Green Cloud Data Centers Research Articles

Green Energy Forecast-Based Bi-Objective Scheduling of Tasks Across Distributed Clouds

Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based Multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.

Deep learning-based multivariate resource utilization prediction for hotspots and coldspots mitigation in green cloud data centers

Deep learning-based multivariate resource utilization prediction for hotspots and coldspots mitigation in green cloud data centers

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.

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.

Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers

The significant growth in the number and types of tasks of heterogeneous applications in green cloud data centers (GCDCs) dramatically increases their providers' revenue from users as well as energy consumption. It is a big challenge to maximize such revenue, while minimizing energy cost in a market where prices of electricity, availability of renewable power generation, and behind-the-meter renewable generation contract models differ among the geographical sites of the GCDCs. A multiobjective optimization method that investigates such spatial differences in the GCDCs is for the first time proposed to trade off such two objectives by cost-effectively executing all tasks while meeting their delay constraints. In each time slot, a constrained biobjective optimization problem is formulated and solved by an improved multiobjective evolutionary algorithm based on decomposition. Realistic data-based simulations prove that the proposed method achieves a larger total profit in faster convergence speed than the two state-of-the-art algorithms. Note to Practitioners-This article considers the tradeoff between profit maximization and energy cost minimization for the green cloud data center (GCDC) providers while meeting the delay constraints of all tasks. Current task-scheduling methods fail to take the advantage of spatial variations in many factors, e.g., prices of electricity and availability of renewable power generation at geographically distributed GCDC locations. As a result, they fail to execute all tasks of heterogeneous applications within their delay bounds in a high-revenue and low-energy-cost manner. In this article, a multiobjective optimization method that addresses the disadvantages of the existing methods is proposed. It is realized by a proposed intelligent optimization algorithm. Simulations demonstrate that in comparison with the two state-of-the-art scheduling algorithms, the proposed one increases the profit and reduces the convergence time. It can be readily implemented and integrated into actual industrial GCDCs.

Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions

Energy efficiency is one of the important issues in green cloud data centers (DCs). In this context, a virtual machine (VM) placement is one of the important techniques which can be used to achieve energy efficiency in such environments. Bio-inspired optimization algorithms are widely used in the literature to solve the VM placement (VMP) problem and different types of them are benefited to achieve energy efficiency while meeting Quality of Service (QoS) and user-specified constraints such as deadlines and cost. This paper presents a comprehensive survey and taxonomy of the bio-inspired VMP schemes. For this purpose, we first provide the essential concepts regarding the VMP and describe various objectives and factors which can be considered in this process. Then, we provide a taxonomy of VMP schemes regarding their applied optimization algorithms and compare their employed factors in the VMP process as well as simulator environments and the metrics which have been utilized in the verification of the investigated VMP frameworks. Finally, the concluding remarks and future researches directions are provided.

Thermal-benchmarking for cloud hosting green data centers

Thermal efficient usage of cloud hosting data center servers saves cooling energy and helps establish green cloud data centers. To achieve this goal, the data centers must be stress-tested to avail thermal data related to server utilization. The inherent limitations of cloud computing limit the control of cloud data center owner over the workload execution of cloud services except for the infrastructure and therefore thermal-aware workload modeling or thermal benchmarking of cloud infrastructure can fill this gap. Thermal-benchmarking techniques, through manipulation of server utilization, reveal the thermal profiles and thermal statistics of the servers that can be useful for thermal efficient data center management. This paper presents a generic approach to thermal-benchmarking and profiling of cloud hosting data center servers. We propose workload models to generate customizable thermal benchmarks for stress testing of data center servers. Additionally, we use workload traces from Alibaba cloud to generate thermal statistics to show that the proposed thermal benchmarking approach is applicable to any data center workload trace for any data center server.

Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds

With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky–Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods. Note to Practitioners —Workload prediction plays an important role in constructing scalable and green distributed cloud data centers. This paper presents a novel and fundamental methodology to achieve accuracy and fast learning for workload prediction. It develops an integrated prediction approach that combines the Savitzky–Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In order to establish a fine prediction model for the obtained information while achieving better prediction results and faster learning speed, this paper proposes an integrated method, SGW-S, to build a prediction model of a task time series and determine its optimal model parameters. The experimental results in the real-world data set show that the proposed method outperforms baseline methods in predicting the large-scale task time series. The proposed approach can aid the design and optimization of industrial cloud data centers and practitioners’ prediction of different types of task time series.

