Energy and resource efficient workflow scheduling in a virtualized cloud environment

High energy consumption (EC) is one of the leading and interesting issue in the cloud environment. The optimization of EC is generally related to scheduling problem. Optimum scheduling strategy is used to select the resources or tasks in such a way that system performance is not violated while minimizing EC and maximizing resource utilization (RU). This paper presents a task scheduling model for scheduling the tasks on virtual machines (VMs). The objective of the proposed model is to minimize EC, maximize RU, and minimize workflow makespan while preserving the task’s deadline and dependency constraints. An energy and resource efficient workflow scheduling algorithm (ERES) is proposed to schedule the workflow tasks to the VMs and dynamically deploy/un-deploy the VMs based on the workflow task’s requirements. An energy model is presented to compute the EC of the servers. Double threshold policy is used to perceive the server’ status i.e. overloaded/underloaded or normal. To balance the workload on the overloaded/underloaded servers, live VM migration strategy is used. To check the effectiveness of the proposed algorithm, exhaustive simulation experiments are conducted. The proposed algorithm is compared with power efficient scheduling and VM consolidation (PESVMC) algorithm on the accounts of RU, energy efficiency and task makespan. Further, the results are also verified in the real cloud environment. The results demonstrate the effectiveness of the proposed ERES algorithm.

     

A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment

The field of live VM (virtual machine) migration has been a hotspot problem in green cloud computing. Live VM migration problem is divided into two research aspects: live VM migration mechanism and live VM migration policy. In the meanwhile, with the development of energy-aware computing, we have focused on the VM placement selection of live migration, namely live VM migration policy for energy saving. In this paper, a novel heuristic approach PS-ES is presented. Its main idea includes two parts. One is that it combines the PSO (particle swarm optimization) idea with the SA (simulated annealing) idea to achieve an improved PSO-based approach with the better global search''s ability. The other one is that it uses the Probability Theory and Mathematical Statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization for energy saving as it has considered not only the optimization of the current problem scenario but also that of the future problem. The experimental results demonstrate that PS-ES evidently reduces the total incremental energy consumption and better protects the performance of VM running and migrating compared with randomly migrating and optimally migrating. As a result, the proposed PS-ES approach has capabilities to make the result of live VM migration events more high-effective and valuable.     

Performance and energy modeling for live migration of virtual machines

Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy consumption. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%.     

Energy efficient job scheduling with workload prediction on cloud data center

Data centers are the backbone of cloud infrastructure platform to support large-scale data processing and storage. More and more business-to-consumer and enterprise applications are based on cloud data center. However, the amount of data center energy consumption is inevitably lead to high operation costs. The aim of this paper is to comprehensive reduce energy consumption of cloud data center servers, network, and cooling systems. We first build an energy efficient cloud data center system including its architecture, job and power consumption model. Then, we combine the linear regression and wavelet neural network techniques into a prediction method, which we call MLWNN, to forecast the cloud data center short-term workload. Third, we propose a heuristic energy efficient job scheduling with workload prediction solution, which is divided into resource management strategy and online energy efficient job scheduling algorithm. Our extensive simulation performance evaluation results clearly demonstrate that our proposed solution has good performance and is very suitable for low workload cloud data center.     

Metapopulation structure and habitat quality in modelling dispersal in the butterfly Iolana iolas

Quantifying dispersal is fundamental to understanding the effects of fragmentation on populations. Although it has been shown that patch and matrix quality can affect dispersal patterns, standard metapopulation models are usually based on the two basic variables, patch area and connectivity. In 2004 we studied migration patterns among 18 habitat patches in central Spain for the butterfly Iolana iolas, using mark–release–recapture methods. We applied the virtual migration (VM) model and estimated the parameters of emigration, immigration and mortality separately for males and females. During parameter estimation and model simulations, we used original and modified patch areas accounting for habitat quality with three different indices. Two indices were based on adult and larval resources (flowers and fruits) and the other one on butterfly density. Based on unmodified areas, our results showed that both sexes were markedly different in their movements and mortality rates. Females emigrated more frequently from patches, but males that emigrated were estimated to move longer daily dispersal distances and suffer higher mortality than females during migration. Males were more likely to emigrate from small than from large patches, but patch area had no significant effect on female emigration. The effects of area on immigration rate and the within-patch mortality were similar in both sexes. Based on modified areas, the estimated parameter values and the model simulation results were similar to those estimated using the unmodified patch areas. One possible reason for the failure to significantly improve the parameter estimates of the VM model is the fact that resource quantity and butterfly population sizes were strongly correlated with patch area. Our results suggest that the standard VM modelling approach, based on patch area and connectivity, can provide a realistic picture of the movement patterns of I. iolas .     

