verify the validity and efficiency of the proposed algorithm.

A Multi-Objective Compounded Local Mobile Cloud Architecture Using Priority Queues to Process Multiple Jobs.

As a result of the greatly increased use of mobile devices, the disadvantages of portable devices have gradually begun to emerge. To solve these problems, the use of mobile cloud computing assisted by cloud data centers has been proposed. However, cloud data centers are always very far from the mobile requesters. In this paper, we propose an improved multi-objective local mobile cloud model: Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri). This new architecture could briefly store jobs that arrive simultaneously at the cloudlet in different priority positions according to the result of auction processing, and then execute partitioning tasks on capable helpers. In the Scheduling Module, NSGA-II is employed as the scheduling algorithm to shorten processing time and decrease requester cost relative to PSO and sequential scheduling. The simulation results show that the number of iteration times that is defined to 30 is the best choice of the system. In addition, comparing with LMCque, LMCpri is able to effectively accommodate a requester who would like his job to be executed in advance and shorten execution time. Finally, we make a comparing experiment between LMCpri and cloud assisting architecture, and the results reveal that LMCpri presents a better performance advantage than cloud assisting architecture.

An optimization approach to resolve activity scheduling conflicts in ADAPTS activity-based model

Activity conflict resolution as the core of scheduling process in activity-based modeling is a challenging step because the activity diary databases mostly report the outcome of the scheduling decisions and often fail to capture key factors influencing the resolution process itself. Consequently, most activity-based frameworks ignore modeling this process by using either predefined set of activity patterns or priority-based assumptions to schedule daily activities and prevent conflict occasions. ADAPTS is one of the few activity-based models that attempts to simulate the process of activity scheduling and resolve the conflicts as they occur. This paper advances the current rule-based conflict resolution model of ADAPTS by implementing an advanced and flexible non-linear optimization model. A set of linear optimization sub-models is then proposed that together perform the same task as the non-linear model, however they are much easier to implement and maintain, while fast to run and flexible to extend. The proposed approach defines an objective function, which aims to minimize the extent of changes in timing and duration of conflicting activities, while fitting them in the schedule. Comparing performance of the proposed model with TASHA scheduler and former resolution module of ADAPTS using CHASE scheduling process data reveals significant improvement in fitting the newly planned activities in the schedules with the minimal modifications in the timing and duration of activities.

MULTIOBJECTIVE OPTIMIZATION MODEL FOR SCHEDULING OF CONSTRUCTION PROJECTS UNDER EXTREME WEATHER

Extreme weather significantly impacts construction schedules and costs and can be a source of schedule de­lays and budget overruns. A multi-objective optimization model, presented herein for the scheduling of construction projects under extreme weather conditions, can generate optimal/near optimal schedules that minimize the time and cost of construction projects in extreme weather regions. The model computations are organized as follows: (1) a scheduling module for developing practical schedules for construction projects, (2) a cost module for computing total project cost, and (3) a multi-objective module for determining optimal/near optimal trade-offs between project time and cost. Two practical examples of the effects of extreme weather on construction time and direct cost are provided, the first of which shows the impact of extreme weather on construction time and cost, and the second of which demonstrates the ability of the model to generate and visually present the optimal trade-offs between the duration and costs of construction projects under extreme weather conditions.

Understanding One's Own Emotions in Cognitively-Able Preadolescents with Autism Spectrum Disorder.

There are still no straightforward answers as to whether understanding one's own emotions is impaired in autism spectrum disorder (ASD). This study evaluated the perception of one's own different emotions, based on the relevant section of the Autism Diagnostic Observation Schedule Module 3 test. Forty boys, aged 8-11years, 20 diagnosed with ASD (IQ≥85) and 20 typically developing children were included. Description of events that elicited specific emotions in ASD was characterized by more 'odd' statements and 'no responses' and less use of content related to 'social situations', 'interpersonal' and 'self-awareness'. More 'no responses' and odd statements were associated with the severity of ASD symptoms. Clinicians should be aware of these differentiating factors during the diagnostic process of ASD.

An intelligent energy management system for large-scale charging of electric vehicles

The problem of large-scale charging of electric vehicles (EVs) with consumer-imposed charging deadlines is considered. An architecture for the intelligent energy management system (iEMS) is introduced. The iEMS consists of an admission control and pricing module, a scheduling module that determines the charging sequence, and a power dispatch module that draws power from a mix of storage, local renewable energy sources, and purchased power from the grid. A threshold admission and greedy scheduling (TAGS) policy is proposed to maximize operation profit. The performance of TAGS is analyzed and evaluated based on average and worst-case performance measures and the optimality of TAGS is established for some instances. Numerical simulations demonstrate that TAGS achieves noticeable performance gains over benchmark techniques.

