Energy Consumption In Cloud Computing Research Articles

A Review Study on Energy Consumption in Cloud Computing

Cloud computing has become a fundamental technology for a wide range of computing services, yet its increasing energy demands present substantial environmental and economic challenges. With the rapid growth of Cloud services and applications, increasing number of researches have been focused on energy saving. The need to reduce energy costs is a constant challenge of cloud providers and data centers. This paper offers an extensive review of the issues surrounding energy consumption in cloud computing, with a focus on algorithms associated with the situational awareness, consolidation, allocation, placement/migration, and scheduling of virtual machines and containers. We conduct a critical analysis of studies from 2018 to 2023, comparing various methodologies aimed at achieving energy efficiency without sacrificing performance. This review delineates current trends, identifies gaps in existing research, and proposes directions for future investigations. Our study emphasizes the necessity of cultivating sustainable practices in cloud computing and provides valuable insights into the practical implementation of energy-efficient solutions in cloud environments.

A Recursive Ant Colony Optimization Algorithm for Energy Consumption in Cloud Computing

The Cloud Computing (CC) has vast amount of data centers that consists of many computing nodes and consumes a huge amount of electrical energy. Hence the researchers found that the high service-level agreements (SLAs) violations and Energy Consumption (EC) are the major challenging issues in CC. The various traditional approaches reduced the EC, but ignored the SLA violation during the selection of Virtual Machine (VM) from overloaded hosts. In order to effectively deal with these issues, this paper proposed the Recursive Ant Colony Optimization (RACO) in 2 different workloads. The main aim of the RACO is to minimize the high EC and SLA violations by the movement of best ant, which is random and identified the distance between their movements. The algorithm consists of 3 major steps includes tracking and updating the pheromone and finally city selection. The proposed method simulated on Cloud Sim to validate the efficiency and stability of the proposed RACO and their performance compared to that of other existing techniques. The results showed that RACO reduced the EC by 40 - 42 % (approx.) which is less than the traditional ACO algorithm in Planet Lab data.
 HIGHLIGHTS
 
 Reduced EC and SLA Violation of Data Center are done via RACO
 VM Allocations are done by IQR and utilises micro, small, medium and extra-large instances
 Pheromone Tracking, updating and city selection are the endeavours of RACO
 RACO reduced EC by 40 - 42 % and SLA Violation

Energy-Aware Real-Time Tasks Processing for FPGA-Based Heterogeneous Cloud

Cloud computing is becoming a popular model of computing. Due to the increasing complexity of the cloud service request, it often exploits heterogeneous architecture. Moreover, some service requests (SRs)/tasks exhibit real-time features, which are required to be handled within a specified duration. Along with the stipulated temporal management, the strategy should also be energy efficient, as energy consumption in cloud computing is challenging. In this paper, we have proposed a strategy, called “Efficient Resource Allocation of Service Request” (ERASER) for energy efficient allocation and scheduling of periodic real-time SRs on cloud platform. The cloud platform is consists of Field Programmable Gate Arrays (FPGAs) as Processing Elements (PEs) along with the General Purpose Processors (GPP). We have further proposed, an SR migration technique to reduce the tasks rejection by serving maximum SRs. Simulation based experimental results demonstrate that the proposed methodology is capable to achieve upto 90 percent resource utilization with only 26 percent SR rejection rate over different experimental scenarios. Comparison results with other state-of-the-art techniques reveal that the proposed strategy outperforms the existing technique with 17 percent reduction in SR rejection rate and 21 percent reduction in energy consumption. Further, the simulation outcomes have been validated on real FPGA test-bed based on Xilinx Zynq SoC with standard benchmark tasks.

Mean field approximations to a queueing system with threshold-based workload control scheme

In this paper, motivated by considerations of server utilization and energy consumption in cloud computing, we investigate a homogeneous queueing system with a threshold-based workload control scheme. In this system, a virtual machine will be turned off when there are no tasks in its buffer upon the completion of a service by the machine, and turned on when the number of tasks in its buffer reaches a pre-set threshold value. Due to complexity of this system, we propose approximations to system performance measures by mean field limits. An iterative algorithm is suggested for the solution to the mean field limit equations. In addition, numerical and simulation results are presented to justify the proposed approximation method and to provide a numerical analysis on the impact of the system performances by system parameters, which suggests that the proposed system saves about % of the energy consumption compared to the ordinary supermarket models.

