Energy Efficient Cloud Computing Research Articles

Adaptive DRL-Based Task Scheduling for Energy-Efficient Cloud Computing

Intelligent task scheduling solutions are highly demanded in the operation of complex cloud data centers so that resources can be utilized in an energy-efficient way while still ensuring various requirements of users. However, the energy problem of task scheduling in cloud environment becomes more challenging with the ever-increasing number of users as well as the constant and unpredictable change of workloads. In this research, we propose an Adaptive Deep Reinforcement Learning-based (ADRL) task scheduling framework for energy-efficient cloud computing. We first present a Change Detection algorithm to detect whether the workload has changed greatly. On this basis, we built an Automatic Generation network to adjust the discount factor of Deep Reinforcement Learning (DRL) dynamically according to the changing workload, which enables faster and more accurate learning. We finally introduce the adaptive DRL to learn the optimal policy of dispatching arriving user requests with the reward aiming to minimize task response time and maximize resource utilization. Simulated experiments have confirmed that the proposed scheduling scheme performs well on accelerating learning convergence and promoting allocation accuracy, thus it is very effective in reducing the average response time of tasks and increasing the CPU utilization rate of resources, which eventually makes the cloud system more energy efficient.

Analysis on energy efficient green cloud computing

Cloud computing offers subscription based storage and resources. Cloud computing provides various services to cloud users. Increased use of information and communication technologies (ICT) has led to increase energy cost as well also increased emission of greenhouse gases like CO2. Since energy is an important asset, green cloud computing comes into picture. Green cloud computing can be achieved by applying various approaches, which uses lesser power as well as emits less CO2 gas, which is hazardous to environment. Energy efficient technologies results in decreased overall energy consumption. There is a need of data centre in almost every field like web applications and businesses which results in producing huge amount of CO2 in environment. To make data centres environmental friendly we need energy efficient approaches which will reduce energy consumption and there hazardous effects on environment. Data is being generated at very high speed so the need of data centres will also increase. Increased data centres will then consume more amount of energy. The main objective of these paper is to reviews various approaches applied by researchers to make energy efficient cloud computing.

Q-learning based dynamic task scheduling for energy-efficient cloud computing

High energy consumption has become a growing concern in the operation of complex cloud data centers due to the ever-expanding size of cloud computing facilities and the ever-increasing number of users. It is critical to find viable solutions to cloud task scheduling so that cloud resources can be utilized in an energy-efficient way while still meeting diverse user requirements in real time. In this research we propose a Q-learning based task scheduling framework for energy-efficient cloud computing (QEEC). QEEC has two phases. In the first phase a centralized task dispatcher is used to implement the M/M/S queueing model, by which the arriving user requests are assigned to each server in a cloud. In the second phase a Q-learning based scheduler on each server first prioritizes all the requests by task laxity and task life time, then uses a continuously-updating policy to assign tasks to virtual machines, applying incentives to reward the assignments that can minimize task response time and maximize each server’s CPU utilization. We have conducted simulation experiments, which have confirmed that implementing a M/M/S queueing system in a cloud can help to reduce the average task response time. The experiments have also demonstrated that the QEEC approach is the most energy-efficient as compared to other task scheduling policies, which can be largely credited to the M/M/S queueing model and the Q-learning strategy implemented in QEEC.

IEEE Access Special Section Editorial: Emerging Trends, Issues, and Challanges in Energy-Efficient Cloud Computing

Cloud computing is one of the most successful business models for providing a simple pay-as-you-go, and therefore is gaining much popularity in the industry. Customers and enterprises can maintain or scale-up a business easily while cutting down on their budget. However, energy consumption is one of the biggest problems in current cloud computing. It is both essential and urgent for governmental and industrial institutions to address this, to achieve rapid growth. The development of energy-efficient cloud computing has to be taken into consideration, which relies on the development of several key technologies: More energy-efficient mediums can be used in cloud computing at the platform level; energy-efficient scheduling algorithms, memory systems, storage systems, resource management policies, etc., can be adopted at the hypervisor level; energy-efficient scheduling, communications, and applications can be applied at the virtual machine level; and energy-efficient mobile cloud computing will be developed, involving green networking and wireless communications, cloud-based mobile applications, and limited resources management.

