Cloud Data Centers Research Articles

Cloud computing load prediction method based on CNN-BiLSTM model under low-carbon background

With the establishment of the "double carbon” goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM ), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} , 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, 34%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} and 45%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} respectively.

DBSCAN based approach for energy efficient VM placement using medium level CPU utilization

Virtual machine placement (VMP) is a popular problem in Cloud Data Centers (CDCs). An efficient virtual machine (VM) allocation is essential for processor speed and energy saving. This is more useful where the CDC uses an Internet of Things (IoT) infrastructure. To enhance energy savings, we aim to improve the adaptive four thresholds energy-aware framework for VM deployment. We observed that the role of the threshold for identifying the over-loaded host is crucial. In order to determine the appropriate threshold, we employed density-based spatial clustering of applications with noise (DBSCAN), medium absolute deviation (MAD), and interquartile range (IQR) using the medium fit power efficient decreasing (MFPED) algorithm. Our proposed algorithm modified medium fit energy efficient decreasing (MMFEED) achieves a reduction in energy consumption of 47.3%, 46.1%, 39%, 23.2%, 10.9%, and 3.4% compared to the IQR, MAD, static threshold (THR), exponential weighted moving average (EWMA), modified energy-efficient virtual machine placement (MEEVMP), and adaptive four threshold energy-aware framework for VM deployment energy efficient (AFED-EF), respectively, under the minimum migration time (MMT) selection policy. The proposed algorithm outperforms these algorithms in terms of energy consumption for VM selection policy MMT.

Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN

Anomaly detection is an important task for maintaining the performance of cloud data center. Traditional anomaly detection primarily examines individual Virtual Machine (VM) behavior, neglecting the impact of interactions among multiple VMs on Key Performance Indicator (KPI) data,e.g., memory utilization. Furthermore, the non-stationarity, high complexity, and uncertain periodicity of KPI data in VM also bring difficulties to deep learning-based anomaly detection tasks. To settle these challenges, this paper proposes MCBiWGAN-GTN, a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network (BiWGAN-GTN) and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). (a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data. (b) The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process. (c) MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components. (d) To adapt the proposed algorithm for the entire cloud data center, a cloud data center anomaly detection framework based on Swarm Learning (SL) is designed. The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline, with an F1-score of 0.96, an accuracy of 0.935, a precision of 0.954, a recall of 0.967, and an FPR of 0.203. The experiments also verify the stability of MCBiWGAN-GTN, the impact of parameter configurations, and the effectiveness of the proposed SL framework.

Distributed service function chaining in NFV-enabled networks: A game-theoretic learning approach

In network function virtualization (NFV), Service Function Chaining (SFC) provides an ordered sequence of virtual network functions (VNFs) and subsequent steering of traffic flows through them to cater to end-to-end services. This paper addresses the NP-hard problem of minimum cost SFC deployment to support customer services that access the carrier network’s NFV infrastructure (NFVI) through some edge routers. To determine the mappings of VNFs to physical servers, a challenging aspect would be the inter-server latencies that may fluctuate over time because of the sharing nature of cloud data centers. To construct the SFC, we come up with three different formulations, each corresponding to a different informational assumption about the link latencies: First, a centralized integer linear programming (ILP) formulation is given under the assumption of the non-causal availability of exact and instantaneous inter-server latencies. The solution to this ILP can serve as a lower bound to benchmark more scalable and realistic schemes. Next, we give a distributed game-theoretic formulation (with service broker agents as players) which only requires the statistical knowledge of link latency fluctuations. The game provably admits a pure Nash equilibrium (NE) and can be solved iteratively through the well-known best response dynamics (BRD) algorithm. Our main novelty lies in the third formulation in which each service broker has neither instantaneous nor statistical knowledge of the latencies. Instead, it relies on a game-theoretic learning algorithm to compose its VNF chain only based on its own history of adopted decisions and experienced delays on each logical link. We prove that the proposed learning algorithm asymptotically converges to NE and evaluate its performance through simulations in terms of convergence and the impact of network parameters.

Multi-objective optimization of virtual machine migration among cloud data centers

AbstractWorkload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.

Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers

As energy-intensive infrastructures, data centers (DCs) have become a pressing challenge for managers due to their significant energy consumption and carbon emissions. Information technology (IT) and cooling systems contribute the most to energy consumption. Energy-aware virtual machine (VM) scheduling methods have been widely demonstrated to reduce energy consumption and operating costs in DCs. However, as realistic DCs exhibit complex power and thermodynamic behaviors, existing works cannot provide efficient measures to optimize computing and cooling power consumption simultaneously. To overcome this challenge, we construct a holistic thermal model (including CPU and server inlet thermal models) to accurately represent the non-uniform, dynamic thermal environment. Subsequently, this work proposes a thermal model-based energy-aware VM placement method (TEVP) to minimize the holistic energy consumption of the DCs, considering resource and thermal constraints. We develop a novel hybrid swarm intelligence algorithm (DE-ERPSO) combining differential evolution (DE) and particle swarm optimization with an elite re-selection mechanism (ERPSO) to explore more energy-efficient VM placement schemes. Extensive experiments are conducted on an extended CloudSim to validate the performance of the proposed TEVP using real-world workload traces (PlanetLab and Azure). Results show that TEVP saves over 5.6% of the total energy consumption over the advanced baselines while maintaining low thermal violations.

