Energy-efficient Cloud Research Articles

Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

Conceptualization and cases of study on cyber operations against the sustainability of the tactical edge

The last decade consolidated the cyberspace as fifth domain of military operations, which extends its preliminarily intelligence and information exchange purposes towards enabling complex offensive and defensive operations supported/supportively of parallel kinetic domain actuations. Although there is a plethora of well documented cases on strategic and operational interventions of cyber commands, the cyber tactical military edge is still a challenge, where cyber fires barely integrate to the traditional joint targeting cycle due to, among others, long planning/development times, asymmetric effects, strict target reachability requirements, or the fast propagation of collateral damage; the latter rapidly deriving on hybrid impacts (political, economic, social, etc.) and evidencing significant socio-technical gaps. In this context, it is expected that Tactical Clouds disruptively facilitate cyber operations at the edge while exposing the rest of the digital assets of the operation to them. On these grounds, the main purpose of the conducted research is to review and in depth analyze the risks and opportunities of jeopardizing the sustainability of the military Tactical Clouds at their cyber edge. Along with a 1) comprehensively formulation of the researched problematic, the study 2) formalizes the Tactical Denial of Sustainability (TDoS) concept; 3) introduces the phasing, potential attack surfaces, terrains and impact of TDoS attacks; 4) emphasizes the related human and socio-technical aspects; 5) analyzes the threats/opportunities inherent to their impact on the cloud energy efficiency; 6) reviews their implications at the military cyber thinking for tactical operations; 7) illustrates five extensive CONOPS that facilitate the understanding of the TDoS concept; and given the high novelty of the discussed topics, this paper 8) paves the way for further research and development actions.

Research on Energy Saving Distribution in Cloud Computing Environment

With the huge increase of cloud infrastructure and the continuous expansion of cloud computing scale, the consumption promotion role of IT resources has been increasing, which has greatly hindered the development of IT industry. Energy consumption has become an important factor restricting the development of cloud computing. How to reduce energy consumption and improve the efficiency of energy utilization in cloud computing has become a new problem. At present, most of the cloud computing task scheduling algorithms are also carried out in job scheduling, which does not consider the allocation and energy consumption of resources. Therefore, the rational allocation of resources is an effective way to improve the efficiency of energy utilization and energy utilization. In this paper, the concept of resource waiting and resource consumption is proposed. It is considered that resource allocation is an effective way to improve the efficiency of platform software. In this paper, the resource space time energy consumption of intelligent energy is defined. The optimal ratio of known energy consumption to 0 and the resource needed for resource tasks and tasks is called map / task allocation and resource allocation. In this paper, the best resources than W test and wait for the relationship between the specific energy and resource; then, to verify that the map / mountain ice task division rationality by experiment; then the experimental data to verify the R2 algorithm can reduce the energy consumption of resources, improve the utilization rate of node resources, optimizing the energy efficiency of Map/Reduce task. The proposed resource ratio model and resource W method can be applied to existing map reduction system, which is of theoretical significance and practical value for improving and optimizing energy efficiency in cloud computing models.

Energy-efficient cloud data center with fair service level agreement for green computing

Energy-efficient cloud data center with fair service level agreement for green computing

Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives. Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD. In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed. Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user. This study aims to achieve the desired balance by benefiting from the history of the user’s behaviour and from sharing resources – more specifically Virtual Machines (VM) – among a coalition of users. Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history. Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times. A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE). The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved. Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy. In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user. Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.

A multi-dimensional double descending maximum padding priority algorithm for cloud data centers

With the development of big data technologies, green cloud data centers have become a key factor in academia and industry. An energy-efficient cloud data center can save costs for cloud computing users. However, the problem of virtual machine mapping has always been a core problem. In the most existing research, the energy consumption generated by cloud data centers has become an important bottleneck restricting the technology of cloud computing. This paper establishes a consumption model of cloud data center energy and a virtual machine mapping rule. Based on this, a multi-dimensional double descending maximum padding priority (MD3MP2) virtual machine mapping algorithm is proposed. The algorithm can not only solve the one-dimensional virtual machine mapping problem of homogeneous data centers, but also successfully solve the multi-dimensional virtual machine mapping problem of homogeneous data centers. Finally, the algorithm is compared with four other algorithms. The experimental results show that the MD3MP2 algorithm is better than the compared algorithms.

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.

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

Hybrid Optimization Model for Energy Efficient Cloud Assisted Wireless Sensor Network

The role of wireless sensor networks is ubiquitous in the present era. The dependency of wireless sensor networks is inevitable for small scale to large scale applications due to its compact, reliable, and efficient processing capabilities. However, wireless sensor network has its few limitations. Since the network is created by deploying sensor nodes and it requires efficient energy management procedures. Localization of nodes is an important process that should be considered in wireless sensor networks which directly relates the energy management. To reduce the node localization issues in wireless sensor networks, this research work proposed a hybrid optimization model using Particle Swarm Optimization and Grey Wolf Optimization as a combined approach. The proposed model effectively handles the node localization issues. To reduce the data processing and storage issues in wireless sensor networks, Cloud module is incorporated in the proposed model which improves the energy management features. Similarly, to transfer the data from node to cloud, hybrid optimization model shortest path discovery process is utilized. This combined approach reduces the packet loss, avoids route failures, improves network reliability, and lifetime compared to conventional models such as ant colony optimization.

Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

AbstractOne of the foremost issues deliberated by the cloud infrastructure providers is to minimize the costs by reducing energy consumption. Though many heuristic algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and so on, have been existed for task scheduling or server consolidation to reduce energy consumption, more energy savings in data centers is required. In this paper, a combination of two distinctive heuristic algorithms, namely Particle Swarm Optimization (PSO) technique for proficient task scheduling and Ant Colony System (ACS) for effective server consolidation are proposed. In PSO, each particle in the swarm finds a task scheduling solution and updates the best solution. It is faster than many meta‐heuristic algorithms. In ACS, each ant in the ant colony tries to find the best destination PMs for migrating VMs from overloaded or underloaded PMs. The proposed PSO‐ACS algorithm is experimented with PlanetLab real workload traces available in the CloudSim toolkit. The algorithm provides reduction in energy consumption, number of VM migrations, and Energy SLA Violation (ESV) metrics while maintaining QoS. In the best‐case scenario, the proposed method reduces the ESV metric by 28%, 27%, and 18% in comparison with other algorithms like LR, ACS, and PSO‐LR.

Hybrid Heuristic Algorithm for Better Energy Optimization and Resource Utilization in Cloud Computing

Energy-efficient execution of the scientific workflow is a challenging task in cloud computing that demands high-performance computing to process growing datasets. Due to the interdependency of tasks in the scientific workflow applications, energy-efficient resource allocation is vital for large-scale applications running on heterogeneous physical machines. Thus, this paper proposes a hybrid heuristic algorithm based energy-efficient cloud computing service (HH-ECO) that offers a significant solution for resource allocation, task scheduling, and optimization of scientific workflows. To ensure the energy-efficient execution, the HH-ECO focuses on executing non-dominant workflow tasks through adaptive mutation and energy-aware migration strategy. HH-ECO adopts the chaotic based particle swarm optimization (C-PSO) principle to optimize the resource allocation, task scheduling, and resource migration by generating the global best plans without local convergence. C-PSO with adaptive mutation avoids the deterioration of global optima while finding the best host to place the virtual machine and ensures an appropriate resource allocation plan. By considering the workflow task precedence relationships during C-PSO based task scheduling, the novel hybrid heuristic method efficiently solves the multi-objective combinatorial optimization problem without dominance among the workflow tasks. The Cloudsim based simulation study delivers superior results compared to the existing methods such as the hybrid heuristic workflow scheduling algorithm (HHWS) and distributed dynamic VM management (DDVM). The proposed approach significantly improves the optimal makespan to 38.27% and energy conservation to 38.06% compared to the existing methods.

Energy Efficient Throughput Aware Traffic Load Balancing in Green Cellular Networks

With the augmentation of affordable multimedia wireless gadgets, the ubiquitous availability of the internet access, and the rapid pace of mobile traffic motivate research towards fifth generation (5G) communications to realize energy-efficient cloud radio access networks (C-RAN) with guaranteed quality of experience. Exploiting green energy harvesting for powering the C-RAN substantially alleviates the energy procurement from the utility grid, carbon footprint, and operational expenses. In this paper, we propose a new dynamic point selection coordinated multipoint (DPS CoMP) based load balancing paradigm emphasizing achievable throughput and energy efficiency (EE) by reducing utility grid consumption from a network level perspective. This paper investigates the radio efficiency, EE, and average on-grid energy saving addressing the key challenges of tempo-spatial dynamics of traffic intensity and renewable energy (RE) generation under a wide range of network setup. Endeavoring load balancing technique strives a balance in network utilities such as green energy utilization and user association based on BS coordination technique in a cluster. Provision of cell sleep approach is contemplated for further energy saving by turning off lightly loaded base stations (BSs) during low traffic arrivals. The proposed CoMP based load balancing algorithm proficiently manages resource block allocation to the new users and elevated the energy efficiency over the conventional location and traffic centric mechanisms. Extensive system-level simulations manifest that the suggested framework enables an adjustable trade-off between radio efficiency and EE, and saves 22% on-grid power consumption and increases EE index by 32%. Afterward, an exhaustive comparison of the proposed method with the existing schemes is pledged for further validation highlighting sustainable 5G wireless systems.

