Grid Resource Scheduling Research Articles

Secure and Latency-Aware Digital Twin Assisted Resource Scheduling for 5G Edge Computing-Empowered Distribution Grids

Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.

Energy Optimization Based on Grid Resource Scheduling Algorithm

Energy Optimization Based on Grid Resource Scheduling Algorithm

Research on Intelligent Monitoring and Maintenance Technology of Substation Based on Digital Twin

The design, research and development, test, operation and maintenance of power grid substation equipment are relatively independent. Related problems in each link cannot be comprehensively managed from the perspective of the full life cycle of equipment. Operation and maintenance personnel need to face data monitoring, network monitoring, equipment monitoring, business system monitoring, fault diagnosis and repair and other business scenarios. There are many types of information, complex association relationship, abstract and inefficient operation and maintenance information, “man-machine-object” can not be two-way cooperation and interaction, fault diagnosis and troubleshooting difficulties. Digital substation intelligent monitoring operational digital twin applications, digital intelligent substation operations provide operational monitoring service ability, realize the grid digital substation twin modeling and environment reconstruction, false or true merged superposition, two-way coordinated interaction, twin intelligence operations and panoramic visual monitoring, field devices for power grid operation maintenance, resource scheduling, and construction planning to provide guidance, Fully guarantee the safe and reliable operation of power grid equipment, realize the full life cycle management of power grid equipment, and improve the level of intelligence, visualization and digitalization of power grid.

An Power Grid Things of Internet Fog Resource Scheduling Algorithm

In the wake of developments in computer and internet, power Internet of things technology is becoming more and more mature. As an extension of cloud computing, fog computing provides computing, network, storage and network edge communication for power grid Internet of things. Due to the limitation of its own storage and computing power and the increasing business demand, it is urgent to improve the efficient and feasible resource scheduling scheme. This paper takes the fog resource scheduling in power grid as starting point, proposes a fog resource scheduling algorithm based on time series optimization for the Internet of things in the power grid, and evaluates the proposed algorithm. The results show that the algorithm has strong application value for electricity the development of Internet of things provides the possibility.

Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing

Existing static grid resource scheduling algorithms, which are limited to minimizing the makespan, cannot meet the needs of resource scheduling required by cloud computing. Current cloud infrastructure solutions provide operational support at the level of resource infrastructure only. When hardware resources form the virtual resource pool, virtual machines are deployed for use transparently. Considering the competing characteristics of multi-tenant environments in cloud computing, this paper proposes a cloud resource allocation model based on an imperfect information Stackelberg game (CSAM-IISG) using a hidden Markov model (HMM) in a cloud computing environment. CSAM-IISG was shown to increase the profit of both the resource supplier and the applicant. Firstly, we used the HMM to predict the service provider's current bid using the historical resources based on demand. Through predicting the bid dynamically, an imperfect information Stackelberg game (IISG) was established. The IISG motivates service providers to choose the optimal bidding strategy according to the overall utility, achieving maximum profits. Based on the unit prices of different types of resources, a resource allocation model is proposed to guarantee optimal gains for the infrastructure supplier. The proposed resource allocation model can support synchronous allocation for both multi-service providers and various resources. The simulation results demonstrated that the predicted price was close to the actual transaction price, which was lower than the actual value in the game model. The proposed model was shown to increase the profits of service providers and infrastructure suppliers simultaneously.

Online auction-based resource scheduling in grid computing networks

The aim of this article is to introduce a novel auction-based algorithm for grid computing wireless networks and resolve some incompetence with dynamic mechanisms. We develop a reverse online auction method to allocate grid resources, where the grid resource providers arrive dynamically and user broker has to make a multi-attribute decision whether to sell tasks or not before the end of current round. In our approach, a trade-some-with-forecast algorithm is proposed to help the user broker to utilize his forecast ability to allocate the grid resource in an online setting. Furthermore, two reverse online auction-based protocols are presented to demonstrate the resource scheduling in grid computing wireless networks. Experiments show that the reverse online auction-based with forecast protocol has better performance in comparison with the reverse online auction-based protocol. It is efficient in terms of auction stages, user satisfaction, and successful forecast.