Spatial Task Scheduling for Cost Minimization in Distributed Green Cloud Data Centers

The infrastructure resources in distributed green cloud data centers (DGCDCs) are shared by multiple heterogeneous applications to provide flexible services to global users in a high-performance and low-cost way. It is highly challenging to minimize the total cost of a DGCDC provider in a market, where bandwidth prices of Internet service providers (ISPs), electricity prices, and the availability of renewable green energy all vary with geographical locations. Unlike existing studies, this paper proposes a spatial task scheduling and resource optimization (STSRO) method to minimize the total cost of their provider by cost-effectively scheduling all arriving tasks of heterogeneous applications to meet tasks’ delay-bound constraints. STSRO well exploits spatial diversity in DGCDCs. In each time slot, the cost minimization problem for DGCDCs is formulated as a constrained optimization one and solved by the proposed simulated annealing-based bat algorithm (SBA). Trace-driven experiments demonstrate that STSRO achieves lower total cost and higher throughput than two typical scheduling methods. Note to Practitioners —This paper investigates the cost minimization problem for DGCDCs while meeting delay-bound constraints for all arriving tasks. Previous task scheduling methods do not jointly investigate the spatial diversity in bandwidth prices of ISPs, electricity prices, and the availability of renewable green energy. Therefore, they fail to cost-effectively schedule all arriving tasks of heterogeneous applications during their delay-bound constraints. In this paper, a new method that overcomes the shortcomings of the existing methods is proposed. It is obtained by using the proposed SBA that solves a constrained optimization problem. Simulation results demonstrate that compared with two typical scheduling methods, it increases the throughput and decreases the cost. It can be readily implemented and integrated into real-world industrial DGCDCs. The future work needs to investigate the indeterminacy of renewable energy and the uncertainty in arriving tasks with rough deep neural network approaches on STSRO.

AI-Powered Green Cloud and Data Center

As the scale of cloud computing expands, its impact on energy and the environment is becoming more and more prominent. According to statistics, data centers’ energy consumption has accounted for 50% of operating costs of the data centers. The rising energy consumption not only needs energy in large quantity but also imposes heavy pressure on the environment. The high energy consumption of cloud data center has become an issue, people pay close attention to, in the information technology field. It is also a problem to be solved urgently. At present, high energy consumption is caused by two reasons. First, a resource scheduling mechanism with the priority of completion time causes low server use ratio, and it is a pervasive phenomenon that small tasks take high consumption. Second, the current refrigerating system of the data center is based on peak value strategy, which causes excessive cooling supply, increases operation cost, and leads to huge waste of energy. In this paper, considering the reason of high energy consumption of the data center, a new framework of green cloud data center is put forward. Using the techniques relevant to artificial intelligence, we put forward a scheduling control engine and an intelligent refrigerating engine aiming at reduction of energy consumption. In addition, we build a green cloud data center platform, realize the scheduling control engine, and verify the feasibility of the framework. It indicates that the framework can realize a cloud platform with low power consumption and a high-energy-efficient data center operation.