Using information and mobile technology improved elderly home care services

BackgroundMany international studies recognized that home care services for elderly can reduce a health system׳s financial burden while maintaining high quality standards of patient care. Nevertheless, published research also reports that first-line care or in-charge nurses often face many challenges while delivering the home care services. To deal with the challenges, researchers have suggested applying information and mobile communication technology (IMCT) to enhance the utilization of elderly home care services. MethodsWe used a Service Oriented Architecture (SOA) to design the system structure and applied an IMCT to develop a mobile roaming sessions of the home care management system to help first-line nurses efficiently deliver care services while implementing functionality. The system includes three main modules: work scheduling, care service, and patient management. The work scheduling module used genetic algorithms to automatically generate monthly work schedules for nurses. Finally, we interviewed several users to understand the system usability. ResultsWe conducted a user experience interview to document and analyze the feasibility and efficiency of the system. The results showed that users found the system very useful in improving the home care services. Particularly, the care scheduling, care management, tour planning, and service logging functions are the most useful features of IMCT tools for improving the home care services. However, the small mobile phone screen and function integrations were main constraints in the system usability. ConclusionsWe applied IMCT to develop a mobile roaming sessions of home care management system to help first-line nurses efficiently deliver care service. The SOA approach successfully allowed us to combine existing information applications in a hospital to the ad hoc reporting in a home care management system. The system cost effectively improved elderly care services for small- to medium-sized home care centers.

A Novel Design of Software System on Chip for Embedded System

More and more transistors are integrated onto a single chip. Embedded systems have powerful cores compared with traditional ones. On-chip SRAM is also integrated to provide faster memory access, which is controlled by software and called SPM. In this paper, a novel software system on chip is proposed to take use of SPM. This system consists of microkernel, optimized scheduling module, and the support for multi-programs in parallel. Software system on chip resides in SPM, which can avoid many main memory accesses, improve the performance and reduce the power consumption. Experimental results show that the system can achieve the design goal.

Energy Consumption Estimation for Machining Processes Based on Real-time Shop Floor Monitoring via Wireless Sensor Networks

The increasing concern about the depletion of the energy repositories places the energy efficiency issues in high priority. In the manufacturing sector, the improvement of energy efficiency is a challenging task due to the complexity of manufacturing systems and the requirements for flexible operation targeting highly customised products. Towards this end, the estimation of the energy consumption of a machining task, and therefore the machining cost, is necessary. This paper presents a machine tool monitoring methodology that integrates sensory systems, a scheduling module, and human operators to perform real-time monitoring on the shop-floor. A monitoring system is designed to capture real-time measurements from sensors attached on machine tools and perform the necessary pre-processing to transmit these measurements to a Cloud server via wireless sensor networks. Furthermore, the input from human operators is utilized to collect the machining parameters. The collected information is fused through an information fusion mechanism to extract meaningful results. The results are stored in a database for the reuse in future tasks by estimating the energy consumption of new cases, through a case-based reasoning approach, prior the job dispatching. Therefore, the machining parameters of the new case can be modified targeting energy consumption reduction. The proposed system is delivered as a Cloud software-as-a-service to realise the philosophy of Cloud manufacturing.

Evaluating Model and BeiDou Based Management System for Scale Operation of Cotton-Pickers

For scale cotton-picker operation, combination of production resources including field, machine, and drivers, should be organized reasonably both in temporal and spatial dimensions. Xinjian Agri. is such a scale cotton picking service company, which owns more than 400 cotton-pickers, hires nearly 1000 personnel, and works for more than ten big farms each season. The total operation area is about 90,000 ha. In this paper, a Cotton-picker Operation Scheduling & Monitoring System (CPOSMS) was developed for Xinjian Agri. CPOSMS is a WebGIS and BeiDou based management software, which includes four main function modules. Overall scheduling module aims to help the company to create machine fleets for the farms based on operation demands and operation capacity. A real-time evaluation model was studied to adjust the rationality. Local scheduling module is to dispatch machines and personnel to form machine unit. Central navigating module is to guide staff to specific field. Operation monitoring module is to monitor and analyze operation process. Experiments in 2015 showed that the CPOSMS is the necessary tool for the company, and the evaluation model and BeiDou based system can improve management efficiency.

