Optimization Technique For Scheduling Research Articles

Hybrid technique for optimal task scheduling in cloud computing environments

Hybrid technique for optimal task scheduling in cloud computing environments

Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review

Based on information from recent research, by 2045, Electric Vehicles (EV) will dominate the roads with presence of more than 80% of its kind. Hence, these vehicles' grid level penetration will increase proportionally, which challenges the existing grid infrastructure in terms of its reliability and energy management capabilities. New techniques to store and consume massive quantities of energy from the power grid, as well as infusing the captive energy within the EV in response to grid demands, are emerging with the advent of electric vehicles. Everything could be handled smoothly only if we schedule the EV operation (charging/discharging) more optimally and efficiently using scheduling algorithms. Despite the existence of many routings and charging schedule computations, nature-inspired optimization approaches might play a critical role in responding to such routing challenges. Researchers have created several optimum scheduling approaches, such as Dynamic Programming, Differential Evolutionary Optimization Techniques, Collaborative Optimization Scheduling, Two-stage optimal scheduling strategy, and so on. The optimum schedule review examines the operation of an EV fleet while considering uncertainty sources and varied EV operating circumstances by integrating heuristic and meta-heuristic techniques. This paper exhibits a deep review on the various EV optimal scheduling techniques and adopted algorithms which are the emerging best practices like predictive analytics, dynamic routing, user centric planning, multi-objective optimization, etc. that reflect the industry's focus on leveraging advanced technologies, data-driven decision-making, and collaborative approaches to enhance the efficiency and sustainability of electric vehicle routing and charging scheduling.

An Optimal Network-Aware Scheduling Technique for Distributed Deep Learning in Distributed HPC Platforms

Deep learning is a growing technique used to solve complex artificial intelligence (AI) problems. Large-scale deep learning has become a significant issue as a result of the expansion of datasets and the complexity of deep learning models. For training large-scale models, the cloud can be used as a distributed HPC (high-performance computing) tool with benefits in cost and flexibility. However, one of the major performance barriers in distributed deep learning in a distributed HPC environment is the network. The performance is often limited by heavy traffic like many stochastic gradient descent transfers for distributed communication. There are many network studies in distributed deep learning to solve these problems, but most research only focuses on improving communication performance and applying new methods or algorithms like overlapping parameter synchronization to minimize communication delay rather than considering the actual network. In this paper, we are focusing on the actual network, especially in a distributed HPC environment. In such an environment, if cluster nodes are assigned to different zones/regions which means a set of an appropriate number of distributed HPC nodes when performing distributed deep learning tasks, performance degradation due to network delay may occur. The proposed network optimization algorithm ensures that distributed work is placed in the same zone as much as possible to reduce network delay. Furthermore, scoring using network monitoring tools like loss, delay, and throughput is applied to select the optimal node within the zone. Our proposal has been validated on the Kubernetes platform, an open source orchestrator for the automatic management and deployment of micro-services. The performance of distributed deep learning is improved through the proposed scheduler.

SMO Based Position Sensorless BLDC Motor Drive Employing Canonical Switching Cell Converter for Light Electric Vehicle

In this paper, an adaptive sliding mode observer (ASMO) based rotor position sensorless brushless DC (BLDC) motor drive with energy regeneration for light electric vehicle (LEV) is designed. A canonical switching cell (CSC) converter is introduced for optimized MPPT performance and soft starting of the motor is assured during sensorless start-up. The CSC converter provides ripple free current in the output to the voltage source inverter (VSI) and minimizes the losses. It also acts as a fundamental building block for a DC-DC converter with the least element counts. For position sensorless commutation of BLDC motor drive, ASMO technique with self-correction ability of commutation error is used. Estimation of rotor angular position and continuous speed tracking for closed loop control are achieved using this method. This control is robust and derived from linear model of BLDC motor. Proper stability analysis of the observer is derived and ensured using pole placement method. Optimal gain scheduling technique is used in ASMO, which reduces the order of the error function making the computation easier. This model is truly adaptive to any parameter variation and load perturbation. To ensure green mobility, a pulse-width modulated regenerative braking algorithm is also integrated using the same VSI, which avoids any additional converter. Experimental results validate this method as effective for EV applications

