Cloud Data Centers Research Articles

EFFICIENT LOAD BALANCING ALGORITHM: MINIMIZING TIME WITH THRESHOLD- BASED APPROACH

The trending technology “Cloud Computing” provides computation and various resources over the internet through providing diverse level of dynamic services at a cost effective rate. The effectiveness of the infrastructure and the usage of cloud services will determine the direction of cloud computing in the future. There are always issues and difficulties in the implementation process of Cloud Computing. In order to better the cloud environment for users, load balancing seeks to meet these needs. Additionally, load balancing makes sure that when workloads are distributed, a particular node is never overloaded or underloaded. It also ensures that tasks are distributed equally. The goal is to investigate on several static and dynamic load balancing techniques in the field of cloud computing. The Threshold based load balancing approach consumes least amount of processing time at cloud data centers through experimental results.

An energy-efficient cloud data center architectural model for the national data center of Bangladesh

This study presents an energy-efficient cloud data center architecture concept for Bangladesh’s National Data Center. To reduce energy consumption and operational expenses, the model employs virtualization and load-balancing techniques, as well as renewable energy sources. The simulation results demon- strate that the model may drastically cut energy usage while maintaining high levels of performance and dependability. This research contributes to the worldwide challenge of climate change by offering a long-term solution for reducing the environmental effect of cloud data centers while maintaining the quality of cloud computing services.

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

Optimizing Renewable Energy Utilization in Cloud Data Centers through Dynamic Overbooking: An MDP-based Approach

Optimizing Renewable Energy Utilization in Cloud Data Centers through Dynamic Overbooking: An MDP-based Approach

A Safe Virtual Machine Scheduling Strategy for Energy Conservation and Privacy Protection of Server Clusters in Cloud Data Centers

A Safe Virtual Machine Scheduling Strategy for Energy Conservation and Privacy Protection of Server Clusters in Cloud Data Centers

Robustness of Workload Forecasting Models in Cloud Data Centers: A White-Box Adversarial Attack Perspective

Robustness of Workload Forecasting Models in Cloud Data Centers: A White-Box Adversarial Attack Perspective

An Efficient Hybrid Optimizer for Resource Reuse in a Cloud Environment

In a cloud context, merging complimentary numerous virtual machines (VMs) on an existing physical machine (PM) is the primary method for optimizing physical resources. One well-known area of research concentrates on making better use of VM migration resources when taking into account the dynamically changing resource demands of VMs. Finding the ideal balance between the complexity and performance of the VM migration optimization is the problem here. On the one hand, effective resource reuse is achieved through VM migration planning, and on the other, VM migration frequency is decreased to improve migration efficiency. On the other hand, a cloud data centre’s enormous PM and VM population typically makes migration planning more challenging, which impedes the VM migration decision-making process. By reducing the number of VM migration options to make VM migration planning easier and address these issues, this study recommend a hybrid Ant Colony and Genetic Algorithm (AGO) resource pool architecture. Then, establishing this model as a base, we develop the hybrid resource-reuse optimization method, which maximizes resource utilization with a minimal number of VM migrations. Finally, we evaluate hybrid AGO using simulation testing and real-world trials on a working cloud platform. Compared to similar methods, the findings show that hybrid AGO increases average resource utilization by 15%, reduces the use of PMs by 15%, and decreases the average number of migrations by 30%.

SSEPC cloud: Carbon footprint aware power efficient virtual machine placement in cloud milieu

The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.

A New Throttled Adapted Load Balancing (TALB) Strategy for Dynamic VM Allocations in Cloud Datacenters

A New Throttled Adapted Load Balancing (TALB) Strategy for Dynamic VM Allocations in Cloud Datacenters

Service Capability Aware Big Data Workflow Scheduling Approach in Cloud Datacenter

Service Capability Aware Big Data Workflow Scheduling Approach in Cloud Datacenter

An Adaptive Threshold-Based Modified Artificial Bee Colony Optimization Technique for Virtual Machine Placement in Cloud Datacenters

An Adaptive Threshold-Based Modified Artificial Bee Colony Optimization Technique for Virtual Machine Placement in Cloud Datacenters

A new throttled adapted load balancing strategy for dynamic VM allocations in cloud datacentres

A new throttled adapted load balancing strategy for dynamic VM allocations in cloud datacentres

Service capability aware big data workflow scheduling approach in cloud datacentre

Service capability aware big data workflow scheduling approach in cloud datacentre

Deep CNN and LSTM Approaches for Efficient Workload Prediction in Cloud Environment

In the dynamic landscape of cloud computing, efficient resource allocation and workload prediction are paramount for optimizing infrastructure utilization, cost management, and overall service quality. As a result, an efficient resource management approach involves pooling available resources among many customers in a manner that considers their energy usage, SLAs, and forecasting accuracy all at once. Based on the performance analysis results, it is determined that integrating these techniques would result in the most efficient and adaptable cloud data centre. We proposed a Model based on Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) for handling SLAs in the cloud from both the perspective of consumers and service providers. Subsequently, we delve into the methodology of applying deep CNN and LSTM models to the problem of workload prediction in cloud environments. This methodology encompasses data preprocessing, model architecture, training parameters, and the choice of performance metrics. To anticipate CPU consumption from time series data and detect SLA violations, we suggested a DCNN-LSTM model. The accuracy prediction, energy usage, CPU use, and Service Level Agreement monitoring are all a part of this model. The proposed method is effective in helping cloud providers cut down on service violations and associated fines. Regarding the composite metric of Energy SLA Violation, which assesses the combined aspects of energy use and adherence to Service Level Agreements (SLAs), DCNN-LSTM surpasses ARIMA-LSTM, CNN, LSTM, and ARIMA 6.8%, 10.88%, 16.6%, and 22.4%, respectively.