Improving Energy Efficiency of Virtual Resource Allocation In Cloud Datacenter

Objective: To minimize the energy frenzied around cloud datacenters. The proposed system provides a dynamic and continuous load prediction along with existing heuristic load prediction to improve the adaptation performance for green computing through Markov chaining of resources. Methodology: The trouble of energy competence through resource management for a large-scale cloud environment to facilitate hosts sites is resolute here. In apply there will be number of underutilized servers in the datacenter which guzzle bulky part of established energy. To minimize the energy frenzied around datacenters the existing system outlines a distributed middleware architecture and presenting one of its key elements, a gossip protocol, Skewness algorithm and load balancing that meets our design goals: justice of resource allocation with respect to hosted sites through overload escaping and efficient edition to load changes and scalability in terms of together the numeral of machines and sites thereby turning off the unused servers for green computing through Markov Chaining Model. This model is scalable enough to signify systems composed of thousands of resources and it makes potential to represent both physical and virtual wealth exploiting cloud explicit concepts such as the infrastructure elasticity. Results: To certify the model, simulation is conducted within the Network Simulator (NS) 2.28 have platform with GCC 4.3 and Fedora 13. The load prediction algorithm and Markov chain model achieves both overload avoidance and green computing for systems with multiresource limitations. Application: Green Wireless Network: To improve the energy efficient, data retrieval and resource service based on coaching, computing and networking in the area of Green Wireless. Green Big Data: To reduce the power consumption, big data merge the concept with green computing. Green job Scheduling: Each Server has different jobs, to save the energy server merged with green computing. Green Cloud data center: To use the green computing in cloud data center to increases the energy efficiency. Keywords: Green Computing, Load Prediction, Markov Chaining, Virtualization

TRS: Temporal Request Scheduling with bounded delay assurance in a green cloud data center

The growing deployment of Internet services in cloud data centers significantly increases the grid energy cost of cloud providers. Considering the environmental effect, many of current cloud providers migrate to green cloud data centers (GCDCs), and seek to reduce the usage of brown energy by partially (or entirely) adopting renewable energy sources. However, the temporal diversity in the grid price, wind speed and solar irradiance makes it a big challenge to minimize the grid energy cost of a GCDC while meeting the performance of each delay bounded request. This work proposes a Temporal Request Scheduling algorithm (TRS) that jointly considers the temporal diversity. TRS considers the long tail in real-life requests’ delay, and can provide strict delay assurance to all arriving requests by scheduling them to execute within their delay bound. Besides, this work explicitly provides mathematical modeling of the relation between the service rate in a GCDC and the refusal of delay bounded requests. Specifically, TRS solves a constrained nonlinear optimization problem by a hybrid meta-heuristic in each of its iterations. Compared with some existing scheduling methods, TRS can achieve higher throughput and lower grid energy cost for a GCDC while meeting each request’s delay requirement.

Green Cloud and Virtual Machines Migration Challenges

Cloud computing, currently, is one of the most efficient technologies over the IT and global application environments. This new environments has become one the major an effective working environment for small and medium scale enterprises. No one can deny the importance of the cloud computing, but replacing conventional data centers by cloud data centers need more attention. One of the important sides of cloud data centers is down time. To achieve this approach, data centers need to be active all the time and this approach needs more power. Thus, one of our concerns is about consuming more energy, because more energy needs more energy production. Therefore, traditional cloud data centers need huge electricity energy for running servers and cooling systems for example. Electricity production need carbon-based fuels, principally coal, oil, and natural gas cause the emission of the greenhouse gases like carbon dioxide (CO2). This emission may cause the global warming that has direct effects on humankind environment. To decrease greenhouse gases, it needed to consume less carbon-based fuels and green cloud data centers based on less consuming energy that known as high-efficient data centers. In green cloud we try to reduce the number of active devices and consume less electricity energy. The green data centers toke an advantage of the VMs ability of copying, deleting and moving across the data center. This action over servers may cause serious effect over all part of data centers in designing green data centers and in this paper bolds these challenges on this path.

I Q R based Approach for Energy Efficient Dynamic VM Consolidation for Green Cloud Data Centers

the advent of cloud computing in the arena of IT field energy consumption and service level agreement (SLA) violation emerge as a major problem, which reduces the profit of cloud service providers (CSP) and affect the cloud customers by fencing the reusability and scalability of the cloud data center services. This problem needs to be eradicate for the efficient resource provisioning in cloud data center. To satisfy the customer need virtual machine (VM) migration technique is required to balance the load of entire data center. Therefore we need to transfer the virtual machine of the overloaded host to the light weighted host using virtual machine migration technique. Due to frequent load balancing of cloud data center enormous amount of energy consumption takes place. This enhances the overall energy cost and degrades the performance of cloud data center. This paper proposes an Energy Efficient Dynamic VM Consolidation algorithm for reducing energy consumption. Keywordscomputing, Cloud service provider (CSP), Energy consumption, SLA violation, Load balancing, VM migration.

