To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability of UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewableenergy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously 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 fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.Index Terms-Renewable energy, uninterruptible power supply (UPS), distributed UPS systems, resource management, energy cost, data centers.• Energy cost of large-scale data centers is skyrocketing.• Consuming renewable energy in data centers brings economical and environmental benefits.

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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 K E Y W O R D S cache coherence, I/O intensive, linear model, monolithic and split disk, resource utilization INTRODUCTIONThis article is motivated by the rapid growth in demand for computation resources in data centers and the widespread of cloud computing. 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.This research is inspired by the following four motivations.Motivation 1: virtualization. Cloud computing has changed the landscape of computing resources delivery models. Large-scale virtualized data centers gain popularity thanks to the rapid growth in demand for computational power-driven by modern service applications combined with the shift to the cloud computing model. 1 The virtualization technique emulates physical computers with a mirrored operating system, virtual disks, virtual CPUs, and virtual memory resources. The emergence of virtual machines promises to streamline the on-demand provisioning of software,

To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability of UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewableenergy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously 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 fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.

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