A Hybrid BCMPO Technique for Optimal Scheduling of Electric Vehicle Aggregators Under Market Price Uncertainty

A hybrid BCMPO technique for optimal scheduling of electric vehicle aggregators under market price uncertainty is proposed in this manuscript. The proposed method is a combined performance of balancing composite motion optimization (BCMO) and political optimizer (PO); thus, it is known as a BCMPO system. The hybrid optimization system is used in this work to explore the programming of electric vehicle aggregators under market price uncertainty. The proposed electric vehicle aggregator contributes to the market price for increasing profits. To model the uncertainty of market price with the BCMPO system, the higher and lower levels of the upstream grid prices are utilized. The results of the proposed algorithm are utilized to build numerous charging and discharging approaches that may be used via the operator for a robust EV aggregator scheduling under the uncertainty of the upstream grid price. At that time, the proposed model accomplished on MATLAB/Simulink platform performance is related to the existing systems, such as genetic algorithm (GA), particle swarm algorithm (PSO) and elephant herding optimization (EHO). The profit of EVs aggregator vs. robust profit using the proposed technique is 10110.

Retraction Note to: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

Retraction Note to: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

Optimal Distributed Generation Placement and Economic Analysis for Enhancing the Active Radial Distribution System using BPSO

Dispersed generation (DG) are known to improve the reliability of electrical grids. However, to maximize their benefits, it’s essential to ensure that DG units are optimally sized and positioned. Poor placement can have adverse effects on power grids, causing voltage profile degradation and an increase in power loss. Various methods have been recommended to determine the best size and location for DG units. This article focuses on establishing a technique for optimal scheduling and operation of DG units to reduce power-loss, improve voltage contour, and enhance network reliability. The Binary particle swarm optimization (BPSO) artificial intelligence method is implemented to find the optimal node and DG size. The applicability of BPSO is demonstrated using the IEEE 33 distribution system with Typ-1 DG, with results compared with other methods. The evidence clearly demonstrates that BPSO reigns supreme over all other methods.

A two-stage deep convolutional model for demand response energy management system in IoT-enabled smart grid

Home Energy Management System (HEMS) plays an integral role in SG that optimally schedules the appliances to achieve energy savings, cost reduction, Peak–Average Ratio (PAR) minimization via the Demand Side Management (DSM) system. Previous studies are developed to perform electricity demand scheduling for the efficient management of electricity in all the sectors. However, effective monitoring and energy management solutions are essential for the residential loads to control the operation of the appliances depending upon the consumption of power during high peak hours. For this purpose, we proposes an optimal load scheduling technique named Two Stage Deep Dilated Multi-Kernel Convolutional network (DDMKC)-Modified Elephant Herd optimization algorithm (MEHOA) approach to manages, shifts the load of the consumer and thereby lowers the electricity bill. In this model, the forecaster scheme utilizes the DR pricing information that accurately predict the future pricing signal to make optimal decision and achieve minimum degree of discomfort. Based on the forecasted future prices, the MEHOA algorithm schedule the power consumption pattern to the appliances to solve the problem residential load management issues of the consumers and thereby enhances the user comfort (UC), alleviate PAR, and lowers the payment of electricity bill. Experimental analysis is conducted in terms of several metrics like energy consumption, user comfort, and PAR measures and compared with other scheduling based optimization models. The proposed model achieves the percentage greater of 60%, 62%, and 65% for the DAP, RTP, and ToU DR pricing schemes respectively. The result shows that the proposed model effectively and efficiently.

Multi-agents based optimal energy scheduling technique for electric vehicles aggregator in microgrids

This paper presents multi-agents based optimal energy scheduling technique at microgrid level, aiming to minimize overall costs allied with the domestic energy consumption and electric vehicles charging during the particular market price and battery degradation costs. Firstly, agents-based optimal technique is presented for the distributed resource management, where local agents operate and accomplish their tasks autonomously that making the microgrid system more intelligent and reliable. Secondly, to model the actual grid voltage and price uncertainties, the proposed technique is applied in a low distribution network considering the upper and lower limits of the grid prices instead of the average/estimated prices. The problem is solved by linear programming considering the generation capabilities of the renewable energy resources and electric vehicle state of charge during the day-ahead period of 24 h. Besides, to deal with the domestic load and electric vehicles state of charge uncertainties, the simulation is carried out based on their energy consumption periods during the day while the electric vehicles initial state of charges is estimated on their daily mileage. For validation, the proposed technique is employed at a low voltage residential area and compared, which shows that the proposed technique total profit raised by 16.92% and 5.60% in comparison with the uncoordinated and stochastic techniques respectively, and guarantee the optimal energy scheduling that satisfies the consumers load demands efficiently.