Evaluation of different initial solution algorithms to be used in the heuristics optimization to solve the energy resource scheduling in smart grids

Over the last years, an increasing number of distributed resources have been connected to the power system due to the ambitious environmental targets, which resulted into a more complex operation of the power system. In the future, an even larger number of resources is expected to be coupled which will turn the day-ahead optimal resource scheduling problem into an even more difficult optimization problem. Under these circumstances, metaheuristics can be used to address this optimization problem. An adequate algorithm for generating a good initial solution can improve the metaheuristic’s performance of finding a final solution near to the optimal than using a random initial solution. This paper proposes two initial solution algorithms to be used by a metaheuristic technique (simulated annealing). These algorithms are tested and evaluated with other published algorithms that obtain initial solution. The proposed algorithms have been developed as modules to be more flexible their use by other metaheuristics than just simulated annealing. The simulated annealing with different initial solution algorithms has been tested in a 37-bus distribution network with distributed resources, especially electric vehicles. The proposed algorithms proved to present results very close to the optimal with a small difference between 0.1%. A deterministic technique is used as comparison and it took around 26h to obtain the optimal one. On the other hand, the simulated annealing was able of obtaining results around 1min.

Enhanced resource scheduling in Grid considering overload of different attributes

Most of scheduling methods in the Grid only consider one special attribute of the resource or one aspect of QoS (Quality of Service) of the job. In this paper, we focus on the problem that how to consider two aspects simultaneously. Based on the requirements of the jobs and the attributes of the resources, jobs are categorized into three kinds: CPU-overload, memory-overload, and bandwidth-overload jobs. One job may belong to different kinds according to different attributes. We schedule the jobs in different categories in different orders, and then propose a scheduling method-MTS (multiple attributes scheduling method) to schedule Grid resources. Based on the comparisons between our method, Min-min, ASJS (Adaptive Scoring Job Scheduling), and MRS (Multi-dimensional Scheduling) show: (1) MTS reduces the execution time more than 15% to other methods, (2) MTS improves the number of the finished jobs before the deadlines of the jobs, and (3) MTS enhances the file size of transmitted files (input files and output files) and improves the number of the instructions of the finished jobs.

Distributed resource scheduling in smart grid with electric vehicle deployment using fireworks algorithm

Global warming and climate change are two key probing issues in the present context. The electricity sector and transportation sector are two principle entities propelling both these issues. Emissions from these two sectors can be offset by switching to greener ways of transportation through the electric vehicle (EV) and renewable energy technologies (RET). Thus, effective scheduling of both resources holds the key to sustainable practice. This paper presents a scheduling scenario-based approach in the smart grid. Problem formulation with dual objective function including both emissions and cost is developed for conventional unit commitment with EV and RET deployment. In this work, the scheduling and commitment problem is solved using the fireworks algorithm which mimics explosion of fireworks in the sky to define search space and the distance between associated sparks to evaluate global minimum. Further, binary coded fireworks algorithm is developed for the proposed scheduling problem in the smart grid. Thereafter, possible scenarios in conventional as well as smart grid are put forward. Following that, the proposed methodology is simulated using a test system with thermal generators.

A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads

In this paper, a multi-objective framework is proposed for the daily operation of a Smart Grid (SG) with high penetration of sensitive loads. The Virtual Power Player (VPP) manages the day-ahead energy resource scheduling in the smart grid, considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G), while maintaining a highly reliable power for the sensitive loads. This work considers high penetration of sensitive loads, i.e. loads such as some industrial processes that require high power quality, high reliability and few interruptions. The weighted-sum approach is used with the distributed and parallel computing techniques to efficiently solve the multi-objective problem. A two-stage optimization method is proposed using a Particle Swarm Optimization (PSO) and a deterministic technique based on Mixed-Integer Linear Programming (MILP). A realistic mathematical formulation considering the electric network constraints for the day-ahead scheduling model is described. The execution time of the large-scale problem can be reduced by using a parallel and distributed computing platform. A Pareto front algorithm is applied to determine the set of non-dominated solutions. The maximization of the minimum available reserve is incorporated in the mathematical formulation in addition to the cost minimization, to take into account the reliability requirements of sensitive and vulnerable loads. A case study with a 180-bus distribution network and a fleet of 1000 gridable Electric Vehicles (EVs) is used to illustrate the performance of the proposed method. The execution time to solve the optimization problem is reduced by using distributed computing.

