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In recent years, the rapid development of mobile devices and embedded system raises a demand for intelligent models to address increasingly complicated problems. However, the complexity of the structure and extensive parameters press significantly on efficiency, storage space, and energy consumption. Additionally, the explosive growth of tasks with enormous model structures and parameters makes it impossible to compress models manually. Thus, a standardized and effective model compression solution achieving lightweight neural networks is established as an urgent demand by the industry. Accordingly, Dynamic Channel Ranking Strategy (DCRS) method is proposed to compress deep convolutional neural networks. DCRS selects channels with high contribution of each prunable layer according to compression ratio searched by reinforcement learning agent. Compared with current model compression methods, DCRS efficaciously applies various channel ranking strategies on prunable layers. Experiments indicate with a 50% compression ratio, compressed MobileNet achieved 70.62% top1 and 88.2% top5 accuracy on ImageNet, and compressed ResNet achieved 92.03% accuracy on CIFAR-10. DCRS reduces more FLOPS in these neural networks. The compressed model achieves the best Top-1 and Top-5 accuracy on ResNet50, the best Top-1 accuracy on MobilNetV1. [ABSTRACT FROM AUTHOR]

With the fast growth of big data applications, it has brought about a huge increase in the energy consumption for big data processing in Cloud data centers. In this study, a frequency-aware and energy-saving strategy based on dynamic voltage and frequency scaling (abbreviated as FAESS-DVFS) is proposed to reduce energy consumption for big data processing in Spark on YARN. Energy saving in two layers (YARN layer and Spark layer) has been designed and implemented for the proposed method. First, an optimal CPU frequency is presented in YARN layer based on the minimum energy efficiency ratio (EER) which can be obtained from status monitoring module. Then, a task scheduling method in Spark layer is constructed to optimize the energy consumption by dynamically adjusting the CPU frequency of nodes in the life cycle of different stages. Test on Hibench, the proposed method can achieve substantial energy saving of up to 29.5% for big data processing compared with the default algorithm in Spark on YARN while satisfying SLA constrains. [ABSTRACT FROM AUTHOR]

Energy consumption is explosive increasing with the fast growth of big data applications. High carbon emissions from big data platforms have serious impacts on environment. In this paper, we propose an energy-aware scheduling algorithm for Spark (EASAS) to reduce energy consumption while satisfying the service level agreement (SLA). First, we present a new energy consumption model based on Spark framework. Then a strategy table for the relationship between tasks and executors is designed to record the execution time and energy consumption of tasks. The task scheduling in Spark is conducted and optimized based on the strategy table. The proposed strategy overcomes the defect of the default scheduling strategy FIFO and FAIR which cannot perceive energy consumption with the characteristics of energy consumption perception and dynamic optimization scheduling. Compared against FIFO and FAIR, Our EASAS effectively reduces on average about 25–40% of the total energy consumption of Spark applications under deadline constrains. [ABSTRACT FROM AUTHOR]

In virtualized data centers, consolidation of virtual machines (VMs) on minimizing the number of total physical machines (PMs) has been recognized as a very efficient approach. This paper considers the energy-efficient consolidation of VMs in a cloud datacenter. Concentrating on CPU-intensive applications, the objective is to schedule all requests non-preemptively, subjecting to constraints of PM capacities and running time interval spans, to make the total energy consumption of all PMs is minimized (called MinTE for abbreviation). The MinTE problem is NP-complete in general. We propose a self-adaptive approach called SAVE. The approach makes decisions of the assignment and migration of VMs by probabilistic processes and is based exclusively on local information. Both simulation and real environment test show that our proposed method SAVE can reduce energy consumption about 30 % against VMWare DRS and 10–20% against ecoCloud on average. Extensive experiments show that our method outperforms the existing method and achieves significant energy savings and high utilization. [ABSTRACT FROM AUTHOR]

High energy consumption in data center has become a key problem, and the Carbon (CO $$_2$$ ) emissions from data center have serious impacts on environment. To improve traditional performance-energy model and minimize energy consumption in cloud data center, an energy-efficient and QoS-aware (EEQoS) model is developed for virtual resources consolidation. The EEQoS model is designed to investigate the trade-off between energy and QoS. The user's satisfaction degree and Euclidean distance of resources are also respectively applied to the QoS model and power model. In addition, this EEQoS model is combined with the particle swarm optimization algorithm by setting the power consumption per QoS value as the objective function to consolidate virtual resources in data center. Compared with traditional power model, the proposed model reduces response time by 27.2%, cost by 31.4%, SLA violations by 40.5% and improves throughput by 13.5% on average while increasing less than 3.8% energy consumption during the whole test period. [ABSTRACT FROM AUTHOR]