Multiobjective Task Scheduling for Energy-Efficient Cloud Implementation of Hyperspectral Image Classification

Cloud computing has become a promising solution to efficient processing of remotely sensed big data, due to its high-performance and scalable computing capabilities. However, existing cloud solutions generally involve the problems of low resource utilization and high energy consumption when processing large-scale remote sensing datasets, affecting the quality-of-service of the cloud system. Aiming at hyperspectral image classification applications, this article proposes an energy-efficient cloud implementation by employing a multiobjective task scheduling algorithm. We first present a parallel computing mechanism for a fusion-based classification method based on Apache Spark. With the general classification flow represented by a workflow model, we formulate a multiobjective scheduling framework that jointly minimizes the total execution time as well as energy consumption. We further develop an effective scheduling algorithm to solve the multiobjective optimization problem and produce a set of Pareto-optimal solutions, providing the tradeoff between computational efficiency and energy efficiency. Experimental results demonstrate that the multiobjective scheduling approach proposed in this work can substantially reduce the execution time and energy consumption for performing large-scale hyperspectral image classification on Spark. In addition, our proposed algorithm can generate better tradeoff solutions to the multiobjective scheduling problem as compared to competing scheduling algorithms.

Green Cloud Software Engineering for Big Data Processing

Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO2) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

Cloud Computing and Energy Efficiency: Mapping the Thematic Structure of Research

The dynamic growth in the use of cloud computing systems results in increasing energy consumption. Consequently, more and more attention is given to energy efficiency issues both in research and theory development as well as the business practice of cloud computing systems. In spite of the rapid development of research, the field has not been mapped from the bibliometric perspective yet. This study aims at publication profiling and mapping the thematic structure of the cloud computing energy efficiency research field. Detailed research objectives include: (1) profiling scientific publications in the field, (2) identifying and exploring thematic research areas, (3) identifying emerging topics and discussing their potential as future research lines. The aforementioned objectives are translated into the following study questions: (1) What are the most productive nations, institutions, source titles, and scholars contributing to research on energy efficiency in cloud computing? (2) What does the thematic structure of the research field look like? (3) What are the “hot” research topics attracting scholars’ attention? The research methodology toolbox includes a combination of bibliometric descriptive studies (research profiling), science mapping (keyword co-occurrence analysis), and literature reviews (systematic literature review). Bibliometric data for analysis were elicited from the Scopus database. The VOSviewer software supported bibliometric analysis and data visualization.

Energy efficiency in cloud computing based on mixture power spectral density prediction

Due to the budget and environmental issues, adaptive energy efficiency receives a lot of attention these days, especially for cloud computing. In the previous research, we developed a combined methodology based on nonparametric prediction and convex optimization to produce proactive energy efficiency-oriented solution. In this work, the predictive analysis was further enhanced by deriving the mixture power spectral density to model the complex cloud monitoring statistics. By engaging the improved technique to the predictive analysis, the prediction process was more adaptive to handle the fluctuation in system utilization. As a consequence, the optimization process could subsequently produce more appropriate setting for energy savings. After the infrastructure setting has been made available, the instruction of virtual machine migration was created and implemented by the cloud orchestrator. This instruction condensed the services into the pool of active facilities, satisfying the objective of power efficiency. Eventually, any physical machine out of the power configuration would be gradually terminated. Compared to our former method, the effectiveness of the proposed technique has been proven by cutting down 4.92% of energy consumption, while still maintaining a similar quality of services.

RETRACTED ARTICLE: A novel scheduling approach to improve the energy efficiency in cloud computing data centers

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

Методы достижения энергоэффективности в облачных вычислениях

The article considers the problem of cloud computing - the environment of data storage and processing that provides access to resources shared among multiple users. Cloud computing is a model for organizing remote access on request for a shared set of configurable computing resources that can be quickly allocated and freed with minimal management or interaction costs with the service provider. Using clouds significantly reduces the costs of large industrial companies and enterprises. Cloud technology helps scale your business quickly and with minimal cost, they can improve productivity together with simplifying many business processes. Cloud computing is vastly expanding opportunities, so that all large companies today are actively switching to cloud services. But this causes significant problems with energy consumption. The energy consumption by cloud computing remains a serious problem, as data processing centers grow in size. There has been proposed the approach to choosing an energy-efficient cloud architecture that aims to reduce the energy consumption of cloud applications in all deployment models, the architectures of cloud infrastructures being given. The architecture supports energy efficiency in building, deploying and operating services.

Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything

The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The “thing” in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such “thing”s generates data, there is a chance of huge data traffic in the internet. This could be handled only by integrating “Internet of Everything (IoE)” paradigm with a completely diversified technology — Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy Efficient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data traffic in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased life-time and reduction in traffic.

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Given the perpetual surging of cloud services’ requests, energy consumption of cloud data centers with their related CO2 emissions still represents major issues. Efficient use of cloud’s resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud’s energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud’s resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra- and inter-cloud network’s resources to be considered. These resources are mainly provisioned through two well-known technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.