DENS: data center energy-efficient network-aware scheduling

In modern data centers, energy consumption accounts for a considerably large slice of operational expenses. The existing work in data center energy optimization is focusing only on job distribution between computing servers based on workload or thermal profiles. This paper underlines the role of communication fabric in data center energy consumption and presents a scheduling approach that combines energy efficiency and network awareness, named DENS. The DENS methodology balances the energy consumption of a data center, individual job performance, and traffic demands. The proposed approach optimizes the tradeoff between job consolidation (to minimize the amount of computing servers) and distribution of traffic patterns (to avoid hotspots in the data center network).     

农户薪柴消费的理论模型及实证研究

农户薪柴消费变化对室内空气质量、农村生态环境建设影响重大,论文构建了山区农户薪柴消费的理论模型,以重庆市典型区1015份农户调查数据为例,运用Tobit模型分析了农户人均薪柴消费量的影响因素。研究表明:农户家庭能源消费中,商品能源和新能源的比重逐步增大,但薪柴依旧是农户普遍使用的能源类型,且消费量占总能源消费量的比重高;通过降低农业劳动力比重和提高非农工资水平两种途径,非农就业能显著降低农户人均薪柴消费量;家庭电器数量增多、其他收入(政府补贴、亲戚帮扶等)增加、及替代能源(液化气、沼气、煤炭等)可获得性增强也能显著降低农户人均薪柴消费量,而户主年龄大、人均牲畜数量多及房屋离集市距离远的农户家庭的人均薪柴消费量高。提出了加快薪柴替代的措施。     

Bridge life cycle assessment with data uncertainty

Purpose

Although life cycle assessment (LCA) has been employed to analyze the environmental impacts of bridges, the uncertainties associated to LCA have not been studied, which have a profound effect on the LCA results. This paper is intended to provide a comprehensive environmental impact assessment of bridge with data uncertainty, by assigning probability distributions on the considered parameters, assessing the variability in the acquisition of inventory and identifying the key parameters with significant environmental impacts.

Methods

A life cycle assessment of a bridge in Shanxi Province of China was conducted in a cradle-to-grave manner, by considering the source of the uncertainty of LCA. A statistical method was applied to quantify the uncertainty of measured inventory data and to calculate the probability distribution of the data. The uncertainty propagation was conducted through using a Monte Carlo simulation. Finally, the factor which is of vital importance to the assessment result was identified by sensitivity analysis.

Results and discussion

For the case of bridge, normal distribution can be adopted to fit environmental substances and environmental impact in steel production. The distributions of the weighted value of human health damage, ecological system damage, and resource and energy consumption can be represented by an approximate similar normal distribution function. The coefficient of variance (COV) of each weighted value is about 40, 30 to 40, and about 6 %, respectively. The COV for the total environmental impact is about 10 % in all stages of the bridge’s life cycle, except the operation stage, which is up to 22.67 %. By conducting sensitivity analysis, PM₁₀, NO_x, and oil consumption was found to have a great influence on the result of human health damage, ecological system damage, and resource and energy consumption, respectively.

Conclusions

The COV for the total environmental impact is 22.67 % in the bridge’s operation stage; it is important to establish a reasonable maintenance strategy to decrease the uncertainty of the bridge’s environmental impact. The COVs of the weighted value for human health damage and resource and energy consumption have a quite modest difference among the four stages of the bridge’s life cycle. However, The COV of the weighted value for ecological system damage shows large difference among the four stages of the bridge’s life cycle; construction stage has the greatest uncertainty. In addition, different values of PM₁₀, NO_x, and oil consumption have a profound influence on the result of human health damage, ecological system damage, and resource and energy consumption, respectively.