DIDAS – User-friendly software package for assisting drip irrigation design and scheduling

The DIDAS software package, based on analytical solutions of linearized water flow and uptake problems, assists in drip-irrigation system design and irrigation scheduling. Water flow is described by superposition of solutions for positive sources (on-surface or subsurface emitters) and negative sinks (root systems). Steady water flow is assumed in the design module and unsteady flow in the irrigation scheduling module. The design tool, based on relative water uptake rate (RWUR) criterion, assesses the effects on water use efficiency of geometrical attributes: distances between emitters along drip lines; separation between drip lines; depth of subsurface emitters; and size and depth of root systems. Evaluation of the maximum possible RWUR assumes no plant–atmosphere resistance to water uptake, i.e., the roots are assumed to apply maximum suction and the water uptake rate depends only on the soil capability to conduct water from sources to sinks. The RWUR computations require only three parameters describing the soil texture, the root zone size, and the potential evaporation, in accounting also for evaporation from the soil surface. The optimizing tool for irrigation scheduling is based on a relative water uptake volume (RWUV) criterion. The computations of diurnal variations of water uptake rates and RWUV for a given irrigation scenario require additional information on the diurnal pattern of plant resistance to water uptake and on the soil hydraulic conductivity. DIDAS also contains a diurnal pattern module for evaluating diurnal water-uptake patterns; it assumes quasi-steady flow and accounts for the diurnal variations of plant–atmosphere resistance and evaporation in fine-tuning the design and in preliminary evaluation of scheduling scenarios. DIDAS was programmed in Delphi, runs on a PC under the Windows operating system, and requires no further software. The drip irrigation scenario is constructed via a few GUI windows, which contain also a library of the required soil input parameters, and a best-fitting procedure for determining them. The computed RWURs and RWUVs are displayed graphically and the tabulated output results can be exported to, e.g., Microsoft Excel for further processing. An updated DIDAS version can be downloaded freely from http://app.agri.gov.il/didas.

A new Self-Adaptive disPatching System for local clusters

The scheduler is one of the most important components of a high performance cluster. This paper introduces a self-adaptive dispatching system (SAPS) based on Torque[1] and Maui[2]. It promotes cluster resource utilization and improves the overall speed of tasks. It provides some extra functions for administrators and users. First of all, in order to allow the scheduling of GPUs, a GPU scheduling module based on Torque and Maui has been developed. Second, SAPS analyses the relationship between the number of queueing jobs and the idle job slots, and then tunes the priority of users’ jobs dynamically. This means more jobs run and fewer job slots are idle. Third, integrating with the monitoring function, SAPS excludes nodes in error states as detected by the monitor, and returns them to the cluster after the nodes have recovered. In addition, SAPS provides a series of function modules including a batch monitoring management module, a comprehensive scheduling accounting module and a real-time alarm module. The aim of SAPS is to enhance the reliability and stability of Torque and Maui. Currently, SAPS has been running stably on a local cluster at IHEP (Institute of High Energy Physics, Chinese Academy of Sciences), with more than 12,000 cpu cores and 50,000 jobs running each day. Monitoring has shown that resource utilization has been improved by more than 26%, and the management work for both administrator and users has been reduced greatly.

Increasing task consolidation efficiency by using more accurate resource estimations

Cloud providers aim to provide computing services for a wide range of applications, such as web applications, emails, web searches, and map-reduce jobs. These applications are commonly scheduled to run on multi-sites clusters that nowadays are becoming larger and more heterogeneous. A major challenge is to efficiently utilize the cluster’s available resources, in particular to maximize overall machine utilization levels while minimizing the application waiting time. We propose a methodology for achieving an efficient utilization of the cluster’s resources while providing the users with fast and reliable computing services. The methodology consists of three main modules: (i) a prediction module that forecasts the maximum resource requirement of a task; (ii) a scheduling module that efficiently allocates tasks to machines; and (iii) a monitoring module that tracks the levels of utilization of the machines and tasks, and can evict one or more tasks from the machines for rescheduling if required.There are multiple ways of predicting task requirements, scheduling tasks on machines and evicting task from machines. The decisions made in each module can have significant impact on not only the objective function but also on the efficiency of the decisions made in other components. We therefore study these different combinations and analyze their interaction in order to determine a configuration that meets the objective of the problem. To test our methodology we have developed a simulator and provide a detailed analysis of these interactions between different modules by using a publicly available trace from a large Google cluster (∼12,000 machines). Our results show that the impact of more accurate resource estimations for the scheduling of tasks and evicting lower priority tasks in case of over-utilization can lead to an increase in the average utilization of the cluster, a reduction in the number of tasks being evicted, and a reduction in task waiting time.