Heuristic Grid Resource Scheduling Algorithm based on Group of Task and Secondary Distribution

Grid resource scheduling policies directly affect the performance of the grid, so the grid resource scheduling algorithm for grid research has become a hot spot. In this paper, based on grid heterogeneity to resource scheduling of load balancing, effective resource utilization and minimize task sets the span of time (Makespan) as the goal, propose a heuristic grid resource scheduling algorithm based on Group of Task and Secondary Distribution, the basic idea is dividing the set of tasks into two groups by using the standard deviation, the one is long tasks group, another is short tasks group .The longest task in long tasks group will be allocated to the worst performance to execute, the rest can be done in the same manner. Tasks in short tasks group will be executed by the Min-Min algorithm , and then through secondary dispatch ideas to make the grid system to quickly reach each resource load balancing , improving resource utilization efficiency and minimizing the task set time span. Finally, the simulation model benchmark Braun et al. demonstrates the effectiveness of the algorithm.

Analysis the Performance of CDS Algorithm with Constrained Price and Deadline in Grid Computing

grids and P2P networks enable aggregation of resources and creation of virtual enterprises for solving large scale problems.Resource planning in Grid computing is an advanced task owing to the heterogeneous and dynamic nature of the resources. Trade based scheduling is more attractive from point of view of business.This paper describes the design, implementation and evaluation of an economic strategy based Grid resource scheduling mechanism. It takes into account the architectural features, special requirements of computational Grids with ensuring economic efficiency. The design is concentrated on two goals. Mainly, the Grid computing environment is regarded as a distributed two-sided trade market; competition occurs on both sides of the market, users and resource providers simultaneously. Next, it needs to provide an effective scheduling environment that must have the ability to offer resources with minimal delay to jobs. In this dissertation, a framework for economic strategy based Grid scheduling is proposed. The framework entities such as users, broker and resources employ Continuous Double Scheduling Algorithm to decide the final values of prices and deadlines. We evaluated the performance of the different strategies of CDS with respect to each other and with the Offer Based Scheduling.

A Survey of Evolutionary Heuristic for Job Scheduling using Grid Computing

Grid resources scheduling has become a challenge in the computational Grid. This paper reviews efficient management techniques for managing resources in Grid computing which crucially depends upon the efficient mapping of the jobs to resources according to the user’s requirements. The mapping of the jobs is an NP-Complete problem. There is no best solution for all grid computing system. In grid atmosphere Job and resource scheduling is one of the key research area in grid environment.

A REVIEW ON SCHEDULING ALGORITHMS FOR RESOURCE MANAGEMENT IN DATA GRIDS

A very promising attractive computing model is Grid Computing. To provide high performance computing, grids aims to combine the geographically distributed, power of heterogeneous , multiple domain spanning computational resources. An efficient and effective scheduling system is important for achieving the capable potentials of grids. In grid environment, scheduling systems do not work for traditional distributed environments due to classes of environments which are completely dissimilar. Over various domains, making scheduling decisions involving resources is the process known as Grid Resource Scheduling. Across the management, grid scheduling must manipulate large-scale resources so there is high complication involved in it than the local resource scheduling. In this survey, discussion are made on grid scheduling algorithm with a view from different points which are objective function, QoS constraints, static vs. dynamic, strategies dealing with dynamic behavior of resources, application models, adaption and so on. This survey mainly focused a review of the subject from scheduling algorithm viewpoint rather than covering the entire grid scheduling area.

Job Scheduling Using Coupling in Grid

The grid computing main concern is to use the resources efficiently. For achieving this many grid resource scheduling algorithms are used for the efficient use of unused resources, especially CPU. The scheduling algorithms assign single complete job to a single resource. Instead, if these algorithms consider the degree of dependency among the modules of a job, they can be allocated parallel to the different resources. This reduces the completion time of the job and the resources can be utilized to its maximum extent. Towards this, the job scheduling using coupling algorithm is proposed. This algorithm puts forward the idea of considering the coupling degree while allocating the modules of a job parallel to different resources. In this algorithm, resource selection is done by using both its functional and non-functional properties. The algorithm works in 3 phases. It groups the interdependent modules of a job into different sets using coupling in the first phase. It checks the non-functional property i.e. availability of a resource using echo procedure in the second phase and in the third phase, the sets created in first phase, are allocated parallel to different available and matching resources. From the simulation results it is observed that job scheduling using coupling algorithm gives better performance in terms of reduced turnaround time as compared to First Come First Served, Largest Task First and Minimum Execution Time scheduling algorithms.