Energy-Efficient Cloud Computing

The summary on Energy-Efficient Cloud Computing delves into the vital exam of the environmental implications associated with cloud computing and proposes techniques to enhance the power efficiency of cloud records facilities. In this complete exploration, the focal point is on mitigating the carbon footprint of cloud technology, emphasizing the utilization of environmentally sustainable practices and revolutionary technology. The environmental effect of cloud computing has turn out to be a urgent subject as facts facilities contribute considerably to power intake and greenhouse gasoline emissions. This research scrutinizes the present day state of energy consumption in cloud information facilities and evaluates its environmental outcomes. The goal is to identify possible answers that not most effective mitigate the ecological footprint however also align with the developing call for for scalable and green cloud offerings. A pivotal issue of the proposed techniques includes the implementation of green records centers. These records facilities integrate eco-friendly technology, which include renewable strength sources and superior cooling structures, to decrease energy intake and decrease environmental effect. By exploring the feasibility and scalability of inexperienced records centers, the research ambitions to provide a sustainable version for future cloud infrastructure improvement. Optimization algorithms become a key era to enhance electricity performance in cloud computing. The look at investigates the application of algorithms designed to optimize resource usage, workload distribution, and normal device overall performance. Through the implementation of these algorithms, the research seeks to enhance the operational performance of cloud statistics centers, lowering power waste and promoting a extra sustainable computing infrastructure. Energy-conscious resource allocation is another essential detail addressed inside the abstract. The studies explores mechanisms to dynamically allocate assets primarily based on real-time power consumption information. By incorporating strength-cognizance into resource management, the goal is to strike a stability among overall performance requirements and electricity performance, ensuring most effective usage of assets whilst minimizing the environmental effect In conclusion, this abstract units the degree for a comprehensive exploration of Energy-Efficient Cloud Computing, losing mild at the environmental challenges posed with the aid of cloud statistics facilities. Through the investigation of green records centers, optimization algorithms, and power-aware resource allocation, the studies aspires to provide practical solutions that harmonize cloud computing with ecological sustainability, fostering a greater electricity-green and environmentally responsible cloud infrastructure.

Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques

Cloud computing is the most prominent computing paradigm in the present era of information technology. However, data centers needed for hosting cloud services demand huge amount of electrical energy and release harmful gases to the atmosphere. To ensure a sustainable future, there is a need to focus on energy efficiency in cloud computing. Early literature pertaining to energy consumption in cloud computing is primarily focused on individual sub-domains like scheduling techniques, optimization, and green computing metrics. Research literature on cloud resource optimization is found to be the most discussed but less structured. This paper intends to provide a complete picture of energy efficiency in cloud computing. It also classifies heuristics-based optimization methods and the dynamic power management techniques. The survey shows the research trends based on regions, journals, conferences, etc., in the domain of energy efficiency in cloud computing. The study concludes with research issues and future research directions.

Guest Editors’ Introduction: Special Issue on Green and Energy-Efficient Cloud Computing Part II

The papers in this special section focus on green and energy efficient cloud computing. Cloud computing has had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on-premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario as well, for example to manage load peaks. Cloud applications are hosted by data centers whose size ranges from tens to tens of thousands of servers, which raises significant challenges related to energy and cost management. It has been estimated that the Information and Communication Technology (ICT) industry alone is responsible for 2-3 percent of the global greenhouse gas emissions. Therefore, we must find innovative methods and tools to manage the energy efficiency and carbon footprint of data centers, so that they can operate and scale in a cost-effective and environmentally sustainable manner. These methods and tools are often categorized as Data Center Infrastructure Management (DCIM) to monitor, control, and optimize data centers with extensive automation. DCIM must also effectively manage the quality of service provided by the data center, since cloud customers require high reliability, availability, usability, and low response times.