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

In cloud computing environments, when a client wants to access any resources, hardware components, or application services, he needs to get a subscription for the same from service providers. The usages of each client are monitored over a network by service providers and later on user will be charged for the services used. Cloud service provider is responsible for providing Quality of Service to clients. As the number of client request increases in cloud environment, cloud service providers face various issues such as scheduling and allocation of resources, security, privacy and virtual machine migration. Swarm intelligence, biological systems, physical and chemical systems based metaheuristic algorithms have proved to be efficient and used to solve real world scheduling optimization problems. This review focused on the insight view of various nature-inspired metaheuristic algorithms and their comparisons on the basis of certain parameters that affects the efficiency and effectiveness of their applicability in order to schedule different tasks in cloud environment. This work facilitates comparative analysis of six metaheuristic techniques quantitatively based on scheduling parameters like makespan and resource utilization cost. The objective of this systematic review is to find the most optimal scheduling technique for solving multi criteria scheduling problem. After evaluating and comparing Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithm, Artificial Bee Colony algorithm, Crow Search Algorithm and Penguin Swarm Optimization Algorithm, it has been identified that Crow Search algorithm is the most optimal technique in terms of makespan and resource utilization cost parameters with significant improvement over others. Finally, the promising research directions has been identified.

Enhanced Time Quantum based Round Robin Algorithm for Cloudlet Scheduling in Cloud Environment

The contemporary organization integrates most of the required resources into their business environment by adopting or leasing the cloud resources that are offered by many of the remote cloud service providers. The cloud computing renders hassle free installation, deployment and maintenance of the resources. The cloud resources and services are observed to be pervasive in nature and therefore efficient technologies are to be adopted in order ensure the effective resource utilization and optimal service delivery to the cloud users. Generally virtualization, optimal scheduling techniques like cloudlet scheduling, virtual machine scheduling and load balancing strategies maximize the resource utilization and service delivery. This paper outlines the foundation concepts of cloud computing and proposes an algorithm named Enhanced Time Quantum based Round Robin Algorithm (ETQRRA). The ETQRRA is proposed to reduce the response time, waiting time, turnaround time and the number of context switching. The result of ETQRRA algorithm is compared with various versions of the existing Round Robin algorithms like Classic Round Robin Algorithm (CRR), Improved Shortest Remaining Burst Round Robin (ISRBRR), Half Life Variable Quantum Time Round Robin (HLVQTRR), Improved Round Robin Varying Quantum (IRRVQ) and Dynamic Time Quantum Round Robin (DTQRR) algorithms to prove the efficiency of ETQRRA. The comparative study shows the performance of ETQRRA outdo the other algorithms taken for the comparison.

A two-layer optimal scheduling framework for energy savings in a data center for Cyber–Physical–Social Systems

In recent years, big data and data analytics based on Cyber–Physical–Social Systems (CPSS) have become increasingly popular in providing valued services to humans. For many applications of CPSS, adequate computing infrastructure, which can be realized using powerful data centers (DCs), is needed. These DCs can then provide CPSS application developers with flexible and efficient High-Performance-Computing-Communications services. In DCs, the energy consumption of the cooling system which dissipates the heat generated by information technology (IT) devices should be optimized. Since the cooling system is one of the main energy consumers of DCs, optimization of its energy consumption can drastically reduce the operating costs while maintaining stable operation of the IT devices by efficient heat dissipation. Therefore, there is continuing development on improving the performance of cooling systems for DCs using different optimization strategies. In particular, model-based optimization algorithms have had impressive advances, but their deployment in real physical systems often becomes difficult due to limited data, poor optimization efficiency, and potential operation risks. In this paper, we propose a two-layer optimal scheduling framework for room-level cooling of DCs. In the global layer, we use limited data to build a set of novel physically-based empirical models to achieve accurate system energy tracking. Then with defined equipment operating constraints, a genetic algorithm efficiently obtains the optimal plan of all equipment control while ensuring safe system operations. In the local layer, through interactions with the global layer, local precision air conditioners are regulated to stabilize the room temperature within a safe range. To test our solution in a real physical system, we deployed the two-layer optimal scheduling technique in the real DC cooling system of Postal Savings Bank of China in Hefei, China. Our solution achieved an impressive average reduction of 6.1% on cooling load factor.

