Energy Consumption In Cloud Research Articles

Federated learning inspired Antlion based orchestration for Edge computing environment.

Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse and independent environment like Fog-Edge, resource management is a critical issue. Hence, scheduling is a vital process to enhance efficiency and allocation of resources properly to the tasks. The manuscript proposes an Artificial Neural Network (ANN) inspired Antlion algorithm for task orchestration Edge environments. Its aim is to enhance resource utilization and reduce energy consumption. Comparative analysis with different algorithms shows that the proposed algorithm balances the load on the Edge layer, which results in lower load on the cloud, improves power consumption, CPU utilization, network utilization, and reduces average waiting time for requests. The proposed model is tested for healthcare application in Edge computing environment. The evaluation shows that the proposed algorithm outperforms existing fuzzy logic algorithms. The performance of the ANN inspired Antlion based orchestration approach is evaluated using performance metrics, power consumption, CPU utilization, network utilization, and average waiting time for requests respectively. It outperforms the existing fuzzy logic, round robin algorithm. The proposed technique achieves an average cloud energy consumption improvement of 95.94%, and average Edge energy consumption improvement of 16.79%, 19.85% in average CPU utilization in Edge computing environment, 10.64% in average CPU utilization in cloud environment, and 23.33% in average network utilization, and the average waiting time decreases by 96% compared to fuzzy logic and 1.4% compared to round-robin respectively.

Empirical Comparison of Energy Efficiency Between 3-Tier and High-Speed 3-Tier Network Topologies

The high levels of energy use for cloud computing affect carbon emissions and climate change. This research thoroughly compares the energy utilisation of conventional and high-speed three-tier data centres (DCs). The green cloud simulator (GCS) was used to compare network configurations and energy-saving algorithms for each cloud's energy consumption in 10 trials. The power saver schedule algorithm used the least server energy, while the green and RR schedule algorithms used more. Despite the testing of several energy conservation approaches, core switch and aggregate switch energy usage remained unchanged. Although the high-speed three-tier DC architecture used more energy than the standard design, this is a key difference between the two network topologies. The authors discuss the power saver schedule algorithm's benefits for server energy utilisation. This study shows how three-tier DC architecture can save money by reducing core switch and aggregation switch energy use. These evaluations should include the make span coefficient, and future research should focus on DC transform energy-saving techniques.

Energy allocation and task scheduling in edge devices based on forecast solar energy with meteorological information

Offloading tasks from edge devices to the cloud is an important method to enhance the performance of the edge device. With the help of EH (Energy Harvesting) technology, the edge device can use the collected green energy to support its operations. Most offloading scheduling methods use as much green energy as the edge device collected. Unlike prior research, we consider the long-term benefits of energy. In this paper, we forecast the solar energy supply with meteorological methods which are based on the weather forecast data. Then, we use quadratic programming to allocate energy based on the forecast energy to maximize energy efficiency. Finally, we use NSGA (Non-dominated Sorting Genetic Algorithms) to offload tasks in the edge device. Simulations show that our proposed method not only minimizes the execution time and the energy consumption of clouds, but also enhances the QoE of users.

Prediction of Power Consumption Utilization in a Cloud Computing Data Centre using Kalman Filter parameters with Genetic Algorithm

Data Centre (DC) has become a critical computing infrastructure that is essential to modern society by providing services such as cloud computing, Internet of Things (IoT) and big data. However, the cost of maintaining DC continues to rise as the demand for information technology services increase and this situation is further exacerbated in a country like Nigeria where there is highly unstable power supply from the national grid. The optimization of energy consumption in cloud computing DC using Genetic Algorithm (GA) to minimize the consumption of energy thereby extending network lifespan was one of the techniques used for optimization of power consumption. But the optimization was carried out with the assumption that all the parts of the modular server that are not carrying traffic is on idle mode and not completely off which consumes extra power compare to when it is completely off. Therefore, this work proposed optimization of power consumption utilization in a cloud computing DC using Kalman Filter (KF) with GA. Historical consumption trend and network traffic is analyzed to reduce the amount spent on power with assumption that servers in the DC operate as modular units which can be powered separately as required, in contrast to keeping entire servers always powered. Data from five different servers were collected from MTN Abuja DC in Nigeria. The servers were named BSC 13, BSC 14, BSC 15, RNC 05 and RNC 06. These consist of data recorded for two year - 5th January to 30th December 2019 as well as 5th January to 31st December 2020. The GA optimizer is used to obtain the best possible values for the Kalman Filter (KF) parameters. Then, the KF model is used to predict the future power consumption value on hourly basis for each day of the week. The proposed model gives low power consumption with accurate prediction when compared with the existing models.