Energy-efficient virtual machine placement in heterogeneous cloud data centers: a clustering-enhanced multi-objective, multi-reward reinforcement learning approach

Energy-efficient virtual machine placement in heterogeneous cloud data centers: a clustering-enhanced multi-objective, multi-reward reinforcement learning approach

Enhancing virtual machine placement efficiency in cloud data centers: a hybrid approach using multi-objective reinforcement learning and clustering strategies

Enhancing virtual machine placement efficiency in cloud data centers: a hybrid approach using multi-objective reinforcement learning and clustering strategies

Scaling Power Management in Cloud Data Centers: A Multi-Level Continuous-Time MDP Approach

Scaling Power Management in Cloud Data Centers: A Multi-Level Continuous-Time MDP Approach

An Implementation on Energy Efficient Task Scheduling in Cloud Environment

Abstract: Cloud computing has revolutionized the way computing resources are utilized by providing scalable, on-demand access to a shared pool of resources over the internet. It offers significant advantages such as cost savings, flexibility, and accessibility, making it an essential technology for businesses and individuals alike. However, the increasing energy consumption associated with cloud data centres has become a critical concern, necessitating the development of energy-efficient task scheduling algorithms. This paper explores various algorithms designed to optimize task scheduling in cloud environments to reduce energy consumption. Among these, heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are popular for their ability to find near-optimal solutions in large search spaces. Additionally, machine learning-based approaches, such as Reinforcement Learning (RL), have shown promise in dynamically adapting to workload variations and improving energy efficiency. Other notable algorithms include Ant Colony Optimization (ACO) and Dynamic Voltage and Frequency Scaling (DVFS), each offering unique mechanisms to balance performance and energy usage. The focus of this paper is on the implementation and comparative analysis of these task scheduling algorithms in a cloud environment. We present a comprehensive evaluation of their performance, highlighting their strengths and limitations in achieving energy efficiency. The results demonstrate that while no single algorithm is universally optimal, tailored combinations of these approaches can significantly enhance energy savings in cloud data centres.

APPLICATION OF ALGORITHMS FOR ANOMALY DETECTION IN HEALTH-ENABLED SENSOR-CLOUD INFRASTRUCTURE

Real-time patient monitoring and early disease diagnosis are two ways that the healthcare industry is benefiting from the integration of sensors and cloud technology. In order to detect changes in patient's health, a variety of non-invasive sensors are applied to the skin to monitor various physiological parameters. The collected data are then wirelessly communicated to the cloud data center. However, the transmitted data are susceptible to several sources of interference called anomalies. Anomalies is when a sudden change occurs from the expected sensor data generated. This may be as a result of sensor faults, measurement faults, injection and alteration by malicious attackers. Therefore, this research tends to conduct a survey on existing algorithms or techniques used for the detection of anomalies in health-enabled sensor-cloud infrastructure. The processes adopted by the algorithms were identified and discussed exhaustively. In addition, the simulation setup and programming languages adopted to implement and evaluate the existing algorithms, followed by the limitations of the algorithms, which may lead to future research directions are captured in this paper. The outcome of the research shows that machine learning algorithms were predominantly adopted for detecting anomalies with the support of clustering and classification processes. Furthermore, Visual Basic.Net simulation tool and Python programming language was mostly adopted for experimentation and evaluation of the existing techniques. Limitations such as overfitting, under-fitting, computation complexity (time and memory space), and missing data are hindering the optimal performance of existing algorithm, which needs to be addressed in future researches.

A Combined Marine Predators and Particle Swarm Optimization for Task Offloading in Vehicular Edge Computing Network

With the rapid advancement in technology, numerous advanced vehicular applications have emerged that generate large volumes of data that need to be processed on the fly. The vehicles' computing resources are limited and constrained in processing the huge amount of data generated by these applications. Cloud data centers, which are large and capable of processing the generated data, tend to be far away from the vehicles. The long distance between the cloud and the vehicles results in large transmission delays, making the cloud less suitable for executing such data. To address the long-standing issue of huge transmission delays in the cloud, edge computing, which deploys computing servers at the edge of the network, was introduced. The edge computing network shortens the communication distance between the vehicles and the processing resources and also provides more powerful computation compared to the vehicles' computing resources. The advantages offered by the vehicular edge network can only be fully realized with robust and efficient resource allocation. Poor allocation of these resources can lead to a worse situation than the cloud. In this paper, a hybrid Marine Predatory and Particle Swarm Optimization Algorithm (MPA–PSO) is proposed for optimal resource allocation. The MPA–PSO algorithm takes advantage of the effectiveness and reliability of the global and local search abilities of the Particle Swarm Optimization Algorithm (PSO) to improve the suboptimal global search ability of the MPA. This enhances the other steps in the MPA to ensure an optimal solution. The proposed MPA–PSO algorithm was implemented using MATLAB alongside the conventional PSO and MPA, and the proposed MPA–PSO recorded a significant improvement over the PSO and MPA.