Quality and energy optimized scheduling technique for executing scientific workload in cloud computing environment

<p class="Abstract">Modern BigData data-intensive and scientific workload execution is challenging. The major issues are reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Extensive workload scheduling has been presented to minimize energy consumption in cloud computing (CC) environment. However, the existing workload scheduling model induces higher amount of interaction cost between inter-processors communications. Further, due to poor resource utilization, routing inefficiency these existing model induces higher energy cost and fails to meet workload QoS prerequisite. For overcoming research challenges, this paper presented quality and energy optimized scheduling (QEOS) technique for executing data-intensive workload by employing dynamic voltage and frequency scaling (DVFS) technique. Experiment outcome shows QEOS model attains good trade-off between system performance and energy consumption in multi-core cloud computing (CC) architectures when compared with existing model.</p>

A Predictive Priority-Based Dynamic Resource Provisioning Scheme With Load Balancing in Heterogeneous Cloud Computing

In cloud computing, resource provisioning is a key challenging task due to dynamic resource provisioning for the applications. As per the workload requirements of the application’s resources should be dynamically allocated for the application. Disparities in resource provisioning produce energy, cost wastages, and additionally, it affects Quality of Service (QoS) and increases Service Level Agreement (SLA) violations. So, applications allocated resources quantity should match with the applications required resources quantity. Load balancing in cloud computing can be addressed through optimal scheduling techniques, whereas this solution belongs to the NP-Complete optimization problem category. However, the cloud providers always face resource management issues for variable cloud workloads in the heterogeneous system environment. This issue has been solved by the proposed Predictive Priority-based Modified Heterogeneous Earliest Finish Time (PMHEFT) algorithm, which can estimate the application’s upcoming resource demands. This research contributes towards developing the prediction-based model for efficient and dynamic resource provisioning in a heterogamous system environment to fulfill the end user’s requirements. Existing algorithms fail to meet the user’s Quality of Service (QoS) requirements such as makespan minimization and budget constraints satisfaction, or to incorporate cloud computing principles, i.e., elasticity and heterogeneity of computing resources. In this paper, we proposed a PMHEFT algorithm to minimize the makespan of a given workflow application by improving the load balancing across all the virtual machines. Experimental results show that our proposed algorithm’s makespan, efficiency, and power consumption are better than other algorithms.

An Ancillary Services Model for Data Centers and Power Systems

Enormous energy consumption of data centers has a major impact on power systems by significantly increasing the electrical load. Due to the increase in electrical load, power systems are facing demand and supply miss-management problems. Therefore, power systems require efficient and intelligent ancillary services to maintain robustness, reliability, and stability. Data centers can provide the computational capabilities to manage power systems; however, data centers consume a tremendous amount of energy, and energy price accounts for a significant portion of their operational cost. Power system jobs will make this situation even more critical for data centers. In our work, we seek an Ancillary Services Model (ASM) to service data centers and power systems. In ASM, we find an optimal job scheduling technique for executing power systems’ jobs on data centers in terms of low power consumption, reduced makespan, and fewer preempted jobs. The power systems’ jobs include Optimal Power Flow (OPF) calculation, transmission line importance index, and bus importance index. Moreover, a Service Level Agreement (SLA) between data centers and power systems is shown to provide mutual benefits.

Optimal Scheduling and Control Techniques of Electric Vehicle Considering Flexible Grid Resources

With the continuous expansion of the scale of electric vehicles and the continuous development of intelligent control technology, it is of great significance for electric vehicles to participate in orderly charging and discharging to improve the efficiency of electric vehicles and ensure the safe and reliable operation of the power grid. Firstly, the behaviour aggregation model of electric vehicle is studied. Then, a two-layer optimal scheduling model is proposed for the operation management system of electric vehicles, which including the upper control model from the perspective of the operation of business operator and the lower control model from the perspective of electric system. Finally, a case study on the orderly charging and discharging of electric vehicles is carried out. The results show that the proposed model plays an important role in reducing load fluctuation and enhancing voltage stability.