A green scheduling algorithm for cloud-based honeynets

Modern businesses leverage cloud architecture to achieve agile and cost-effective technology services. Doing so comes at the expense of the environment though cloud technologies consume large quantities of energy. Cloud energy consumption is concerning in light of global climate trends and dwindling fossil fuel reserves. Consequently, increasing attention is given to sustainable and green cloud computing, which seeks to optimize compute-resource allocation and usage of virtualized systems and services. At the same time, progress toward sustainable and green cloud technology is impeded because as more enterprises deploy services into cloud architecture, cybersecurity threats follow. Unfortunately, cybersecurity technologies are optimized for maximum service overwatch without regard for compute resources and energy. This negates the energy reduction achieved in recent sustainable technology advancements. In this work, a generalized cybersecurity honeynet scheduling algorithm is proposed, in which power, CPU, and network overhead are operationalized to increase sustainability while balancing defensive mechanisms. The work describes both the mathematical foundation for the algorithm and a pseudocode proof of concept.

Optimizing cloud resource scheduling for enhanced energy efficiency

Energy consumption in cloud computing plays a vital role in operating costs for both the service provider and the cloud user. The cloud is scalable and can provide access as per demand. Due to this, resource access requests are increasing and submitted to the server. To manage all requests, scheduling is the solution to assign requests with the quality of service. To avoid high operating costs, resource scheduling needs to be energy-aware. In this paper, energy-aware resource scheduling in the cloud is proposed. The total resource utilization of each resource has been calculated, and energy is optimized through the antlion optimization algorithm to avoid high power consumption. The resource is identified with its best utilization value and assigned to submitted workloads on a priority basis. The experimental results of the proposed work are analyzed with existing autonomic frameworks, and it is found that the proposed work performs exceptionally well. In addition to addressing the increasing resource access requests, the proposed energy-aware resource scheduling in cloud computing also focuses on optimizing overall system performance. The antlion optimization algorithm is employed to efficiently manage energy consumption, ensuring that power usage is minimized without compromising the quality of service. The algorithm calculates the total resource utilization for each component in the cloud infrastructure, allowing for a thorough understanding of system dynamics. The paper optimizes cloud computing by prioritizing efficient and capacity-based resource assignment, improving responsiveness, user experience, and reducing costs. The approach also promotes environmental sustainability. Experimental results validate its effectiveness in energy efficiency and system responsiveness, contributing to advanced resource management in cloud computing for sustainable and cost-effective services.

Dynamic virtual machine relocation system for energy‐efficient resource management in the cloud

SummaryCloud services can be expanded and offered in a flexible manner depending on the needs of the users, and cloud computing has become more popular. The energy consumption to provide users with service quality is the biggest concern in virtualization technology. Therefore, reducing energy consumption is a key step in reducing the operating costs of various organizations' data centers. In this article, we propose a dynamic VM relocation (DVMR) system used inside data centers to study virtualization technologies for reducing energy consumption in cloud computing environments. The DVMR system provides a method for predicting PM load by using and selecting loaded and unloaded PM, thereby effectively managing virtualization and energy saving. Experimental evaluation is done by implementing load threshold‐based relocation. The results show that the proposed system can efficiently balance the load between different PMs and efficiently decrease the energy consumption of the entire system.

Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform

In the era of information explosion, the energy consumption of cloud data centers is significant. It’s critical to reduce the energy consumption of large-scale data centers while guaranteeing quality of service (QoS), especially the energy consumption of video cloud computing platforms. The application of virtual machine (VM) consolidation has been regarded as a promising approach to improve resource utilization and save energy of the data centers. In this paper, an energy efficient and QoS-aware VM consolidation method is proposed to address the issues. A combined prediction model based on grey model and ARIMA is applied to host status detection, and we provide a new scheme that VM placement policy based on resource utilization and varying energy consumption to search most suitable host and VM selection policy called AUMT selecting VM with low average CPU utilization and migration time. Extensive experimental results based on the cloudsim simulator demonstrate that proposed approach enables to achieve the objectives reducing energy consumption, number of migrations, SLAV and ESV by an average of 56.07%, 79.21%, 91.01% and 84.34% compared with the benchmark methods and the AUMT can reduce energy consumption, the number of migrations and ESV by an average of 15.46%, 28.11% and 3.96% compared with the state-of-the-art method.

