Improved Chameleon Swarm Optimization-Based Load Scheduling for IoT-Enabled Cloud Environment

Internet of things(IoT)and cloud computing(CC)becomes widespread in different application domains such as business,e-commerce,healthcare,etc.The recent developments of IoT technology have led to an increase in large amounts of data from various sources.In IoT enabled cloud environment,load scheduling remains a challenging process which is applied for ensuring network stability with maximum resource utilization.The load scheduling problem was regarded as an optimization problem that is solved by metaheuristics.In this view,this study develops a new Circle Chaotic Chameleon Swarm Optimization based Load Scheduling(C3SOA-LS)technique for IoT enabled cloud environment.The proposed C3SOA-LS technique intends to effectually schedule the tasks and balance the load uniformly in such a way that maximum resource utilization can be accomplished.Besides,the presented C3SOA-LS model involves the design of circle chaotic mapping(CCM)with the traditional chameleon swarm optimization(CSO)algorithm for improving the exploration process,shows the novelty of the work.The proposed C3SOA-LS model computes an objective with the minimization of energy consumption and makespan.The experimental outcome implied that the C3SOA-LS model has showcased improved performance and uniformly balances the load over other approaches.

A new database of building-space-specific internal loads and load schedules for performance based code compliance modeling of commercial buildings

Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling.However,recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types.As a solution to this issue,this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling,and in particular code compliance modeling for commercial buildings.The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed.Then,the proposed method to develop the building-space-specific loads and load schedules is introduced.After that,the database for these building-space-specific loads and load schedules is presented.In addition,one case is studied to demonstrate the applications of these loads and load schedules.In this case study,three methods are used to develop building energy models:space-specific(using knowledge of the distribution and location of space types and applying the space-specific data in the developed database),building-level(assuming a lack of knowledge of the space types and using the building-level data in the developed database),and calculated-ratio(assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database).The energy results simulated by using these three methods are compared,which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods.Finally,this paper discusses the application scope and maintenance of this new database.

A Model Predictive Scheduling Algorithm inReal-Time Control Systems

Abstract-The ineffective utilization of power resources has attracted much attention in current years. This paper proposes a real-time distributed load scheduling algorithm considering constraints of power supply. Firstly, an objective function is designed based on the constraint, and a base load forecasting model is established when aggregating renewable generation and non-deferrable load into a power system, which aims to transform the problem of deferrable loads scheduling into a distributed optimal control problem. Then, to optimize the objective function, a real-time scheduling algorithm is presented to solve the proposed control problem. At every time step, the purpose is to minimize the variance of differences between power supply and aggregate load, which can thus ensure the effective utilization of power resources. Finally, simulation examples are provided to illustrate the effectiveness of the proposed algorithm.

Improved DHOA-Fuzzy Based Load Scheduling in IoT Cloud Environment

Internet of things (IoT) has been significantly raised owing to thedevelopment of broadband access network, machine learning (ML), big dataanalytics (BDA), cloud computing (CC), and so on. The development of IoTtechnologies has resulted in a massive quantity of data due to the existenceof several people linking through distinct physical components, indicatingthe status of the CC environment. In the IoT, load scheduling is realistictechnique in distinct data center to guarantee the network suitability by fallingthe computer hardware and software catastrophe and with right utilize ofresource. The ideal load balancer improves many factors of Quality of Service(QoS) like resource performance, scalability, response time, error tolerance,and efficiency. The scholar is assumed as load scheduling a vital problem inIoT environment. There are many techniques accessible to load scheduling inIoT environments. With this motivation, this paper presents an improved deerhunting optimization algorithm with Type II fuzzy logic (IDHOA-T2F) modelfor load scheduling in IoT environment. The goal of the IDHOA-T2F is todiminish the energy utilization of integrated circuit of IoT node and enhancethe load scheduling in IoT environments. The IDHOA technique is derivedby integrating the concepts of Nelder Mead (NM) with the DHOA. Theproposed model also synthesized the T2L based on fuzzy logic (FL) systemsto counterbalance the load distribution. The proposed model finds usefulto improve the efficiency of IoT system. For validating the enhanced loadscheduling performance of the IDHOA-T2F technique, a series of simulationstake place to highlight the improved performance. The experimental outcomesdemonstrate the capable outcome of the IDHOA-T2F technique over therecent techniques.

Intelligent Building Load Scheduling Based on Multi-Objective Multi-Verse Algorithm

In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.

