A comprehensive survey for scheduling techniques in cloud computing

Q-learning based dynamic task scheduling for energy-efficient cloud computing

Using the internet of things in smart energy systems and networks

Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality Xin such circumstances. In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12X) and reducing end device energy consumption (by up to 58%).

Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems

A cloud server energy consumption measurement system for heterogeneous cloud environments

Experimental and quantitative analysis of server power model for cloud data centers

Machine learning and its promising branch deep learning have proven to be effective in a wide range of application domains. Recently, several efforts have shown success in applying deep learning techniques for automatic vulnerability discovery, as alternatives to traditional static bug detection. In principle, these learning-based approaches are built on top of classification models using supervised learning. Depending on the different granularities to detect vulnerabilities, these approaches rely on learning models which are typically trained with well-labeled source code to predict whether a program method, a program slice, or a particular code line contains a vulnerability or not. The effectiveness of these models is normally evaluated against conventional metrics including precision, recall and F1 score. In this paper, we show that despite yielding promising numbers, the above evaluation strategy can be insufficient and even misleading when evaluating the effectiveness of current learning-based approaches. This is because the underlying learning models only produce the classification results or report individual/isolated program statements, but are unable to pinpoint bug-triggering paths, which is an effective way for bug fixing and the main aim of static bug detection. Our key insight is that a program method or statement can only be stated as vulnerable in the context of a bug-triggering path. In this work, we systematically study the gap between recent learning-based approaches and conventional static bug detectors in terms of fine-grained metrics called BTP metrics using bug-triggering paths. We then characterize and compare the quality of the prediction results of existing learning-based detectors under different granularities. Finally, our comprehensive empirical study reveals several key issues and challenges in developing classification models to pinpoint bug-triggering paths and calls for more advanced learning-based bug detection techniques. 

How About Bug-Triggering Paths? - Understanding and Characterizing Learning-Based Vulnerability Detectors

The invention discloses a part energy consumption model-based cloud server energy consumption calculation method and system. The method comprises the steps that a hardware detection module in the system collects server hardware information; a model matching module performs matching with a built-in hardware model database according to the obtained hardware information, finds corresponding energy consumption parameters and writes the parameters in an energy consumption model configuration file; a data collection module collects a resource utilization rate; a power consumption estimation module performs system or process real-time energy consumption calculation by using energy consumption models of corresponding parts according to the information obtained by adaptation and the resource utilization rate; the system performs persistent storage in two modes on the calculated server energy consumption information; and the system can transmit the server hardware information and historical andreal-time energy consumption data to a statistics end program. Through the method and the system, a user can view and monitor energy consumption information of a current server in real time; and an energy consumption reference is provided for load scheduling of a data center.

Part energy consumption model-based cloud server energy consumption calculation method and system

The rapid maturing of nanotechnology and its wide range of applications not bring benefits only, so the downsides are worth noting. Nanopollutants, generally undetectable, are often found as byproducts involved in various chemical or physical reactions. Many nanopollutants are concerned and suspected of causing negative impact on human health due to their unpredictable existence in the environment. Unlike the traditional sensing systems which can detect select gaseous molecules, effective nanoparticle sensing methods are still lacking in the literature. In this paper, sensing of zinc-containing nanopollutants suspended in N2 with a room-temperature ionic liquid (RTIL) ([C4mim][PF6] (1-butyl-3-methyl imidazolium hexafluorophosphate)) has been studied. The sensitivities (RIL/Rnano) obtained by determination of the RTIL resistance to the absorbed phosphor fly ash (<200 nm) is 7.0, while the RTIL has higher sensitivity of 11 and 10, respectively, to ZnO and ZnS nanoparticles By componentfitted X-ray absorption near edge structure (XANES) spectroscopy, it seems that the N-methyl imidazole (mim) complexes are formed during sensing of the phosphor fly ash, ZnO and ZnS nanoparticles with the RTIL. The 1H and 31P NMR observations also suggest that the (mim) behaves as a carrier during sensing of nanopollutants with the RTIL.

/pdf/sensing-of-zinc-containing-nanopollutants-with-an-ionic-3uu9weva4e.pdf

Haoyu Wang

Papers

A cloud server energy consumption measurement system for heterogeneous cloud environments

Experimental and quantitative analysis of server power model for cloud data centers

How About Bug-Triggering Paths? - Understanding and Characterizing Learning-Based Vulnerability Detectors

Part energy consumption model-based cloud server energy consumption calculation method and system

Sensing of zinc-containing nanopollutants with an ionic liquid