Variable threshold-based hierarchical load balancing technique in Grid

Load balancing is an important aspect of Grid resource scheduling. This paper attempts to address the issue of load balancing in a Grid, while maintaining the resource utilization and response time for dynamic and decentralized Grid environment. Here, to its optimum value, a hierarchical load balancing technique has been analysed based on variable threshold value. The load is divided into different categories, such as lightly loaded, under-lightly loaded, overloaded, and normally loaded. A threshold value, which can be found out using load deviation, is responsible for transferring the task and flow of workload information. In order to improve response time and to increase throughput of the Grid, a random policy has been introduced to reduce the resource allocation capacity. The proposed model has been rigorously examined over the GridSim simulator using various parameters, such as response time, resource allocation efficiency, etc. Experimental results prove the superiority of the proposed technique over existing techniques, such as without load balancing, load balancing in enhanced GridSim.

OPT-Min-Min Scheduling Algorithm of Grid Resources

As a newly emerged distributed computing platform, the Gird aims at the implementation of resources sharing and collaborative computing on the internet. Research on grid resource scheduling algorithm is one of the core problems of grid technology, as well as an essential part of grid resource management. Resource scheduling in grid environment refers to the application of tasks scheduling algorithm to assign the works in the grid to the right resources. During the allocation process, the computing performance of grid resource nodes, communication parameters and loading balance, etc., shall be put into consideration. Since they are all dynamic for both the grid resources and the grid resource nodes, requirements on grid resource scheduling algorithm are relatively higher if compared to those existing parallel resource scheduling algorithm. Based on the Min-Min scheduling algorithm, the OPT- Min-Min scheduling algorithm is presented to overcome the drawbacks. Upon the basis of applying Min-Min pre- scheduling into stage one, by adapting the strategy of two- rounds scheduling, the assignment on heavy load resources are rescheduled to balance the load. Actual cases are used to illustrate the superiority of OPT-Min-Min scheduling algorithm to the Min-Min scheduling algorithm. Simulation experiments were carried out to the batch grid resource scheduling algorithms including Min-Min, Max-Min, Min- mean and OPT-Min-Min. According to the ETC generation technique used in experimental benchmarks, ETC matrix was generated. By the comparing and analyzing the Min- Min, Max-Min and Min-mean scheduling algorithm, the validity of OPT-Min-Min scheduling algorithm is proved. Index Terms—Grid; Scheduling Strategy; OPT-Min-Min; resource scheduling algorithm; load balance

Preference-based Fair Resource Sharing and Scheduling Optimization in Grid VOs

In this paper, we deal with problems of efficient resource management and scheduling in utility Grids. There are global job flows from external users along with resource owners’ local tasks upon resource non-dedication condition. Competition for resource reservation between independent users, local and global job flows substantially complicates scheduling and the requirement to provide the necessary quality of service. A meta-scheduling model, justified in this work, assumes a complex combination of job flow dispatching and application-level scheduling methods for jobs, as well as resource sharing and consumption policies established in virtual organizations (VOs) and based on economic principles. A solution to the problem of fair resource sharing among VO stakeholders with simulation studies is proposed.

Intelligent Resource Scheduling in Green Smart Grid Considering Uncertainties

Intelligent Resource Scheduling in Green Smart Grid Considering Uncertainties

Real-time Energy Resource Scheduling Considering a Real Portuguese Scenario

The development in power systems and the introduction of decentralized generation and Electric Vehicles (EVs), both connected to distribution networks, represents a major challenge in the planning and operation issues. This new paradigm requires a new energy resources management approach which considers not only the generation, but also the management of loads through demand response programs, energy storage units, EVs and other players in a liberalized electricity markets environment. This paper proposes a methodology to be used by Virtual Power Players (VPPs), concerning the energy resource scheduling in smart grids, considering day-ahead, hour-ahead and real-time scheduling. The case study considers a 33-bus distribution network with high penetration of distributed energy resources. The wind generation profile is based on a real Portuguese wind farm. Four scenarios are presented taking into account 0, 1, 2 and 5 periods (hours or minutes) ahead of the scheduling period in the hour-ahead and realtime scheduling.