Cloud Computing Data Centers Research Articles

Cross regional online food delivery: Service quality optimization and real-time order assignment

Online food delivery (OFD) represents a rapidly evolving e-business application that leverages cloud computing data centers, playing a crucial role in meeting the demands of urban lifestyles. With diverse order fulfillment features and increasing expectations for service quality, the task of effectively assigning riders for timely long-distance, cross-regional deliveries presents a significant engineering challenge. Previous studies often relied on traditional rider allocation methods that fail to account for varying capacities, or they utilized non-intelligent systems that did not adequately address fluctuating order demands and service delays. In this study, we introduce a robust Mixed Integer Linear Programming (MILP) optimization framework designed to minimize the total service time and delivery cost for cross-regional orders. This framework divides a large OFD area into multiple regions and utilizes both transfer vehicles and riders to optimize deliveries. To enhance the predictive accuracy of our model, we incorporate advanced machine learning techniques. Specifically, we employ the Long Short-Term Memory (LSTM) model to forecast regional order demands accurately, reflecting the dynamic nature of the marketplace. Additionally, Extreme Gradient Boosting (XGBoost) is tailored to dynamically predict travel times from restaurants to customer locations, facilitating more precise scheduling and resource allocation within the MILP framework. These machine learning techniques significantly bolster the MILP framework by providing detailed, accurate predictions that improve decision-making processes and adaptability to real-time conditions. Acknowledging the complexity of this optimization problem, we further enhance our approach by integrating a meta-heuristic algorithm, Adaptive Large Neighbor Search (ALNS), which efficiently assigns orders to the appropriate transfer vehicles and riders within polynomial time. Our Cross Regional Online Food Delivery (XROFD) system is meticulously designed to optimize both customer satisfaction and rider incentives. Simulation experiments confirm that the XROFD system not only reduces service times and delivery costs but also markedly enhances customer satisfaction and provides superior incentives for riders, outperforming existing state-of-the-art methods.

Quantum Enhanced Josephson Junction Field-Effect Transistors for Logic Applications

The need of data centers for cloud and network computing has dramatically increased power consumption. In 2014, data centers in the U.S. consumed an estimated 70 billion kWh, representing about 1.8% of total U.S. electricity consumption [“United States Data Center Energy Usage Report," published in June 2016 and supported by the Federal Energy Management Program of the U.S. Department of Energy]. Worldwide, the International Energy Agency estimates that currently about 1% of all global electricity is used by data centers. It is expected that by 2030 the electricity use of information and communications technology may exceed 20% of all global electricity. The microelectronics industry has been focusing on aggressively shrinking the size of logic and memory devices to reduce power consumption. However, this scaling is going to eventually stop as we approach the fundamental materials limit. Moreover, the power dissipation due to the increased number of transistors is also expected to be a limiting factor for processor performance.Superconducting computing has been suggested as a promising approach for low-energy, power-efficient circuit applications. Moreover, using superconducting interconnects can further reduce power consumption. Josephson junction field-effect transistors (JJFET, Fig. a) have recently emerged as a promising candidate for superconducting computing. JJFETs are particularly useful for low power consumption applications as they are operated with, in the superconducting regime, zero voltage drop across its source and drain. Feasibility of using JJFETs for logic operations has been explored [1]. Moreover, high noise margin has been demonstrated for the logic inputs [2]. Compared to single flux quantum approach, JJFET circuits can use designs similar to conventional silicon MOSFETs [1].For JJFETs to perform logic operations, the gain-factor (αR) value must be larger than 1. Here αR = dIc/d(Vg–Vt) x πΔ/Ic, Δ is the superconducting gap, Vg the gate bias voltage, Vt the threshold voltage. Ic ∝ exp(-L/ξc) is the critical supercurrent, where L is the channel length and ξc the carrier coherence length [3]. In a conventional JJFET, ξc ∝ (Vg–Vt)0.5 (Fig. b), and thus dIc/d(Vg–Vt) is small (Fig. c). This translates to a requirement of superconducting transition temperature of ~ 400K for αR larger than 1, far exceeding any recorded critical temperatures. As such, it is impossible to use conventional JJFETs for logic operations.Here, we propose a novel type of JJFET based on quantum phase transition, such as the excitonic insulator (EI) transition in a type-II InAs/GaSb heterostructure [4,5], for low-energy, power-efficient logic applications. The nature of the collective phenomenon in the EI quantum phase transition can provide a sharp transition of the supercurrent states (e.g., ξc ∝ (Vg–Vt)5 and dIc/d(Vg–Vt) very large, as shown in Figs. b and c, respectively) which will enable αR larger than 1 with an easy-to-achieve superconducting transition temperature of ~ 40K. In this talk, we will present some preliminary results demonstrating that indeed the gain factor in these quantum enhanced JJFETs can be greatly improved, thus making them a promising candidate for logic applications.Sandia National Laboratories is a multimission laboratory managed and operated by NTESS LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. DOE’s NNSA under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. DOE or the United States Government.