Resource placement strategy optimization for smart grid application using 5G wireless networks

<p>With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.</p>

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

AbstractRecently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching‐learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi‐objective problem, which we call VMP‐TLBO. VMP‐TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP‐TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP‐TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.

Method for evaluation on energy consumption of cloud computing data center based on deep reinforcement learning

In view of the problem that the current method for evaluation on energy consumption of cloud computing data center has larger fluctuations of relative error and deviation value for evaluated power variation and greater evaluation training losses, this paper proposes an energy consumption evaluation method for cloud computing data center. Firstly, we build an energy consumption evaluation model, analyze the correlation between independent variables and dependent variables through multiple linear regression model and multiple nonlinear regression model, and then measure the accuracy of the evaluation results by using average relative deviation and relative deviation; The depth learning mechanism is introduced, and the depth residual network is used for iterative training of regression parameters to complete the mapping of regression parameters; Comprehensively obtain the regression parameters in the energy consumption model, put them into the memory playback pool, solve the energy consumption evaluation function according to the sub strategy parameters, and reconstruct the energy consumption model of cloud computing data center. The comprehensive evaluation of energy consumption of cloud computing data center is realized. The experimental results show that the energy consumption deviation of this method is small and stable. It can effectively reduce the relative error of evaluation power change and reduce the evaluation training loss.

Cloud-edge collaboration based transferring prediction of building energy consumption

Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge.

Optimization of energy consumption in cloud computing datacenters

Cloud computing has emerged as a practical paradigm for providing IT resources, infrastructure and services. This has led to the establishment of datacenters that have substantial energy demands for their operation. This work investigates the optimization of energy consumption in cloud datacenter using energy efficient allocation of tasks to resources. The work seeks to develop formal optimization models that minimize the energy consumption of computational resources and evaluates the use of existing optimization solvers in testing these models. Integer linear programming (ILP) techniques are used to model the scheduling problem. The objective is to minimize the total power consumed by the active and idle cores of the servers’ CPUs while meeting a set of constraints. Next, we use these models to carry out a detailed performance comparison between a selected set of Generic ILP and 0-1 Boolean satisfiability based solvers in solving the ILP formulations. Simulation results indicate that in some cases the developed models have saved up to 38% in energy consumption when compared to common techniques such as round robin. Furthermore, results also showed that generic ILP solvers had superior performance when compared to SAT-based ILP solvers especially as the number of tasks and resources grow in size.

Proactive Virtual Machine Scheduling to Optimize the Energy Consumption of Computational Cloud

The rapid expansion of communication and computational technology provides us the opportunity to deal with the bulk nature of dynamic data. The classical computing style is not much effective for such mission-critical data analysis and processing. Therefore, cloud computing is become popular for addressing and dealing with data. Cloud computing involves a large computational and network infrastructure that requires a significant amount of power and generates carbon footprints (CO2). In this context, we can minimize the cloud's energy consumption by controlling and switching off ideal machines. Therefore, in this paper, we propose a proactive virtual machine (VM) scheduling technique that can deal with frequent migration of VMs and minimize the energy consumption of the cloud using unsupervised learning techniques. The main objective of the proposed work is to reduce the energy consumption of cloud datacenters through effective utilization of cloud resources by predicting the future demand of resources. In this context four different clustering algorithms, namely K-Means, SOM (Self Organizing Map), FCM (Fuzzy C Means), and K-Mediod are used to develop the proposed proactive VM scheduling and find which type of clustering algorithm is best suitable for reducing the energy uses through proactive VM scheduling. This predictive load-aware VM scheduling technique is evaluated and simulated using the Cloud-Sim simulator. In order to demonstrate the effectiveness of the proposed scheduling technique, the workload trace of 29 days released by Google in 2019 is used. The experimental outcomes are summarized in different performance matrices, such as the energy consumed and the average processing time. Finally, by concluding the efforts made, we also suggest future research directions.

Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center

Dynamic Voltage and Frequency Scaling, and Adaptive Body Biasing are increasingly adopted hardware techniques to improve energy efficiency of multi-core servers by adjusting reconfigurable supply and body bias voltages. Existing algorithms cannot fulfill the potential of the techniques because random variations of workload and background traffic can lead to coupling of voltage configurations over time and hinder effective real-time reconfigurations. This paper proposes a new approach which enables multi-core servers to optimize in real-time their configurations under random traffic variations. The approach asymptotically minimizes the time-averaged energy consumption of cloud computing while maintaining platform stability in a fully decentralized fashion. Lyapunov optimization is employed to decouple and separately optimize the voltage configuration, inter- and intra-server offloading schedules among servers and over time. The voltage configuration which is non-convex is proved to increasingly exhibit convexity with growing workloads. The optimality loss from the non-convexity asymptotically diminishes. Simulations show our approach dramatically reduces the power if the cloud is lightly loaded, or converts the power to processing capacity otherwise. Embraced by theoretical breakthroughs, the approach can potentially revolutionize cloud computing.

A Comparative Study between Cloud Energy Consumption Measuring Simulators

A Comparative Study between Cloud Energy Consumption Measuring Simulators

A low-power task scheduling algorithm for heterogeneous cloud computing

As a new type of computing, cloud computing has led to a major computational change. Among many technologies in cloud computing, task scheduling has always been studied as a core issue by industry and academia. In the existing research, the main goal is completion time or load balancing. However, as the expansion of cluster size, energy consumption becomes a problem that must be faced. In this paper, the first of maximum loss scheduling algorithm is proposed. The algorithm is a low-power algorithm that can greatly reduce the energy consumption of cloud computing clusters through loss comparison rule. The effect of this method is more obvious as the cluster size and the number of tasks increase. Experimental simulation results show that the proposed method is significantly better than the Max–Min, Min–Min, Sufferage and E-HEFT algorithms. Compared to Min–Min, Max–Min, Sufferage and E-HEFT algorithms, average completion time of the algorithm reduces 16%, 12%, 8% and 14%, respectively. At the same time, the load balancing effect is also better than Min–Min and Sufferage algorithms.

An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased.

Hypercube Based Genetic Algorithm for Efficient VM Migration for Energy Reduction in Cloud Computing

If we choose to compare computing technology to coral reef then cloud computing technology is its very live and growing end. Its challenges are new and demand innovative measure to bring the size of its expending data centers under calipers and bridle its energy consumptions. Reduction in the consumption of energy is to be brought about without compromising quality-of-service and efficacy. For this, we purpose a Hypercube based Genetic Algorithm (HBGA) for efficient VM migration for energy reduction in cloud computing under QoS (Quality-of-service) constraint. The proposed HBGA technique can be implemented in two phases. First, in a data center the physical machines organize themselves in such a way as to acquire a highly scalable structure called Hypercube. The hypercube imperceptibly grates itself up or dips low in sympathy with VM instances as they mount up or get depleted. Secondly on the basis of this representation model of the compute nodes, and given the hypercube topology in which they are organized we propose three algorithms: (a)Hypercube based Node Selection Algorithm to minimize energy consumption (b) Hypercube based VM Selection Algorithm which minimizes the number of VM to be migrated. (c) To solve the problem of VM Placement we propose Hypercube based Genetic algorithm.Experimental results of comparisons between the proposed HBGA method viz-a-viz the existing solutions show a marked reduction in energy consumption of cloud computing environment.

Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds

The last decade witnessed a dramatic advance in cloud computing research and techniques. One of the key challenges in this field is reducing the massive amount of energy consumption in cloud computing data centers. Many power-aware virtual machine (VM) allocation and consolidation approaches were proposed to reduce energy consumption efficiently. However, most of the existing efficient cloud solutions save energy at the cost of significant performance degradation. In this paper, we propose a strategy to calculate the optimized working utilization levels for host computers. As the performance and power data need to be measured on real platforms, to make our design practical, we propose a strategy named PPRGear'' which is based on the sampling of utilization levels with distinct Performance-to-Power Ratios (PPR) calculated as the number of Server Side Java operations completed during a certain time period divided by the average active power consumption in that period. In addition, we present a framework for virtual machine allocation and migration which leverages the PPR for various host types. By achieving the optimal balance between host utilization and energy consumption, our framework is able to ensure that host computers run at the most power-efficient utilization levels, i.e., the levels with the highest PPR, thus tremendously reducing energy consumption with ignorable sacrifice of performance. Our extensive experiments with real world traces show that compared with three baseline energy-efficient VM allocation and selection algorithms, IqrMc, MadMmt, and ThrRs, our framework is able to reduce the energy consumption up to 69.31% for various host computer types with fewer migration times, shutdown times, and little performance degradation for cloud computing data centers. (C) 2019 Elsevier B.V. All rights reserved.