A New Production Scheduling Module Using Priority-Rule Based Genetic Algorithm

Production scheduling is an important function that determines the efficiency and productivity of a production system. Many optimization methods, techniques, tools, and heuristics have been used to solve production scheduling problems, accordingly priority rules are implemented for customers’ orders in real-world applications. Simulations and heuristic methods are quite useful for making decisions, and they are used mostly to design and improve production systems by reducing their complexity. In this study, a Priority Rule-Based Genetic Algorithm Scheduling (PRGA-Sched) module was developed to provide shorter total completion time in production scheduling. The module was integrated with the Faborg-Sim simulation tool. As a case study, a heating boiler manufacturing system was analyzed and simulated with six products and customers’ orders by using production data from the PRGA-Sched module in Faborg-Sim. The results showed that a shorter total completion time is obtained and saved than the initial situation by via PRGA-Sched module. (Received in July 2014, accepted in April 2015. This paper was with the authors 3 months for 2 revisions.)

The Autism Diagnostic Observation Schedule, Module 4: Application of the Revised Algorithms in an Independent, Well-Defined, Dutch Sample (n = 93).

This study examined the discriminative ability of the revised Autism Diagnostic Observation Schedule module 4 algorithm (Hus and Lord in J Autism Dev Disord 44(8):1996–2012, 2014) in 93 Dutch males with Autism Spectrum Disorder (ASD), schizophrenia, psychopathy or controls. Discriminative ability of the revised algorithm ASD cut-off resembled the original algorithm ASD cut-off: highly specific for psychopathy and controls, lower sensitivity than Hus and Lord (2014; i.e. ASD .61, AD .53). The revised algorithm AD cut-off improved sensitivity over the original algorithm. Discriminating ASD from schizophrenia was still challenging, but the better-balanced sensitivity (.53) and specificity (.78) of the revised algorithm AD cut-off may aide clinicians’ differential diagnosis. Findings support using the revised algorithm, being conceptually conform the other modules, thus improving comparability across the lifespan.

A real-time Active Routing approach via a database for airport surface movement

Airports face challenges due to the increasing volume of air traffic and tighter environmental restrictions which result in a need to actively integrate speed profiles into conventional routing and scheduling procedure. However, only until very recently, the research on airport ground movement has started to take into account such a speed profile optimisation problem actively so that not only time efficiency but also fuel saving and decrease in airport emissions can be achieved at the same time. It is envisioned that the realism of planning could also be improved through speed profiles. However, due to the multi-objective nature of the problem and complexity of the investigated models (objective functions), the existing speed profile optimisation approach features high computational demand and is not suitable for an on-line application. In order to make this approach more competitive for real-world application and to meet limits imposed by International Civil Aviation Organization for on-line decision time, this paper introduces a pre-computed database acting as a middleware to effectively separate the planning (routing and scheduling) module and the speed profile generation module. Employing a database not only circumvents duplicative optimisation for the same taxiway segments, but also completely avoids the computation of speed profiles during the on-line decision support owing a great deal to newly proposed database initialization procedures. Moreover, the added layer of database facilitates, in the future, more complex and realistic models to be considered in the speed profile generation module, without sacrificing on-line decision time. The experimental results carried out using data from a major European hub show that the proposed approach is promising in speeding up the search process.

Female Autism Phenotypes Investigated at Different Levels of Language and Developmental Abilities.

This study investigated the differences in clinical symptoms between females and males with autism spectrum disorder (ASD) across three verbal ability groups (nonverbal, phrase and fluent speech), based on which Autism Diagnostic Observation Schedule module was administered to 5723 individuals in four research datasets. In the Simons Simplex Collection and Autism Treatment Network, females with ASD and phrase or fluent speech had lower cognitive, adaptive, and social abilities than males. In the Autism Genetics Resource Exchange and the Autism Consortium, females with phrase or fluent speech had similar or better adaptive and social abilities than males. Females who were nonverbal had similar cognitive, adaptive, and social abilities as males. Population-based longitudinal studies of verbally fluent females with ASD are needed.

Allocation and scheduling of network resource for multiple control applications in SDN

The network resource allocation in SDN for control applications is becoming a key problem in the near future because of the conflict between the need of the flow-level flexibility control and the limited capacity of flow table. Based on the analysis of the difference of the definition of network resource between SDN and traditional IP network, the idea of the integrated allocation of link bandwidth and flow table for multiple control applications in SDN is proposed in this paper. Furthermore, a price-based joint allocation model of network resource in SDN is built by introducing the price for each of the resources, which can get the proportional fair allocation of link bandwidth and the minimum global delay at the same time. We have also designed a popular flow scheduling policy based on the proportional fair allocation of link bandwidth in order to achieve the minimum global delay. A flow scheduling module has been implemented and evaluated in Floodlight, named virtual forwarding space (VFS). VFS can not only implement the fair allocation of link bandwidth and minimum delay flow scheduling in data plane but also accelerate packet forwarding by looking up control rules in control plane.