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Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value. [ABSTRACT FROM AUTHOR]

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment. [ABSTRACT FROM AUTHOR]

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

Cloud computing has become the most challenging research field in the current information technology scenario. In this, a set of user tasks are scheduled and allocated to numerous kinds of heterogeneous virtual machines (VMs) in cloud data centers (CDCs), and these VMs are hosted by diverse types of heterogeneous physical machines (PMs). It extends several features, encompassing elasticity, safety, sustainability, and even adequate maintenance compared to traditional data centers. There are numerous techniques available for VM scheduling and allocation. However, it still requires the existence of new mechanisms that can reduce the execution time (ET) of the tasks, improve the optimization of energy usage and resource utilization (RU), and reduce time consumption. Along with optimization, VM scheduling (VMS) and VM allocation (VMA) are two-level issues that need to be considered with essential policies to govern these mechanisms. Hence, for executing optimal VMS and VMA in the data center, new optimization methodologies, such as enhanced shark smell optimization algorithm (ESSOA) at the first level and Brownian movement-centered gravitation search algorithm (BMGSA) at the second level, are proposed in this work to define the policies. Firstly, the user requests for VMs are reserved on the most appropriate PM by the proposed ESSOA, which has the lowest execution cost within deadline limits, and the proposed BMGSA decides the allocation of the chosen VM on the most appropriate PM within the resource limitations at the second level. To demonstrate the proposed algorithm's efficiency, the simulations are carried out using the Java language-based CloudSim simulator, and the proposed mechanism outcomes acquired are compared with the existing approaches. The simulation results show that the suggested algorithm is efficient in terms of the execution cost, degree of imbalance (DOI), make span (MS), and resource utilization (RU). [ABSTRACT FROM AUTHOR]

In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection, and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection, thereby reducing the generation of SLAV, ensuring the performance, and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption. [ABSTRACT FROM AUTHOR]

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field. [ABSTRACT FROM AUTHOR]

Energy consumption is the key factor leading to high operating costs in data center. In this paper, considering the data center service level under the premise of using electricity and gas as energy supply of energy supply and energy consumption from two angles, the data center energy scheduling model is built. The lower model is calculation scheduling model for the data center, and the upper model is data center energy supply scheduling model. Then the particle swarm algorithm is used to simulate the schedule. The results show that using natural gas as supplementary energy supply can effectively reduce the overall energy consumption of data center in considering the service delay level of data center. [ABSTRACT FROM AUTHOR]

The amount of energy required by Cloud Data Centers (CDCs) has increased significantly in this digital age, and as a result, there is a pressing need to reduce CDC energy ingesting. Consolidation of virtual machines (VMs) and effective virtual machine placement (VMP) techniques are commonly employed in large data middles to reduce energy consumption. The VMP is an NP-hard subject with infeasible optimum explanations even for tiny data middles, and it is dealt with using the Metaheuristic Optimization Algorithm, which is an experiential approach to optimization. With this in mind, this study introduces a novel energy-aware VMP technique for CDCs that is founded on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA) and is enhanced for energy efficiency (EAVMP-CSSA). The EAVMP-CSSA technique attempts to reduce CDC energy ingesting by dropping the quantity of active servers supporting virtual machines. The recommended EAVMP-CSSA strategy also aims to balance the resource operation of active servers (i.e., CPU, RAM, and Bandwidth), hence reducing waste and increasing efficiency. Furthermore, by combining the ideas of chaotic maps with the standard Salp Swarm Optimization Algorithm (SSA), the CSSA is intended to improve overall performance and reduce computational costs (SSA). A comprehensive range of experimental analyses are performed to ensure that the EAVMP-CSSA technique performs better, and the findings are compared to current VMP techniques. The EAVMP-CSSA approach achieves an effective outcome with a maximum service rate of 98.12%, whereas the Random, FFD, ACO, and AP-ACO procedures achieve a minimum service rate of 74.40%, 78.80%, 90.70%, and 96.31%, respectively. The experimental results demonstrate that the EAVMP-CSSA approach outperforms other assessment metrics. [ABSTRACT FROM AUTHOR]

In order to solve the problem with the expansion of the industrial scale, the contradiction between energy resources and development of printing and dyeing enterprises must implement refined management and optimize the allocation of resources such as production technology, energy consumption types, and metering instruments. As a printing and dyeing textile industry with high energy consumption and high pollution, energy conservation and emission reduction have become difficult problem to be solved in this industry. The premise of optimizing resource allocation is to have an objective and scientific evaluation of the current energy resource allocation of printing and dyeing enterprises. In view of this, through the investigation of printing and dyeing enterprises, this paper puts forward the index system of enterprise energy consumption optimization evaluation. Based on the application of data warehouse and combined with historical data, a new energy consumption optimization evaluation method is proposed. This survey has basically understood the current situation of energy and water resources management of a printing and dyeing enterprise and pointed out the direction for the development of enterprise energy optimization project in the next step. By means of multimedia informatization, with the help of Internet of Things sensing technology, building a new generation of energy management system, improving the refined management of energy measurement, and improving the energy assessment system, enterprises can achieve significant economic and social benefits in terms of energy conservation and emission reduction. On this basis, a low-power scheduling strategy for typical data center applications is designed and implemented. The algorithm uses the input data of different calculation parts, performs matching to determine the redundant part of the calculation process, and schedules the algorithm. Experimental data show that the mean square error of the limit tree is 0.0004248 and the mean square error of the decision tree is 0.01581; through calculation, the algorithm can achieve 23% and 17% energy saving. [ABSTRACT FROM AUTHOR]