     

From high-availability to collapse: quantitative analysis of “Cloud-Droplet-Freezing” attack threats to virtual machine migration in cloud computing

Virtual machines (VM) migration can improve availability, manageability, performance and fault tolerance of systems. Current migration researches mainly focus on the promotion of the efficiency by using shared storage, priority-based policy etc.. But the effect of migration is not well concerned. In fact, once physical servers are overloaded from denial-of-service attack (DDoS) attack, a hasty migration operation not only unable to alleviate the harm of the attack, but also increases the harmfulness. In this paper, a novel DDoS attack, Cloud-Droplet-Freezing (CDF) attack, is described according to the characteristics of cloud computing cluster. Our experiments show that such attack is able to congest internal network communication of cloud server cluster, whilst consume resources of physical server. Base on the analysis of CDF attack, we highlight the method of evaluating potential threats hidden behind the normal VM migration and analyze the flaws of existing intrusion detection systems/prevention system for defensing the CDF attack.     

Model uncertainty analysis using data analytics for life-cycle assessment (LCA) applications

Purpose

Objective uncertainty quantification (UQ) of a product life-cycle assessment (LCA) is a critical step for decision-making. Environmental impacts can be measured directly or by using models. Underlying mathematical functions describe a model that approximate the environmental impacts during various LCA stages. In this study, three possible uncertainty sources of a mathematical model, i.e., input variability, model parameter (differentiate from input in this study), and model-form uncertainties, were investigated. A simple and easy to implement method is proposed to quantify each source.

Methods

Various data analytics methods were used to conduct a thorough model uncertainty analysis; (1) Interval analysis was used for input uncertainty quantification. A direct sampling using Monte Carlo (MC) simulation was used for interval analysis, and results were compared to that of indirect nonlinear optimization as an alternative approach. A machine learning surrogate model was developed to perform direct MC sampling as well as indirect nonlinear optimization. (2) A Bayesian inference was adopted to quantify parameter uncertainty. (3) A recently introduced model correction method based on orthogonal polynomial basis functions was used to evaluate the model-form uncertainty. The methods are applied to a pavement LCA to propagate uncertainties throughout an energy and global warming potential (GWP) estimation model; a case of a pavement section in Chicago metropolitan area was used.

Results and discussion

Results indicate that each uncertainty source contributes to the overall energy and GWP output of the LCA. Input uncertainty was shown to have significant impact on overall GWP output; for the example case study, GWP interval was around 50%. Parameter uncertainty results showed that an assumption of ±?10% uniform variation in the model parameter priors resulted in 28% variation in the GWP output. Model-form uncertainty had the lowest impact (less than 10% variation in the GWP). This is because the original energy model is relatively accurate in estimating the energy. However, sensitivity of the model-form uncertainty showed that even up to 180% variation in the results can be achieved due to lower original model accuracies.

Conclusions

Investigating each uncertainty source of the model indicated the importance of the accurate characterization, propagation, and quantification of uncertainty. The outcome of this study proposed independent and relatively easy to implement methods that provide robust grounds for objective model uncertainty analysis for LCA applications. Assumptions on inputs, parameter distributions, and model form need to be justified. Input uncertainty plays a key role in overall pavement LCA output. The proposed model correction method as well as interval analysis were relatively easy to implement. Research is still needed to develop a more generic and simplified MCMC simulation procedure that is fast to implement.

     

A tractable revenue management model for capacity allocation and overbooking over an airline network

In this paper, we develop a revenue management model to jointly make the capacity allocation and overbooking decisions over an airline network. The crucial observation behind our model is that if the penalty cost of denying boarding to the reservations were given by a separable function, then the optimality equation for the joint capacity allocation and overbooking problem would decompose by the itineraries. We exploit this observation by building an approximation to the penalty cost that is separable by the numbers of reservations for different itineraries. In this case, we can obtain an approximate solution to the optimality equation by plugging the separable approximation into the boundary condition of the optimality equation. Our computational experiments compare our approach with a standard deterministic linear programming formulation, as well as a recent joint capacity allocation and overbooking model. When compared with the standard deterministic linear programming formulation, our approach can provide significant profit improvements. On the other hand, when compared with the recent joint capacity allocation and overbooking model, our approach can provide similar profit performance with substantially shorter runtimes.     

Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.     

