Power Management Strategies and the impact of carbon dioxide emission from the data centers across the globe have drawn significant attention worldwide. The rapid growth of energy consumed in data centers has lead to 1) huge costs 2) depletion of non-renewable resources such as coal and petroleum, and 3) emission of greenhouse gases like CO2 in the atmosphere. These greenhouse gas significantly contributes to the climate change of the earth. To tackle this challenge, our research deals with modeling the energy resources of data centers, thereby offering insights to reduce global carbon footprint and energy cost. In our model, we prioritize green energy consumption by eliminating the brown energy resources. In this process, we devise an algorithm that can determine the amount of CO2 emission in the atmosphere per hour by different energy resources. We create an energy model for data centers by incorporating the support vector regression algorithm. Our model is adroit at projecting energy consumed in data centers powered by green energy. Our experimental results confirm that our model consistently delivers high prediction accuracy in terms of energy usage in data centers. The model is expected to facilitate data analytic venues to optimize energy efficiency and sustainability for the development of future data centers.

Leveraging on-site renewable sources like solar and wind provides ample opportunities on developing environmental friendly and energy-efficient data centers. Evidence shows that renewable-aware job schedulers conserve energy by adjusting the arrangement of non-urgent workload according to renewable energy states. We propose an energy management system with a renewable-aware scheduler called REDUX3, which offers a smart way of managing the energy supply of data centers powered by the grid and renewable energy. Due to the intermittent nature of renewable energy resources, REDUX3 judiciously back-fills workload when renewable energy is sufficient, and defer workload to the next time slot if renewable energy is at outage state. As an integrated and smarter update from our previous work [1], [2] and [3], REDUX3 also orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuating level. Compared with the existing strategies, REDUX3 demonstrates a prominent capacity of boosting renewable energy utilization.

Abstract Models for virtual machines running on cloud computing systems. Modeling system behaviors of clouds is a grand challenge because the resource utilization in VMs is heterogeneous due to variability in workload conditions. We address this challenging issue by uniquely (1) objectifying the usage prediction of virtualized resources and (2) predicting the performance trends of programs running on clouds. At the heart of the modeling system, we pay particular attention to CPU cores, disk size, main memory space, and input data volume, which serve as important factors for the developed prediction module. We devise two resource‐utilization prediction algorithms driven by two distinctive sets of I/O and CPU intensive benchmarks, where one algorithm deals with execution time and the other one revolves around input data size. We investigate the correlation between CPU/disk utilization and VM live migrations. Our system aims at not only providing performance optimization for virtualized resources but also ensuring service level agreement (SLA) and Quality of Service (QoS). The model fits the curve quite well, thereby advocating for the efficiency of the algorithm. The case studies conducted in this project draw the comparisons between the performance of striped and monolithic disks as well as bringing forth the problem of cache coherence that causes hindrance to the experiment. We also deal with the cache‐coherence problem to improve the accuracy of our prediction algorithms

In modern cloud computing environments, there is a tremendous growth of data to be stored and managed in data centers. Large-scale data centers demand high utilization of computing and storage resources, which lead to expensive operational cost for energy usage. Evidence shows that consolidating virtual machines (VMs) can conserve energy consumption in clouds through VM migrations. VM-consolidation techniques, however, inevitably induce a burden on performance. To address this issue, we propose a holistic solution - EGRET - to boost energy efficiency of cloud computing platforms by seamlessly integrating the DVFS scheme with the VM-consolidation technique. EGRET dynamically determines the most energy-efficient strategy by issuing a command to either scale CPU frequencies on a VM or marking the VM as underutilized. We conduct extensive experiments to evaluate the performance of EGRET. The experimental results show that EGRET substantially improves the energy efficiency of cloud computing platforms.

With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapid expanding research field, which provides an unprecedented opportunity in various clinical applications such as drug response predictions and disease diagnosis. It is thus essential and desirable to build a prediction model for clinical outcomes based on microbiome data that usually consist of taxon abundance and a phylogenetic tree. Importantly, all microbial species are not uniformly distributed in the phylogenetic tree but tend to be clustered at different phylogenetic depths. Therefore, the phylogenetic tree represents a unique correlation structure of microbiome, which can be an important prior to improve the prediction performance. However, prediction methods that consider the phylogenetic tree in an efficient and rigorous way are under-developed. Here, we develop a novel deep learning prediction method MDeep (microbiome-based deep learning method) to predict both continuous and binary outcomes. Conceptually, MDeep designs convolutional layers to mimic taxonomic ranks with multiple convolutional filters on each convolutional layer to capture the phylogenetic correlation among microbial species in a local receptive field and maintain the correlation structure across different convolutional layers via feature mapping. Taken together, the convolutional layers with its built-in convolutional filters capture microbial signals at different taxonomic levels while encouraging local smoothing and preserving local connectivity induced by the phylogenetic tree. We use both simulation studies and real data applications to demonstrate that MDeep outperforms competing methods in both regression and binary classifications. Availability and Implementation: MDeep software is available at https://github.com/lichen-lab/MDeep Contact:[email protected].

To cut back energy consumption of virtual-machine-powered data centers, we build an optimization model for virtual machines running in DVFS-enabled cloud data centers. With the model in place, cloud computing systems are equipped to keep track of dynamic power and static power of processors in virtual machines. Unlike existing dynamic voltage and frequency scaling schemes, our solution orchestrates frequency requirements rather than task execution times. The model makes it possible to obtain an optimal frequency ratio, which minimizes energy consumption of virtual machines. As a result, a data center's energy efficiency is boosted by controlling CPU frequency to meet the optimal frequency ratio. We demonstrate a way of manipulating frequency ratios to pushing up energy efficiency without violating virtual machines' frequency requirements. The experimental results unveil that our modeling approach offers a practical way of conserving the energy consumption of virtual machines running in data centers.