Distance Aware VM Allocation Process to Minimize Energy Consumption in Cloud Computing

Background: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. Aim and Objective: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. Methods: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Results and Conclusion: Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Quality and energy optimized scheduling technique for executing scientific workload in cloud computing environment

<p class="Abstract">Modern BigData data-intensive and scientific workload execution is challenging. The major issues are reliable processing, performance efficiency and energy efficacy perquisite of BigData processing framework. This work assume self-aware MC architectures that autonomously adjust or optimize their performance to accommodate users quality of service (QoS) performance requirement, job execution performance, energy efficiency, and resource accessibility. Extensive workload scheduling has been presented to minimize energy consumption in cloud computing (CC) environment. However, the existing workload scheduling model induces higher amount of interaction cost between inter-processors communications. Further, due to poor resource utilization, routing inefficiency these existing model induces higher energy cost and fails to meet workload QoS prerequisite. For overcoming research challenges, this paper presented quality and energy optimized scheduling (QEOS) technique for executing data-intensive workload by employing dynamic voltage and frequency scaling (DVFS) technique. Experiment outcome shows QEOS model attains good trade-off between system performance and energy consumption in multi-core cloud computing (CC) architectures when compared with existing model.</p>

Host Utilization Algorithm to Reduce Energy Consumption in Cloud Computing

The utilization of distributed computing server farm is developing quickly to fulfill the large increment required for systems administration, High-Performance Computing(HPC) as well as stockpiling assets for executing business and logical applications. The process of Virtual Machine (VM) solidification is inclusive of VMs getting relocated in order to make use of less physical servers. As a result, it enables the shut down or lowpower mode of more number of servers which enhances the vitality utilization effectiveness, working expense and CO2 discharge. An urgent advance in VM union is have over-burden discovery, which endeavors to foresee whether a physical server is going to be oversubscribed with VMs. On the contrary to usual studies which performed utilization of CPU being the standalone indicator for host overload, a multiple correlation host overload detection algorithm was proposed in the recent study by considering a lot of factors in this regard. A higher load balance model was introduced in this text for the general public cloud, supported by the concept of cloud partitioning, in addition to a switch mechanism used to strategize differently under different scenarios. The IP address is generally shared by a true server and carbo balance. In this regard, the load balancer considers the interface developed with IP address which accepts request packets and the packets are directed to the selected servers. With an aim to improve the efficiency in public cloud environment, the algorithm employed the sport theory in the load balancing strategy.

An Event Diminishment Model to Optimize Cloud Environment

Cloud computing is to compute a task assigned to a set of connections, software and services that can be utilized by the user over a network. The trending need of Cloud infrastructure has drastically scale up the energy need of data centers, which has become a critical issue. In the row also lead to high carbon emission which is not environment friendly so there is a need of energy efficient approach in cloud computing The research paper aims to reach a theoretical notion of sustainable development with proposing an incentive for reducing global warming through effective clustering techniques and methods. This paper aims to reduce cloud events by applying map reduce on large event clusters formed in cloud. The purpose of the paper is to develop a better methodology for handling the events of cloud computing and possibly clustering and reducing the similar types of events. This approach might lead to the reduction of carbon-dioxide gas (which is a greenhouse gas) by less usage of servers in cloud data centers. With the advent of IT services in cloud computing energy consumption it is necessary for the developing technology to progress towards sustainable development rather thrashing and harnessing energy from every possible means.