Domestic Water Use in the United States: A Life-Cycle Approach

Water and energy are two primary natural resources used by building occupants. A life-cycle assessment (LCA) is performed for water-consuming plumbing fixtures and water-consuming appliances during their operational life for four different building types. Within the cycle studied, water is extracted from the natural environment, subjected to water treatment, pumped to buildings for use, collected for wastewater treatment, and discharged back to the natural environment. Specifically, the impacts of water use, electricity and natural gas generation, energy consumption (for water and wastewater treatment, and for water heating), and the manufacture of water and wastewater treatment chemicals are evaluated both quantitatively and qualitatively on a generalized national level in the United States of America.
It is concluded that water use and consumption within buildings have a much larger impact on resource consumption than the water and wastewater treatment stages of the life cycle. To study this more specifically, the resource consumption of four different building types-an apartment building, a college dormitory, a motel, and an office building-is considered. Of these four building types, the apartment has the highest energy consumption (for water and wastewater treatment, and for water heating) per volume of water used, whereas the office building has the lowest. Similarly, the calculated LCA score for the apartment building is typically greater than those of the other three building types.     

Using priced timed automaton to analyse the energy consumption in cloud computing environment

According to the fact that cloud servers have different energy consumption on different running states, as well as the energy waste problem caused by the mismatching between cloud servers and cloud tasks, we carry out researches on the energy optimal method achieved by a priced timed automaton for the cloud computing center in this paper. The priced timed automaton is used to model the running behaviors of the cloud computing system. After introducing the matching matrix of cloud tasks and cloud resources as well as the power matrix of the running states of cloud servers, we design a generation algorithm for the cloud system automaton based on the generation rules and reduction rules given ahead. Then, we propose another algorithm to settle the minimum path energy consumption problem in the cloud system automaton, therefore obtaining an energy optimal solution and an energy optimal value for the cloud system. A case study and repeated experimental analyses manifest that our method is effective and feasible.     

Mvmotion: a metadata based virtual machine migration in cloud

As one of the most important features of virtualization, virtual machine (VM) migration provides great benefits for load balancing, resources-saving, fault tolerance in modern cloud data centers. Considering the network traffic caused by transferring data during VM migration imposes a huge pressure on network bandwidth of cloud data centers, and by analyzing the characteristic of the transferred data, we found that the redundant data, which is produced between two physical hosts by hosting virtual machines cloned from same VM template, can be reduced to relieve the network traffic pressure. This paper presents a Metadata based VM migration approach (Mvmotion) to reduce the amount of transferred data during migration by utilizing memory de-redundant technique between two physical hosts. Mvmotion utilizes the hash based fingerprints to generate Metadata of memory, which is used to identify redundant memory of VMs between two hosts. Based on the Metadata, the transfer of redundant memory data during migration can be eliminated. Experiment demonstrates that, compare to Xen’s default migration approach, Mvmotion can reduce the total transferred data by 29–97 %, and decreases the migration time by 16–53 %.     

Downscaling Aggregate Urban Metabolism Accounts to Local Districts

Urban metabolism accounts of total annual energy, water, and other resource flows are increasingly available for a variety of world cities. For local decision makers, however, it may be important to understand the variations of resource consumption within the city. Given the difficulty of gathering suburban resource consumption data for many cities, this article investigates the potential of statistical downscaling methods to estimate local resource consumption using socioeconomic or other data sources. We evaluate six classes of downscaling methods: ratio‐based normalization; linear regression (both internally and externally calibrated); linear regression with spatial autocorrelation; multilevel linear regression; and a basic Bayesian analysis. The methods were applied to domestic energy consumption in London, UK, and our results show that it is possible to downscale aggregate resource consumption to smaller geographies with an average absolute prediction error of around 20%; however, performance varies widely by method, geography size, and fuel type. We also show how mapping these results can quickly identify districts with noteworthy resource consumption profiles. Further work should explore the design of local data collection strategies to enhance these methods and apply the techniques to other urban resources such as water or waste.     

Incorporating climate change into ecosystem service assessments and decisions: a review

Climate change is having a significant impact on ecosystem services and is likely to become increasingly important as this phenomenon intensifies. Future impacts can be difficult to assess as they often involve long timescales, dynamic systems with high uncertainties, and are typically confounded by other drivers of change. Despite a growing literature on climate change impacts on ecosystem services, no quantitative syntheses exist. Hence, we lack an overarching understanding of the impacts of climate change, how they are being assessed, and the extent to which other drivers, uncertainties, and decision making are incorporated. To address this, we systematically reviewed the peer‐reviewed literature that assesses climate change impacts on ecosystem services at subglobal scales. We found that the impact of climate change on most types of services was predominantly negative (59% negative, 24% mixed, 4% neutral, 13% positive), but varied across services, drivers, and assessment methods. Although uncertainty was usually incorporated, there were substantial gaps in the sources of uncertainty included, along with the methods used to incorporate them. We found that relatively few studies integrated decision making, and even fewer studies aimed to identify solutions that were robust to uncertainty. For management or policy to ensure the delivery of ecosystem services, integrated approaches that incorporate multiple drivers of change and account for multiple sources of uncertainty are needed. This is undoubtedly a challenging task, but ignoring these complexities can result in misleading assessments of the impacts of climate change, suboptimal management outcomes, and the inefficient allocation of resources for climate adaptation.     