Green Cloud and reduction of energy consumption

By using global application environments, cloud computing based data centers growing every day and this exponentially grows definitely effect on our environment. Researchers that have a commitment to their environment and others which was concerned about the electricity bills came up with a solution which called “Green Cloud”. Green cloud data centers based on how consume energy are known as high efficient data centers. In green cloud we try to reduce number of active devices and consume less electricity energy. In green data centers toke an advantage of VM and ability of copying, deleting and moving VMs over the data center and reduce energy consumption. This paper focused on which parts of data centers may change and how researchers found the suitable solution for each component of data centers. Also with all these problems why still the cloud data centers are the best technology for IT businesses.

A Tree Regression-Based Approach for VM Power Metering

Cloud computing is developing so fast that more and more data centers have been built every year. This naturally leads to high-power consumption. Virtual machine (VM) consolidation is the most popular solution based on resource utilization. In fact, much more power can be saved if we know the power consumption of each VM. Therefore, it is significant to measure the power consumption of each VM for green cloud data centers. Since there is no device that can directly measure the power consumption of each VM, modeling methods have been proposed. However, current models are not accurate enough when multi-VMs are competing for resources on the same server. One of the main reasons is that the resource features for modeling are correlated with each other, such as CPU and cache. In this paper, we propose a tree regression-based method to accurately measure the power consumption of VMs on the same host. The merits of this method are that the tree structure will split the data set into partitions, and each is an easy-modeling subset. Experiments show that the average accuracy of our method is about 98% for different types of applications running in VMs.

Green Cloud: An Energy Efficient Load Balancing Approach Using Global Load Optimization

High Performance Green Cloud Computing is an emerging field in the upcoming IT industry. With the main focus on balancing resource utilization in green cloud environment, IT applications need a highly rapid and scalable access services. Cyber Guarder Approach (CGA) based on trust management in green cloud facilitates access control to resources pool but the energy expenditure is of higher rate while balancing the load. One of the important aspects of green cloud being optimization of energy utilization, Energy-aware Allocation Policies (EAP) maximized the revenues. However, EAP policies increased the running server, resulting in higher amount of frequency and imbalance in running server during resource utilization in green cloud. For balancing the load on the Infrastructure as a Service (IaaS), an energy efficient load balancing approach using global load optimization (GC-GLO) method is presented on the green cloud data center in this paper. The energy efficient load balancing approach uses the Global Load Optimization (GLO) method to allocate the resources in a dynamic manner using the load factor in two ways. First, GC-GLO approach uses the logical hierarchy process to allocate the resources in the servers present in green cloud zone. The logical hierarchy process in GC-GLO selects the task in a hierarchical manner and allocates the task between cloud severs’ based on the resource need. With the exact form of resource utilization in GC-GLO, improves the utilization rate in the green cloud environment. Based on the resource utilization, the GC-GLO approach then uses the global load optimization algorithm to measure the load rate of each green cloud server. The energy expenditure on the resource allocation and the IaaS maintenance on the green cloud are eventually measured. The approach, GC-GLO has been validated by conducting careful performance measure study using CloudSim platform in terms of factors such as energy expenditure rate on IaaS maintenance, resource utilization rate and running time. The results demonstrate that global load optimization has extensive potential as it offers significant performance gains to load balancing efficiency and false positive rate under dynamic workload scenarios.

A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

A Location Selection Policy of Live Virtual Machine Migration for Power Saving and Load Balancing

Green cloud data center has become a research hotspot of virtualized cloud computing architecture. And load balancing has also been one of the most important goals in cloud data centers. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on location selection (migration policy) of live VM migration for power saving and load balancing. We propose a novel approach MOGA-LS, which is a heuristic and self-adaptive multiobjective optimization algorithm based on the improved genetic algorithm (GA). This paper has presented the specific design and implementation of MOGA-LS such as the design of the genetic operators, fitness values, and elitism. We have introduced the Pareto dominance theory and the simulated annealing (SA) idea into MOGA-LS and have presented the specific process to get the final solution, and thus, the whole approach achieves a long-term efficient optimization for power saving and load balancing. The experimental results demonstrate that MOGA-LS evidently reduces the total incremental power consumption and better protects the performance of VM migration and achieves the balancing of system load compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.