Enhanced placement and migration of virtual machines in heterogeneous cloud data centre

Enhanced placement and migration of virtual machines in heterogeneous cloud data centre

The impact of data recovery criteria, data backup schedule and data backup prosses on the efficiency of data recovery management in data centers

A large-scale cloud data center must have a low failure incidence rate and great service dependability and availability. However, due to several issues, such as hardware and software malfunctions that regularly cause task and job failure, large-scale cloud data centers still have high failure rates. These mistakes can have a substantial impact on cloud service dependability and need a large resource allocation to recover from failures. Therefore, it is important to have an efficient management of data recovery to protect organizations data from loss. This paper aims to study some factors that may improve the management of data recovery by using quantitative research design as a methodology. The results of hypothesis testing give strong evidence supporting the positive and significant correlations between the proposed hypotheses and the efficiency of data management recovery. This study finds that the presence of a data center in an organization demands the development of a solid plan for the most effective usage of a software program to handle data recovery.

Dynamic Optimization of Energy Efficiency and Service Level Agreement violation in Cloud Data Centers via Virtual Machine Consolidation

With the escalating proliferation of cloud computing services, cloud data center providers are actively in pursuit of solutions not only to curtail energy consumption but also to efficiently cater to the unique demands of their users, thereby enhancing user satisfaction. The process of consolidating virtual machines emerges as a viable strategy for striking a balance between energy consumption and Service Level Agreement violation (SLAv), potentially surmounting this predicament faced by cloud service providers. This study aims to minimize energy consumption and SLAv to their utmost feasible extent by utilizing the First Median Threshold (FMT) technique for physical host selection and virtual machine allocation, in conjunction with the Maximum Ratio of CPU utilization to memory Utilization (MRCU) method for virtual machine selection within the framework of virtual machine consolidation. This dual-pronged approach not only translates into cost savings for cloud providers but also ensures the delivery of optimal services to users. Furthermore, a novel method has been introduced for selecting virtual machines for migration and appropriately choosing physical hosts to achieve equilibrium between two critical parameters: energy consumption and SLAv. The proposed methodology is implemented and evaluated using the CloudSim simulator, utilizing real-world PlanetLab data. The comprehensive analysis of the simulation results reveals that the proposed approach can significantly reduce energy consumption while effectively mitigating SLAv for users.

An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges.

Energy-efficient resource allocation in cloud infrastructure using L3F-MGA and E-ANFIS

In industry along with academia, Task Scheduling (TS) along with Resources Allocation (RA) remains a challenging issue even though lots of work was carried out in the Cloud Computing (CC) field. CC uses massive virtualized Data Centers (DC). Still, it took an enormous quantity of energy on account of the constant demand by users for resources in addition to inefficient RA techniques. The higher Energy Consumption (EC) because of improper RA inside DC can be overcome using energy-aware RA techniques. Therefore, this paper proposes a Levy flight Firefly Modified Genetic Algorithm (L3F-MGA) as well as an Entropy-based Adaptive Neuros-Fuzzy Inferences System (E-ANFIS) for energy-efficient Resources Allocation (EE-RA) in cloud structure that manages the user demands and also lessens the EC of Cloud Data Center (CDC). In the proposed work, centred on task features together with cloud resource features, the input tasks are arranged using L3F-MGA. Conversely, the Virtual Machine (VM) status is identified. After that, centred on the task feature and VM status details, the E-ANFIS allows the active VM to complete the tasks effectively. The experiments compare the proposed method's performance for effective RA with other associated works. The proposed RA technique shows EC by 11 % lesser than the existing CM-GA. The Processing Time of E-ANFIS is 8 % lesser than CM-GA, 14 % less than ANFIS, 20 % less than GA, and 16 % less than PSO.

Achieving resilient cities using data-driven energy transition: A statistical examination of energy policy effectiveness and community engagement

In an era where energy usage is increasingly critical due to the growing prevalence of cloud computing data centers, this study delves into the realm of dynamic energy management (DEM) within cloud data centers (CDCs) to foster efficient power utilization. Our research introduces a Dynamic System Management (DSM) framework tailored for CDCs, encompassing a dynamic voltage scaling (DVS) management unit, a load balancing unit, and a task scheduling unit (TSU). The TSU employs a stochastic Petri net-based planning procedure and advances a 'role-based' resource placement approach, optimizing task scheduling for enhanced system performance and energy efficiency. Through comprehensive simulation tests, we validate the proposed system scheme. Furthermore, this paper extends its focus to augment the resilience of energy policies in the context of smart cities and foster community engagement. Building upon the existing Biogeography-Based Optimization (BBO), we propose enhancements to fortify its robustness and capabilities. Notably, our modified BBO approach demonstrates remarkable reductions in power consumption—27%, 32%, and 39% less when compared to BBO, particle swarm optimization, and genetic algorithms, respectively. These findings underscore the pivotal role of data-driven energy transition and community engagement in building resilient cities for the future.