Multi-User Concurrent Job Scheduling Method of Network Analysis Application Based on CPU/GPU Cluster

One of the main challenges in Grid systems is designing an adaptive, scalable, and model-independent method for job scheduling to achieve a desirable degree of load balancing and system efficiency. Centralized job scheduling methods have some drawbacks, such as single point of failure and lack of scalability. Moreover, decentralized methods require a coordination mechanism with limited communications. In this paper, we propose a multi-agent approach to job scheduling in Grid, named Centralized Learning Distributed Scheduling (CLDS), by utilizing the reinforcement learning framework. The CLDS is a model free approach that uses the information of jobs and their completion time to estimate the efficiency of resources. In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level. We evaluated the efficiency of the CLDS method by designing and performing a set of experiments on a simulated Grid system under different system scales and loads. The results show that the CLDS can effectively balance the load of system even in large scale and heavy loaded Grids, while maintains its adaptive performance and scalability.

Implementation of Advanced Power Application Software to KEPCO'S Energy Management System

This paper describes the implementation of real-time stability assessment in the power system control centers of the Independent System Operator in Bosnia and Herzegovina (NOS-BiH) and, respectively, a National Dispatching System in North Africa. These solutions are conceptually different in terms of stability assessment scope and techniques -- but are very similar in terms of implementation approach. At the NIS-BiH, it provides for rapidly quantifying the risk of blackout, tracking the distance to instability in real-time, and presenting the results in meaningful and user-friendly formats. The technique is based on a fast steady-state stability algorithm that computes the maximum system loadability for the current state and for postulated contingencies. The stability application is seamlessly integrated with the SINAUT Spectrum SCADA/EMS and runs automatically after each successful execution of the State Estimator and/or Dispatcher's Power Flow. The North Africa solution is based on dynamic security analysis aimed at evaluating the transient system stability conditions in advance of performing scheduled system outages, e.g., major switching actions such as switching transmission lines or large capacitors. Practical experience results are presented and illustrated.

The operation of electric power systems can be managed remote and automatically from control centers using energy management systems (EMS). In these control centers many on-line and off-line functions are carried out periodically. Among those functions, we can list: the automatic generation control, state estimation, topological analysis, and static security analysis.

Deadline-aware job scheduling problems have been attracting attention in the application domains of scientific workflows and commercial data-center wide area networks. Satisfying all of the demands before the corresponding deadlines requires optimally spreading traffic along future times. In this paper, we propose a novel deadline-aware job scheduling method that leverages a deep reinforcement learning policy gradient algorithm. We obtain the following findings through several experiments on evaluating the proposed method. The EDF-trained initial policy with the proposed method outperforms a random initial policy. We evaluate how the training period influence the performance of the proposed method and the relationship between the training period length and the success ratio. We also demonstrate the proposed method outperformed with other job-scheduling methods, i.e., round-robin, first-come-first-served, and earliest-due-first, with the gaps between these methods and the proposed method expanding as the traffic load increased.

Guest Editorial: Situational awareness of integrated energy systems

A simulation approach to the comparative analysis of various job scheduling methods

Heterogeneous cluster environments are becoming an increasingly popular platform for executing parallel applications. Efficient heterogeneous parallel applications must account for the differences inherent in such an environment. Specifically, faster machines should possess more data items than their slower counterparts and communication should be minimized over slow network links. We propose the -Heterogeneous Bulk Synchronous Parallel (HBSP ) model, which is based on the BSP model of computation, as a framework for developing applications for heterogeneous systems. The BSP model is appropriate for -level (one communication network) heterogeneous systems. HBSP extends BSP hierarchically to address level heterogeneous machines. The utility of the model is demonstrated through the design and analysis of the gather and one-to-all broadcast operations. Experimental results demonstrate the improved performance that results from effectively exploiting the heterogeneity of the underlying system. By hiding the non-uniformity of the underlying system from the application developer, the HBSP model offers a framework that encourages the design of heterogeneous parallel software.

A new fast static security analysis algorithm, based on the interval analysis theory and probability theory, is proposed to improve the efficiency and accuracy of security analysis during power network interaction. In this paper, the characteristics of network interaction and uncertainty in the interaction process are illustrated firstly. After that the dimension reduction technique is employed in DC interval load flow calculation to determine the branch power flow efficiently. Then active power related contingency screening index is proposed, based on the DC interval analysis and the probability theory, which facilitates the screening of serious contingencies during the interactive process and improves the accuracy of contingency screening. Finally, cumulated and Gram-Charlier series expansion based AC probabilistic power flow is carried out for different operating scenarios during interactive time process. JS 1018-bus example system is introduced to validate the proposed method. Compared with traditional static security analysis, the proposed method can provide more efficient and accurate results for real time system operation.

This paper introduces a new approach of power system static security analysis based on the vulnerability index (VI) and network contribution factor (NCF) method. Vulnerability index method provides quantitative vulnerability information about generation, transmission, load condition, and the whole system. NCF method gives fast approximate power flow results due to parameter change (contingency) based on the base load flow condition and network information. The contingency list can be chosen based on NCF method and VI evaluation. Comparison with the full AC power flow method shows that this approach is promising for fast and accurate static security analysis

Decoupled State-Estimation in Energy Control Centres

Fog computing is an extended version of cloud computing, which is used to serve huge data in the Internet of Things scenario. The development of fog computing improves the equipment’s computational power and delay by using modern information technology. Additionally, this fog computing improves calculation, communique, and storage services towards the edge of a network. The applications of fog computing are smart grid, street line parking automation system, smart health-care monitoring system, mobile big data analytics, etc. Additionally, the fog computing is used to solve the issues faced by the rapid development of IoT. The acceptable quality of experience is difficult to obtain among the concentrated applications and resource constrained gadgets. The aforementioned issues is resolved by using the task scheduling in fog computing. This paper discusses the classification of job scheduling and conventional methods that are used for job scheduling in fog computing along with its advantages and limitations. This detailed analysis will help researchers to obtain a better solution for the current concerns faced in fog computing in big data processing.

The use of parallel and distributed systems has become very common in the last decade. Dividing data is one of the challenges in these types of systems. Divisible load theory (DLT) is one of the proposed methods for scheduling data distribution in parallel or distributed systems. Many researches have been done in this field, but scheduling a multi-installment heterogeneous system with two-level hierarchical topology in which communication mode is blocking has not been addressed. In this paper, we find the proper size of task for each sub tree. Finally, in the experiments section, we show that the proposed methods work correctly and give us the best scheduling.

This chapter deals with the power flow problem. The power flow algorithms include the Newton-Raphson method in both polar and rectangular forms, the Gauss-Seidel method, the DC power flow method, and all kinds of decoupled power flow methods such as fast decoupled power flow, simplified BX and XB methods, as well as decoupled power flow without major approximation. Power system state estimation derives a real-time model through the received data from a redundant measurement set. Different kinds of methods about state estimation are introduced in the chapter. Among them, the weighted least squares (WLS) state estimation methods are widely used. There are three most commonly used measurement types in power system state estimation. They are the bus power injections, the line power flows, and the bus voltage magnitudes.

In modern Power Control Centers, the State Estimator application is responsible for determining the actual state of a power network based on the current network model, the available telemetry (analog measurements and digital statuses) and pseudo-measurements (scheduling patterns and data manually entered by the operator). At present each control center runs its own version of a State Estimator. We propose that it is much more economical to run one grid-wide State Estimator at one site and just exchange input and output data with the individual control centers. In this paper we describe the architecture for a Highly Available Distributed RAM (HADRAM) system that can provide the necessary computational power and reliability.