Optimization of containerized application deployment in virtualized environments: a novel mathematical framework for resource-efficient and energy-aware server infrastructure

This study addresses the critical need for effective resource management through software container migration in cloud data centers. It emphasizes its role in avoiding resource shortages and reducing energy consumption in cloud environments. This study introduces a novel multi-objective integer linear programming (ILP) approach for software container replacements, complemented by a specialized algorithm designed to migrate software containers between over- and underutilized hosts to enhance efficiency compared to traditional optimization methods. The simulation results demonstrate the algorithm's effectiveness, validating its potential for optimizing energy usage and resource allocation in cloud environments. Statistical analyses confirm the proposed model's and algorithm's superiority over benchmark approaches, highlighting their potential for enhancing resource management in cloud computing systems.

Sustainable energy efficient workflow classification and scheduling in geo distributed cloud datacenter

Data centers are a major source of carbon emissions and are subsequently contributing to global carbon footprints. Keeping in view of providing a sustainable solution to society, we have analyzed various factors that can help to achieve carbon neutrality and maximum sustainability. Our study pointed towards the need to follow a sustainable approach for incoming workflow throughout the life-cycle of Data centers. We analyzed that workloads need to be segregated before assigning them to the data centers so that energy-efficient resource allocation could be done. This paper demonstrates unsupervised learning techniques to cluster the incoming cloud workloads. The heterogeneous workloads were characterized using machine learning approaches and appropriate clusters were crafted. For analysis, Google Cluster Dataset is used. In order to improve the accuracy, data were normalized, and random samples of data were selected for clustering. The machine learning algorithms applied were able to successfully determine the appropriate clusters that can further be used for energy-efficient resource scheduling.

Performance optimization and energy minimization of cloud data center using optimal switching and load distribution model

Cloud computing is an effective computing methodology used in all stages of business. Most of the Cloud Data Centers (CDC) operates on the basis of peak load and huge scales. Hence, it necessitates saving the energy in CDC. This study introduces an energy-efficient strategy based on the fat tree. Here, Taylor-based Manta-Ray Foraging Optimization (Taylor-MRFO) is developed by combining the Taylor series with Manta Ray Foraging Optimization (MRFO) to distribute the load in a CDC. In load distribution, the cloud data switching to the preferred mode is done by the Actor critic neural network (ACNN). Furthermore, the developed Taylor-MRFO+ACNN provided a better outcome than the conventional approaches with the least energy consumption of 0.4930, least load of 0.3631, and least fitness of 0.4343. For setup-1, when the population size is 15, the load value obtained by the proposed method is 23.43 %, 10.19 %, 7.18 %, 5.31 %, 4.43 %, and 2.58 % higher when compared to the existing approaches namely, Artificial Bee colony(ABC), Efficient Load Optimization and Resource Minimization (ELORM), Adaptive Parameter- Ant Colony Optimization (AP-ACO), Multi-Objective Memetic Algorithm-Adaptive Plant Intelligent Behavior Optimization (MOMA-APIBO), Cooling Control Algorithm (CCA), and Minimum Total Power (MinPR).

Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm

This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

Minimum-energy virtual machine placement using embedded sensors and machine learning

Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.

SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

SCRUB: a novel energy-efficient virtual machines selection and migration scheme in cloud data centers

Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in Cloud Data Centers

Utilizing multi-population ant colony system and exponential grey prediction model for multi-objective virtual machine consolidation in Cloud Data Centers

Efficient Resource Management in Cloud Environments: A Modified Feeding Birds Algorithm for VM Consolidation

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we introduce the Modified Feeding Birds Algorithm (ModAFBA) as an innovative solution for virtual machine (VM) consolidation in cloud environments. The primary objective is to enhance resource management and operational efficiency in cloud data centers. ModAFBA incorporates adaptive position update rules and strategies specifically designed to minimize VM migrations, addressing the unique challenges of VM consolidation. The experimental findings demonstrated substantial improvements in key performance metrics. Specifically, the ModAFBA method exhibited significant enhancements in energy usage, SLA compliance, and the number of VM migrations compared to benchmark algorithms such as TOPSIS, SVMP, and PVMP methods. Notably, the ModAFBA method achieved reductions in energy usage of 49.16%, 55.76%, and 65.13% compared to the TOPSIS, SVMP, and PVMP methods, respectively. Moreover, the ModAFBA method resulted in decreases of around 83.80%, 22.65%, and 89.82% in the quantity of VM migrations in contrast to the aforementioned benchmark techniques. The results demonstrate that ModAFBA outperforms these benchmarks by significantly reducing energy consumption, operational costs, and SLA violations. These findings highlight the effectiveness of ModAFBA in optimizing VM placement and consolidation, offering a robust and scalable approach to improving the performance and sustainability of cloud data centers.