A review of optimal operation of microgrids

The term microgrid refers to small-scale power grid that can operate autonomously or in concurrence with the area’s main electrical grid. 
 The intermittent characteristic of DGs which defies the power quality and voltage manifests the requirement for new planning and operation approaches for microgrids. Consequently, conventional optimization methods in new power systems have been critically biased all through 
 the previous decade. One of the main technological and inexpensive tools in this regard is the optimal generation scheduling of microgrid. As a primary optimization tool in the planning and operation fields, optimal operation has an undeniable part in the power system. This paper reviews and evaluates 
 the optimal operation approaches mostly related to microgrids. In this work, the foremost optimal generation scheduling approaches are compared in terms of their objective functions, techniques and constraints. To conclude, 
 a few fundamental challenges occurring from the latest optimal generation scheduling techniques in microgrids are addressed.

RETRACTED ARTICLE: MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services

In cloud computing infrastructure-based services, resource scheduling is still an open issue. Normally resource scheduling involves multi-objective fulfillment but often developed as single-objective problems and solutions are proposed. For dealing with multi-objective problems, optimization techniques come in-aid to develop various techniques as cloud resource scheduling is a soft computing problem. The ultimate aim of cloud resource scheduling is to reduce the billing cost of users and to increase the revenue of cloud service providers. In this paper, the MCAMO technique is proposed for cloud resource scheduling especially dealing with infrastructure-based cloud services. This method deals with multi-objective by applying multi constraints while resource scheduling in infrastructure cloud services. The proposed method is novel as it deals with the constraints of the submitted jobs along with fulfilling the objectives of the cloud service client. For a powerful arrangement, the fitness value worth takes a base worth value and the improved determination of the asset resources relies upon the MCAMO calculation. The performance of the MCAMO technique is assessed by comparing through few existing multi-objective constraints applied VM machines scheduling techniques using the cloudsim simulator. The comparison proves that the proposed MCAMO technique provides optimized resource scheduling than other methods.

Optimal Regulation of Pumping Station in Water Distribution Networks Using Constant and Variable Speed Pumps: A Technical and Economical Comparison

Greenhouse gas emission is one of the main environmental issues of today, and energy savings in all industries contribute to reducing energy demand, implying, in turn, less carbon emissions into the atmosphere. In this framework, water pumping systems are one of the most energy-consuming activities. The optimal regulation of pumping systems with the use of variable speed drives is gaining the attention of designers and managing authorities. However, optimal management and operation of pumping systems is often performed, employing variable speed drives without considering if the energy savings are enough to justify their purchasing and installation costs. In this paper, the authors compare two optimal pump scheduling techniques, optimal regulation of constant speed pumps by an optimal ON/OFF sequence and optimal regulation with a variable speed pump. Much of the attention is devoted to the analysis of the costs involved in a hypothetical managing authority for the water distribution system in order to determine whether the savings in operating costs is enough to justify the employment of variable speed drives.

Analysis of Socket Communication Technology Based on Machine Learning Algorithms Under TCP/IP Protocol in Network Virtual Laboratory System

When communicating between node machines in different locations in the network virtual lab system, the network layer shields the differences of the lower layer networks and cannot provide uniform data transmission of connectionless packets. Aiming at this problem, a self-correcting and optimal scheduling technique for communication networks based on deep machine learning algorithm is proposed. The branching algorithm of the algorithm mainly involves enhancing data learning. By using TCP/IP protocol for communication, a method and program implementation of communication using Socket mechanism under TCP/IP protocol are proposed. System users access the virtual lab primarily through the appropriate browser. The core part of the network virtual lab is server-side communication technology and experimental design. In a virtual lab system, a large number of nodes must be relied on for real-time communication, and the TCP/IP protocol must be followed. The Socket mechanism should be used to implement TCP/IP-based communication. Two breakthroughs were realized, resource scheduling decision and optimization of indirect system parameter selection. The experimental results show that the technical requirements of high throughput and ultra-low latency of the current virtual experimental system are realized.