Energy Management Techniques for Cloud Based Environment

Cloud Computing has the facility to transform a large part of information technology into services in which computer resources are virtualized and made available as a utility service. From here comes the importance of scheduling virtual resources to get the maximum utilization of physical resources. The growth in server’s power consumption is increased continuously; and many researchers proposed, if this pattern repeats continuously, then the power consumption cost of a server over its lifespan would be higher than its hardware prices. The power consumption troubles more for clusters, grids, and clouds, which encompass numerous thousand heterogeneous servers. Continuous efforts have been done to reduce the electricity consumption of these massive-scale infrastructures. To identify the challenges and required future enhancements in the field of efficient energy consumption in Cloud Computing, it is necessary to synthesize and categorize the research and development done so far. In this paper, the authors prepare taxonomy of huge energy consumption problems and its related solutions. The authors cover all aspects of energy consumption by Cloud Datacenters and analyze many more research papers to find out the better solution for efficient energy consumption. Keywords: Cloud computing, Collocated virtual machines, Live migration, Load balancing, Resource scheduling

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.

VMDFS: virtual machine dynamic frequency scaling framework in cloud computing

Development of modern techniques, such as virtualization, underlies new solutions to the problem of reducing energy consumption in cloud computing. However, for the infrastructure as a service providers, it would be a difficult process to guarantee energy saving. Analysis of the workload of applications shows that the average utilization of virtual machines has many fluctuations; therefore, deciding about how to control such fluctuations in virtual machines plays a significant role in improving the energy consumption of datacenters. In this study, an adaptable model called virtual machine dynamic frequency system (VMDFS) has been developed whose its innovation is monitoring the average fluctuations of workloads to vary the CPU frequency of virtual machines at runtime, dynamically. In this model, enhanced exponential moving average method is used to predict workload fluctuations, and then after calculating a smoothing coefficient for the utilization fluctuations, the coefficient is used to control the CPU frequency (or computing power) of virtual machines. The proposed model was compared with several base line approaches such as DVFS using real datasets from CoMon project (PlanetLab). The results of experiments on VMDFS show that besides the reduced service-level agreement violation by up to 43.22%, the overall energy consumption is reduced by 40.16%. In addition, the overall runtime before a host shutdown increased by 17.44% in average, while the runtime before a virtual machine migration increased by 7.2%. This also shows an overall decrease in the number of migrations.

Energy Saving from Cloud Resources for a Sustainable Green Cloud Computing Environment

Cloud computing services are proliferation. The Cloud computing resources face major pitfall in energy consumes. The prime of energy consumption in cloud computing is by means of client computational devices, server computational devices, network computational devices and power required to cool the IT load. The cloud resources contribute high operational energy cost and emit more carbon emission to the environment. Therefore the cloud services providers need green cloud environment resolution to decrease the operational energy cost along with environmental impact. The most important objective of this effort is to trim down the energy from utilized and unutilized (idle) cloud resources and save the energy in cloud resources efficiently. To achieve the sustainable green cloud environment from an Energy Saving Algorithm used to choose the appropriate virtual services so that the power at the client, server, and network recourses can be reduced.

Energy management strategy in cloud computing: a perspective study

Optimizing energy consumption in cloud computing is yet a challenge despite the diversity of the proposed energy management strategies. Indeed, and during our related work study we have observed that the different elements or components which should be considered in order to be able to properly manage energy consumption in a cloud computing context are not well defined and/or discussed in terms of importance. This makes the proper classification and/or comparison of the different proposed strategies or techniques very difficult. Consequently, this paper aims, on the one hand, at defining and discussing properly such components in order to create a guideline and, on the other hand, to ease both the classification and the comparison of these proposed strategies and techniques. Second and after discussing some common weaknesses related to the current energy consumption optimization techniques and methods, this paper proposes energy-saving technique which uses a novel load detecting policy. This policy is based on the median absolute deviation method which uses the median and the standard deviation to calculate upper and lower thresholds which aim to classify hosts into either overloaded or under-loaded state. Simulation results have shown better results of the proposed technique compared to the existing ones especially in reducing energy consumption and the number of virtual machine migrations in addition to better active host time. Indeed, we found that the average of saved energy is around 40% compared to the built in techniques in cloudSim.