Reinforcement Learning Algorithm for Solving Load Commitment Problem Considering a General Load Model

Under the smart grid paradigm, in the near future all consumers will be exposed to variable pricing schemes introduced by utilities. Hence, there is a need to develop algorithms which could be used by the consumers to schedule their loads. In this paper, load scheduling problem is formulated as a LCP （load commitment problem）. The load model is general and can model atomic and non-atomic loads. Furthermore, it can also take into consideration the relative discomfort caused by delay in scheduling any load. For this purpose, a single parameter ＂uric＂ is introduced in the load model which captures the relative discomfort caused by delay in scheduling a particular load. Guidelines for choosing this parameter are given. All the other parameters of the proposed load model can be easily specified by the consumer. The paper shows that the general LCP can be viewed as multi-stage decision making problem or a MDP （Markov decision problem）. RL （reinforcement learning） based algorithm is developed to solve this problem. The efficacy of the algorithm is investigated when the price of electricity is available in advance as well as for the case when it is random. The scalability of the approach is also investigated.

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization

A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production process.Therefore,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line.In this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line.The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter.On this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter.The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm.It combines the advantages of the two algorithms and improves their global search ability and convergence speed.The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

Interruptible Load Scheduling Model Based on an Improved Chicken Swarm Optimization Algorithm

With the continuous growth of the tertiary industry and residential loads,balancing the power supply and consumption during peak demand time has become a critical issue.Some studies try to alleviate peak load by increasing power generation on the supply side.Due to the short duration of peak load,this may cause redundant installation capacity.Alternatively,others attempt to shave peak demand by installing energy storage facilities.However,the aforementioned research did not consider interruptible load regulation when optimizing system operations.In fact,regulating interruptible load has great potential for reducing system peak load.In this paper,an interruptible load scheduling model considering the user subsidy rate is first proposed to reduce system peak load and operational costs.This model has fully addressed the constraints of minimum daily load reduction and user interruption load time.After that,by taking a community in Shanghai as an example,the improved chicken swarm optimization algorithm is applied to solve the interruptible load scheduling scheme.Finally,the simulation results validate the efficacy of the proposed optimization algorithm and indicate the significant advantages of the proposed model in alleviating the peak load and reducing operational costs.

Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network, we present a coordinated scheduling method following the divisible load scheduling paradigm. The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions. We consider a sensor network consisting of several clusters. In a cluster, after related raw data measured by source nodes are collected at the fusion node, in-network data aggregation is further considered. The scheduling strategies consist of two phases： intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Finally, numerical examples are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.

Smart homes: potentials and challenges

Decentralized distributed clean-energy sources have become an essential need for smart grids to reduce the harmful effects of conventional power plants.Smart homes with a suitable sizing process and proper energy-management schemes can share in reducing the whole grid demand and even sell clean energy to the utility.Smart homes have been introduced recently as an alternative solution to classical power-system problems,such as the emissions of thermal plants and blackout hazards due to bulk plants/transmission outages.The appliances,sources and energy storage of smart homes should be coordinated with the requirements of homeowners via a suitable energy-management scheme.Energy-management systems are the main key to optimizing both home sources and the operation of loads to maximize home-economic benefits while keeping a comfortable lifestyle.The intermittent uncertain nature of smart homes may badly affect the whole grid performance.The prospective high penetration of smart homes on a smart power grid will introduce new,unusual scenarios in both generation and loading.In this paper,the main features and requirements of smart homes are defined.This review aims also to address recent proposed smart-home energy-management schemes.Moreover,smart-grid challenges with a high penetration of smart-home power are discussed.

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of electricity.Moreover,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number optimization.Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices.Furthermore,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day.Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.

A Dynamic Load Balancing Mechanism for Distributed Systems

It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized. In this paper, we present.a dynamic load balancing mechanism (DLB). It adopts a centralized approach and is network topology independent. The DLB mechanism employs a set of thresholds which are automatically adjusted as the system load changes. lt also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease. The performance of the proposed algorithm is evaluated by intensive simulations for various parameters. The simulAtion results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings. Furthermore, compared with a previously proposed algorithm, DLB algorithm demonstrates improved performance, especially when the system is heavily loaded and the load is unevenly distributed.

Machine learning and price-based load scheduling for an optimal IoT control in the smart and frugal home

We pose and study a scheduling problem for an electric load to develop an Internet of Things(IoT)control system for power appliances,which takes advantage of real-time dynamic energy pricing.Using historical pricing data from a large U.S.power supplier,we study and compare several dynamic scheduling policies,which can be implemented in a smart home to activate a major appliance(dishwasher,washing machine,clothes dryer)at an optimal time of the day,to minimize electricity costs.We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins.The features used in the machine learning problem are hourly market,spot and day-ahead prices along with delayed label of the prior day.We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies.We observe that the delayed label has most predictive power across features,followed,on average,by spot,hourly market,and day-ahead energy prices.We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage.Our test system includes an interactive voice interface via an intelligent personal assistant.