Guest Editorial: Special Issue on Algorithms and Computational Models for Sustainable Computing in Cloud and Data Centers

The papers in this special section focus on the development of algorithms and computational models for sustainable computing in cloud and data centers. These papers bring together high quality contributions covering the topics of energy efficiency cloud computing, advances in sustainable cloud and data centers, energy aware applications at various levels of the computational and data storage processes, and emerging trends in sustainable computing. New research findings, novel approaches, and algorithms are presented for energy-efficient Cloud computing, energy-aware memory management solutions for data centers and processing of large data sets, energy aware scheduling approaches for data centers and processing of large data sets, energy consumption computational models, analytics and performance evaluations, and applications as well as emerging trends and applications.

Guest Editors’ Introduction: Special Issue on Green and Energy-Efficient Cloud Computing: Part I

The papers in this special section focus on green and energy efficient cloud computing. Cloud computing has had a huge commercial impact and has attracted the interest of the research community. Public clouds allow their customers to outsource the management of physical resources, and rent a variable amount of resources in accordance to their specific needs. Private clouds allow companies to manage on-premises resources, exploiting the capabilities offered by the cloud technologies, such as using virtualization to improve resource utilization and cloud software for resource management automation. Hybrid clouds, where private infrastructures are integrated and complemented by external resources, are becoming a common scenario as well, for example to manage load peaks. Cloud applications are hosted by data centers whose size ranges from tens to tens of thousands of servers, which raises significant challenges related to energy and cost management. It has been estimated that the Information and Communication Technology (ICT) industry alone is responsible for 2-3 percent of the global greenhouse gas emissions. Therefore, we must find innovative methods and tools to manage the energy efficiency and carbon footprint of data centers, so that they can operate and scale in a cost-effective and environmentally sustainable manner. These methods and tools are often categorized as Data Center Infrastructure Management (DCIM) to monitor, control, and optimize data centers with extensive automation. DCIM must also effectively manage the quality of service provided by the data center, since cloud customers require high reliability, availability, usability, and low response times.

RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

Energy Efficient Multi-Level Network Resources Management in Cloud Computing Data Centers

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment

Energy consumption has become a significant concern for cloud service providers due to financial as well as environmental factors. As a result, cloud service providers are seeking innovative ways that allow them to reduce the amount of energy that their data centers consume. They are calling for the development of new energy-efficient techniques that are suitable for their data centers. The services offered by the cloud computing paradigm have unique characteristics that distinguish them from traditional services, giving rise to new design challenges as well as opportunities when it comes to developing energy-aware resource allocation techniques for cloud computing data centers. In this article we highlight key resource allocation challenges, and present some potential solutions to reduce cloud data center energy consumption. Special focus is given to power management techniques that exploit the virtualization technology to save energy. Several experiments, based on real traces from a Google cluster, are also presented to support some of the claims we make in this article.

Novel Resource Allocation Algorithm for Energy-Efficient Cloud Computing in Heterogeneous Environment

Energy efficiency is one of the most important design considerations for a cloud data center. Recent approaches to the energy-efficient resource management for data centers usually model the problem as a bin packing problem with the goal of minimizing the number of physical machines (PMs) employed. However, minimizing the number of PMs may not necessarily minimize the energy consumption in a heterogeneous cloud environment. To address the problem, this paper models the resource allocation problem in a heterogeneous cloud data center as a constraint satisfaction problem (CSP). By solving this constraint satisfaction problem, an optimal resource allocation scheme, which includes a virtual machine provision algorithm and a virtual machine packing algorithm, is designed to minimize the energy consumption in a virtualized heterogeneous cloud data center. Performance studies show that this proposed new scheme outperforms the existing bin-packing based approaches in terms of energy consumption in heterogeneous cloud data centers.

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Cloud computing offers utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. In this paper, we define an architectural framework and principles for energy-efficient Cloud computing. Based on this architecture, we present our vision, open research challenges, and resource provisioning and allocation algorithms for energy-efficient management of Cloud computing environments. The proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS). In particular, in this paper we conduct a survey of research in energy-efficient computing and propose: (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering QoS expectations and power usage characteristics of the devices; and (c) a number of open research challenges, addressing which can bring substantial benefits to both resource providers and consumers. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant cost savings and demonstrates high potential for the improvement of energy efficiency under dynamic workload scenarios.