Cloud computing, an emerging computing paradigm, has been widely concerned due to its high scalability and availability. An essential stage of cloud computing is cloud resource management. Currently, the existing research about cloud computing technology has two prevalent disadvantages: high energy consumption and low resource utilization. Considering greedy scheduling is an effective strategy for cloud resource management technology in cloud computing, particularly in improving resource utilization and reducing energy consumption, we consider the heterogeneous characteristics of resources to save energy consumption of datacenter when tasks are the fundamental element of cloud datacenter. Meanwhile, granular computing is a complex problem-solving strategy through a granulation method. Thus, we introduce granular computing theory into cloud task scheduling and propose a greedy scheduling strategy based on different information granules, dividing the tasks into three types (i.e., CPU, memory, and hybrid type). Finally, we assign various scheduling strategies for cloud tasks with different characteristics. All the numerical experiments on the CloudSim platform show that our method has significant effects on energy consumption optimization and is a practical task scheduling algorithm. [ABSTRACT FROM AUTHOR]

The impact of energy on the economy must be examined not only from an energy structure standpoint but also from an economic structure standpoint, as there is a close relationship between energy structure and energy economy. To avoid the negative effects of energy consumption on the economy, it is also necessary to assess the constraints to the development of the energy economy and then optimize the energy economy and energy structure to achieve rapid economic growth. The research object in this paper is a data center powered by renewable energy, and the resource and energy consumption management model and algorithm are examined. Different adaptive scheduling algorithms are designed for different emphases, and an approximate application scheduling algorithm for renewable energy utilization based on DM is proposed. When building a data center, researching the resource management and energy consumption management strategies can help determine the best energy access scheme. It can provide the necessary reference for specific planning, such as software and hardware configuration, equipment parameter determination, and power access, resulting in lower construction and operation costs. [ABSTRACT FROM AUTHOR]

Summary: The current research investigates the effects of numbers and arrangements of the cooling units on the thermal and energy management of in‐row cooling architectures of data centers. Three different configurations with different numbers of cooling units; namely one rack between each two cooling units (Configuration A), two racks between each two cooling units (Configuration B), and three racks between each two cooling units (Configuration C), are numerically investigated. A scaled physical module of data center rack is experimentally constructed and tested for model validation. Two thermal evaluation indices (Supply Heat Index SHI and Index of Mixing IOM) and two energy efficiency metrics (Beta Index β and Energy Utilization Index ηr) are used to investigate, evaluate, and compare the thermal management and energy efficiency of the three configurations. The results show that the three configurations have good thermal homogeneity and the same good values of the thermal and energy efficiency metrics at the intermediate racks. However, problems of hot air circulations with hot spots and bad performances indices were noticed at terminal racks of the racks array. The energy utilization index is in the optimal range (about 2) for the intermediate rack and drop to 1.5 for the terminal racks. Economic study shows that configuration B has the minimum capital cost and configuration A has the minimum needed white area with a capital cost very close to that of B. Accordingly, configuration B is economically recommended, and configuration A is recommended for limited white area. [ABSTRACT FROM AUTHOR]

Summary: The high rates of electricity consumption in data centers become a worldwide concern. Water‐side free cooling is an effective solution to reduce the energy consumed by the data center's cooling system. However, the water‐side free cooling system is not well planned, and the free cooling utilization rate is low in different climate zone. Moreover, the effects of local climatic conditions on the optimal setting parameters have not been researched. In this study, the optimal setting in chilled water supply temperature, temperature difference, and chilled water return temperature is analyzed to enhance the efficiency of the free cooling system. We observed that the optimal setting could reduce annual energy consumption by 23.5%–60.8% in various climate zones and has substantial energy‐saving potential in the subtropical highland climate zone. The results show that the optimal setting parameters are highly affected by the annual accumulative wet‐bulb temperature distribution. Furthermore, considering a monthly analysis of these enhanced parameters, we proposed a dual‐working condition cooling system for the data center's future construction. This research work guides the design of such cooling systems in various climate zones. [ABSTRACT FROM AUTHOR]

Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare. [ABSTRACT FROM AUTHOR]

Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users' experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience. [ABSTRACT FROM AUTHOR]

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized. [ABSTRACT FROM AUTHOR]

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) technology is one of the energy-efficiency approaches to save energy with acceptable quality of service (QoS). In our previous studies, Artificial Bee Colony (ABC) based VM allocation policy can make a good tradeoff between performance and energy consumption. However, there are two problems in state-of-the-art ABC based approaches: (1) how to find global optimized solutions efficiently; (2) how to minimize the decision time of VM allocation. To solve these two problems, the idea of simulated annealing is adopted to get a better global optimum, and the idea of gradient descent is applied to accelerate the speed of finding solution space in Δt. Compared with state-of-the-art ABC based policies, the experimental results show that the proposed algorithm efficiently reduces energy consumption and SLA violation. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the amount of datawith time inmobile video applications such as cloud video surveillance (CVS), the increasing energy consumption in video data centers has drawn widespread attention for the past several years. Addressing the issue of reducing energy consumption, we propose a low energy consumption storage method specially designed for CVS systems based onthe service level agreement (SLA) classification. A novel SLA with an extra parameter of access time period is proposed and then utilized as a criterion for dividing virtual machines (VMs) and data storage nodes into different classifications. Tasks can be scheduled in real time for running on the homologous VMs and data storage nodes according to their access time periods. Any nodes whose access time periods do not encompass the current time will be placed into the energy saving state while others are in normal state with the capability of undertaking tasks. As a result, overall electric energy consumption in data centers is reduced while the SLA is fulfilled. To evaluate the performance, we compare the method with two related approaches using the Hadoop Distributed File System (HDFS). The results show the superiority and effectiveness of our method. [ABSTRACT FROM AUTHOR]