A SURVEY FOR ENERGY EFFICIENCY IN CLOUD DATA CENTERS

Services such as Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS) are provided by Cloud Computing. Subscription based computing resources and storage is offered in cloud. Cloud Computing is boosted by Virtualization technology. To move running applications or VMs starting with one physical machine then onto the next, while the customer is associated is named as Live VM migration. VM migration is empowered by means of Virtualization innovation to adjust stack in the server farms.
 Movement is done fundamentally to deal with the assets progressively. Server Consolidation’s main goal is to expel the issue of server sprawl. It tries to pack VMs from daintily stacked host on to fewer machines to satisfy assets needs. On other hand Load balancing helps in distributing workloads across multiple computing resources. Also in the presence of low loaded machines it avoids machines from getting overloaded and maintains efficiency. To balance the load across the systems in various cases, live migration technique is used with the application of various algorithms. The movement of virtual machines from completely stacked physical machines to low stacked physical machines is the instrument to adjust the entire framework stack. When we are worried about the energy consumption in Cloud Computing, VM consolidation & Server Consolidation comes into scenario in Virtual Machine movement method which itself implies that there is low energy consumption.

A novel cost based model for energy consumption in cloud computing.

Cloud data centers consume enormous amounts of electrical energy. To support green cloud computing, providers also need to minimize cloud infrastructure energy consumption while conducting the QoS. In this study, for cloud environments an energy consumption model is proposed for time-shared policy in virtualization layer. The cost and energy usage of time-shared policy were modeled in the CloudSim simulator based upon the results obtained from the real system and then proposed model was evaluated by different scenarios. In the proposed model, the cache interference costs were considered. These costs were based upon the size of data. The proposed model was implemented in the CloudSim simulator and the related simulation results indicate that the energy consumption may be considerable and that it can vary with different parameters such as the quantum parameter, data size, and the number of VMs on a host. Measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment. Also, measured results validate the model and demonstrate that there is a tradeoff between energy consumption and QoS in the cloud environment.

Efficiency Energy Consumption in Cloud Computing Based on Constant Position Selection Policy in Dynamic Virtual Machine Consolidation

Efficiency Energy Consumption in Cloud Computing Based on Constant Position Selection Policy in Dynamic Virtual Machine Consolidation

Using Virtualization Technique to Increase Security and Reduce Energy Consumption in Cloud Computing

An approach has been presented in this paper in order to generate a secure environment on internet Based Virtual Computing platform and also to reduce energy consumption in green cloud computing. The proposed approach constantly checks the accuracy of stored data by means of a central control service inside the network environment and also checks system security through isolating single virtual machines using a common virtual environment. This approach has been simulated on two types of Virtual Machine Manager (VMM) Quick EMUlator (Qemu), HVM (Hardware Virtual Machine) Xen and outputs of the simulation in VMInsight show that when service is getting singly used, the overhead of its performance will be increased. As a secure system, the proposed approach is able to recognize malicious behaviors and assure service security by means of operational integrity measurement. Moreover, the rate of system efficiency has been evaluated according to the amount of energy consumption on five applications (Defragmentation, Compression, Linux Boot Decompression and Kernel Boot). Therefore, this has been resulted that to secure multi-tenant environment, managers and supervisors should independently install a security monitoring system for each Virtual Machines (VMs) which will come up to have the management heavy workload of. While the proposed approach, can respond to all VM’s with just one virtual machine as a supervisor..

Resource Scheduling Optimization Algorithm of Energy Consumption for Cloud Computing Based on Task Tolerance

In order to solve that the energy was seriously wasted in cloud computing, an algorithm of energy consumption optimization for cloud computing based on the task tolerance (ECCT) was proposed. The concept of the task tolerance was put forward in the algorithm. The resource utilization of cloud computing node was maximized through increasing the task tolerance. At the same time, the parallelism degree of tasks were increased so that the task waiting time was reduced, which led to a reduction of energy consumption in cloud computing. Parameters were estimated and experiments on actual cloud computing sets were illustrated. The results showed that the total energy consumption and executive time were reduced more in the proposed algorithm than sequential executive algorithm (SEA). In addition, the advantage would be increasingly apparent with the increasing in assignments.

Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport

Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circumstances cloud computing can consume more energy than conventional computing where each user performs all computing on their own personal computer (PC).