Multi-Dimensional Resources Scheduling Problem (MDRSP, usually a multi-objective optimization problem) has attracted focus in the management of large-scale cloud computing systems as the collaborative operation of various devices in the cloud affects resource utilization and energy consumption. Effective management of the cloud requires a higher performance method to solve MDRSP. Considering the complex coupling between multi-dimensional resources and focusing on virtual machines allocation, we propose GGA-HLSA-RW (GHW, a novel family of genetic algorithms) to optimize the utilization and energy consumption of the cloud. In GGA-HLSA-RW, we add a growth stage to the genetic algorithm and construct a Growable Genetic Algorithm (GGA) using the Heuristic-based Local Search Algorithm (HLSA) with Random multi-Weights (RW) as the growth route. Based on the GHW, we propose GHW-NSGA II and GHW-MOEA/D by applying the sorting strategies and population regeneration mechanism of NSGA II and MOEA/D. To evaluate the performance of GHW, we carry out extensive experiments on the simulation dataset and AzureTraceforPacking2020 for the problems of minimizing the maximum utilization rate of resources for each dimension and minimizing total energy consumption. Experiment results demonstrate the advantages of growth strategy and dimensionality reduction strategy of GHW, as well as validate the applicability and optimality of GHW in realistic cloud computing. The experiments also demonstrate our proposed GHW-NSGA II and GHW-MOEA/D have better convergence rates and optimality than state-of-the-art NSGA II and MOEA/D. • For multi-dimensional resource scheduling of Cloud, Growable GA is better than GA. • GHW: Heuristic-based local search as the growth of GGA. • GHW-NSGA II and GHW-MOEA/D outperform NSGA II and MOEA/D. [ABSTRACT FROM AUTHOR]

Large-scale MapReduce clusters that routinely process big data bring challenges to the cloud computing. One of the key challenges is to reduce the response time of these MapReduce clusters by minimizing their makespans. It is observed that the order in which these jobs are executed can have a significant impact on their overall makespans and resource utilization. In this work, we consider a scheduling model for multiple MapReduce jobs. The goal is to design a job scheduler that minimizes the makespan of such a set of MapReduce jobs. We exploit classical Johnson model and propose a novel framework HScheduler, which combines features of both classical Johnson's algorithm and MapReduce to minimize the makespan for both offline and online jobs. Our Offline HScheduler reaches the theoretical lower bound (optimum) and Online HScheduler is 2-competitive which is the best-known constant ratio for minimizing the makespan. Through extensive real data tests, we find that HScheduler has better performance than the best-known approach by 10.6-11.7 % on average for offline scheduling and 8-10 % on average for online scheduling. The HScheduler can be applied to improve responsive time, throughput and energy efficiency in cloud computing. [ABSTRACT FROM AUTHOR]

Our paper considers the following fundamental scheduling problem. There are n deterministic jobs to be scheduled offline on multiple identical machines, which have bounded capacities. Each job is associated with a start-time, an end-time, a process time, and demand for machine capacity. The goal is to schedule all of the jobs non-preemptively in their start-time-end-time windows, subject to machine capacity constraints such that the total busy time of the machines is minimized. We refer to this problem as minimizing the total busy time for the scheduling of multiple identical machines (MinTBT). This problem has important applications in power-aware scheduling for Cloud computing, optical network design, customer service systems, and other related areas. Scheduling to minimize busy times is already NP-hard in the special case where all jobs have the same process time and can be scheduled in a fixed time interval. One best-known result for this problem is a 5-approximation algorithm for special instances using first-fit-decreasing algorithm. In this paper, we propose and prove a 3-approximation algorithm, modified first-fit-decreasing-earliest for the general case and obtain more results for special cases. We then show how our results are applied in cloud computing to improve the energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper considers online energy-efficient scheduling of virtual machines (VMs) for Cloud data centers. Each request is associated with a start-time, an end-time, a processing time and a capacity demand from a Physical Machine (PM). The goal is to schedule all of the requests non-preemptively in their start-time-end-time windows, subjecting to PM capacity constraints, such that the total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on multiple machines; it has important applications in power-aware scheduling in cloud computing, optical network design, customer service systems, and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances and unit-size jobs using First-Fit algorithm where g is the total capacity of a machine. In this paper, a $(1+\frac{g-2}{k}-\frac{g-1}{k^{2}})$ -competitive algorithm, Dynamic Bipartition-First-Fit (BFF) is proposed and proved for general case, where k is the ratio of the length of the longest interval over the length of the second longest interval for k>1 and g≥2. More results in general and special cases are obtained to improve the best-known bounds. [ABSTRACT FROM AUTHOR]