Modelling the influence of copepod behaviour on faecal pellet export at high latitudes

The contribution of faecal pellet (FP) production by zooplankton to the downward flux of particulate organic carbon (POC) can vary from <1 % to more than 90 % of total POC. This results from varying degrees of interception and consumption, and hence recycling, of FPs by zooplankton in the upper mixed layers, and the active transport of FP to depth via diel vertical migration (VM) of zooplankton. During mid-summer at high latitudes, synchronised diel VM ceases, but individual zooplankton may continue to make forays into and out of the surface layers. This study considers the relative importance of different VM behaviours on FP export at high latitudes. We focussed on copepods and parameterised an individual-based model using empirical measures of phytoplankton vertical distribution and the rate of FP production, as a function of food availability. FP production was estimated under three different behaviours common to high-latitude environments (1) no VM, (2) foray-type behaviour and (3) synchronised diel VM. Simulations were also made of how each of these behaviours would be observed by an acoustic Doppler current profiler (ADCP). The model found that the type of copepod behaviour made a substantial difference to the level of FP export to depth. In the absence of VM, all FPs were produced above 50 m, where the probability of eventual export to depth was low. In foray-type scenarios, FP production occurred between 0 and 80 m, although the majority occurred between 30 and 70 m depth. Greatest FP production in the deeper layers (>70 m) occurred when diel VM took place. Simulated ADCP vertical velocity fields from the foray-type scenario resembled field observations, particularly with regard to the occurrence of positive anomalies in deeper waters and negative anomalies in shallower waters. The model illustrates that active vertical flux of zooplankton FP can occur at high latitudes even when no synchronised VM is taking place.     

Migration cost and profit oriented cloud federation formation: hedonic coalition game based approach

Cloud federation has paved the way for cloud service providers (CSP) to collaborate with other CSPs to serve users’ resource requests, which are prohibitively high for any single CSP during peak time. Moreover, to entice different CSPs to participate in federation, it is necessary to maximize the profit of all CSPs involved in the federation. Further, federation enables overloaded CSPs to distribute their load among other underloaded member CSPs of federation by migrating the virtual machines (VM). Migration of VM among member CSPs of federation, also enables to increase the reliability and availability of cloud services on occurrence of faults in the datacenters of CSPs. Thus it becomes important for CSPs to form a federation with other CSPs, in such a way that the migration cost of VMs between CSPs of the same federation is minimized and simultaneously profit of CSPs in federation is maximized. In this paper, we model the problem of forming federation among CSPs as a hedonic coalition game, with a utility function depending on profit and migration cost, with the objective of maximizing the former and minimizing the latter. We propose an algorithm to solve this hedonic game and compare its performance with other existing game-theory based cloud federation formation mechanisms.     

Metapopulation structure and movements in five species of checkerspot butterflies

We studied the patterns and rates of migration among habitat patches for five species of checkerspot butterflies (Lepidoptera: Melitaeini) in Finland: Euphydryas aurinia, E. maturna, Melitaea cinxia, M. diamina and M. athalia. We applied the virtual migration (VM) model to mark-release-recapture data collected from multiple populations. The model includes parameters describing migration and survival rates and how they depend on the areas and connectivities of habitat patches. The number of individuals captured varied from 73 to 1,123, depending on species and sex, and the daily recapture probabilities varied between 0.09-0.52. The VM model fitted the data quite well. The results show that the five species are broadly similar in their movement rates and patterns, though, e.g. E. maturna tends to move shorter distances than the other species. There is no indication of any phylogenetic component in the parameter values. The parameter values estimated for each species suggest that a large percentage (80-90%) of migration events were successful in the landscapes that were studied. The area of the habitat patch had a substantial effect on emigration and immigration rates, such that butterflies were more likely to leave small than large patches and large patches were more likely than small patches to receive immigrants.