Optimized Job Scheduling approach based on Genetic algorithms in smart Grid environment

The advances in communications and information technologies have been playing a major role in all aspects of our lives. One of those majors aspects that affect our daily lives is the power grids which lead to what we call Smart Grids. One of the major challenges in these grids is to optimize the consumption and resources. This paper presents an optimized job scheduling approach using genetic algorithm which provides a minimum cost for completing different tasks in a grid environment.  In grid environment different independent appliances are sharing the same resources depending on the availability of resources and the need of these appliances to run. There are different job scheduling approached starting from typical strategies, Ant Colony (AC) and Genetic Algorithm (GA). In this paper we present a cost optimized Genetic Algorithm approach for appliances job scheduling by considering different parameters like job duration time, the resources availability and the job priority to start. The proposed approach is tested using a simulator written in c++ programming language. The results show that the total saving in cost is better than the previous approaches

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Group-based optimization for parallel job scheduling in clusters via heuristic search

Job scheduling for parallel processing typically makes scheduling decisions on a per job basis due to the dynamic arrival of jobs. Such decision making provides limited options to find globally best schedules. Most research uses off-line optimization which is not realistic. We propose an optimization on the basis of limited-size dynamic job grouping per priority class. We apply heuristic domain-knowledge-based hi-level search and branch-and-bound methods to heavy workload traces to capture good schedules. Special plan-based conservative backfilling and shifting policies are used to augment the search. Our objective is to minimize average relative response times for long and medium job classes, while keeping utilization high. The scheduling algorithm is extended from the SCOJO-PECT coarse-grain pre-emptive time-sharing scheduler. The proposed scheduler was evaluated using real traces and Lublin-Feitelson synthetic workload model. The comparisons were made with the conservative SCOJO-PECT scheduler. The results are promising--the average relative response times were improved by 18-32 while still able to contain the loss of utilization within 2

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Novel genetic algorithm for scheduling of appliances

YesThe introduction of smart metering has brought more detailed information on the actual load profile of a house. With the ability to measure, comes the desire to control the load profile. Furthermore, advances in renewable energy have made the consumer to become supplier, known as Prosumer, who therefore also becomes interested in the detail of his load, and also his energy production. With the lowering cost of smart plugs and other automation units, it has become possible to schedule electrical appliances. This makes it possible to adjust the load profiles of houses. However, without a market in the demand side, the use of load profile modification techniques are unlikely to be adapted by consumers on the long term. In this research, we will be presenting work on scheduling of energy appliances to modify the load profiles within a market environment. The paper will review the literature on algorithms used in scheduling of appliances in residential areas. Whilst many algorithms presented in the literature show that scheduling of appliances is feasible, many issues arise with respect to user interaction, and hence adaptation. Furthermore, the criteria used to evaluate the algorithms is often related only to reducing energy consumption, and hence CO2. Whilst this a key factor, it may not necessarily meet the demands of the consumer. In this paper we will be presenting work on a novel genetic algorithm that will optimize the load profile while taking into account user participation indices. A novel measure of the comfort of the customer, derived from the standard deviation of the load profile, is proposed in order to encourage the customer to participate more actively in demand response programs. Different scenarios will also be tested.This work was supported by the British Council and the UK Department of Business Innovation and Skills under GII funding for the SITARA project

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

Analysis of power consumption in heterogeneous virtual machine environments

Reduction of energy consumption in Cloud computing datacenters today is a hot a research topic, as these consume large amounts of energy. Furthermore, most of the energy is used inefficiently because of the improper usage of computational resources such as CPU, storage and network. A good balance between the computing resources and performed workload is mandatory. In the context of data-intensive applications, a significant portion of energy is consumed just to keep alive virtual machines or to move data around without performing useful computation. Moreover, heterogeneity of resources increases the difficulty degree, when trying to achieve energy efficiency. Power consumption optimization requires identification of those inefficiencies in the underlying system and applications. Based on the relation between server load and energy consumption, we study the efficiency of data-intensive applications, and the penalties, in terms of power consumption, that are introduced by different degrees of heterogeneity of the virtual machines characteristics in a cluster