Synergies and Challenges in the Integration of Cloud Computing and Deep Learning: Current Status, Interconnectedness, and Future Directions

This article reviews the status and recent developments in the integration of cloud computing and deep learning, as well as the interrelationship between these two technologies. The paper explores the intersection of cloud computing and deep learning in addressing cybersecurity challenges. Amidst the rapid expansion of the worldwide public cloud services market, the vulnerability to cyber-attacks and breaches in data management is on the rise. Different intrusion detection systems use different deep learning techniques to improve the effectiveness of intrusion detection in cloud computing environments. Additionally, the use of encryption technology and the corresponding deep learning retrieval technology further improves the security of cloud data. Moreover, the paper deeply studies how the scheduling mechanism of deep reinforcement learning can optimize the performance of cloud services by efficiently allocating resources and solving the problem of slow cloud service speed. It also derives the optimal energy strategy through deep neural networks to address the energy consumption challenges in cloud computing data centers. This article also reviews the five emerging architectures of cloud computing and explores the role of deep learning within these frameworks. Finally, it analyzes some of the challenges facing the future of cloud computing and deep learning, including the security and confidentiality of cloud computing, as well as low latency and high throughput optimization in the field of deep learning. In summary, this article provides insight into current trends, challenges, and future prospects for the evolving integration between cloud computing and deep learning.

The butterfly effect of cloud computing on the low-carbon economy

This paper investigates the long-term butterfly effect of cloud computing on the low-carbon economy against the dual backdrop of the digital age and carbon neutrality. Utilizing a full-sample and sub-sample approach, the study identifies the complex interrelationship between the China Cloud Computing Index (CCI) and the Low Carbon Index (LCI). The quantitative examination reveals how CCI impacts LCI – both favorably and unfavorably. The positive effects suggest that cloud computing serves as a motivator in the environmentally friendly economy. In case of negative impacts, however, it is not possible to always determine the incentive effects owing to the large amounts of energy consumption generated by cloud computing data centers. Meanwhile, the positive influence of LCI on CCI indicates how the pursuit of low carbon-economy (as a goal) will bring opportunities for the explosive growth of the cloud computing industry. Cloud computing has increasingly prompted the fluctuation of carbon emissions in the whole ecosystem. By exploring this butterfly effect, this study digs deeper into the complex interrelationship between the discussed indexes. It is expected that the study outcomes will offer meaningful recommendations for the two main vectors of economic development - greenization and digitalization.

Optimizing Task Scheduling in Cloud Data Centres with Dynamic Resource Allocation Using Genetic Algorithm (TSOGA)

Nowadays, Massive business applications are increasingly giving attention to cloud computing data centres because of its high potential, adaptability, and efficiency in supplying several sources of both software and hardware to support networked consumers. The criteria for autonomy of virtual machines necessitate a flexible resource allocation strategy for Virtual Machines (VMs) .The majority of resource utilization models were inaccurate, making it impossible to determine the virtual machine's energy usage directly from the hardware. Due to the size of modern data centres and the constantly changing character of their resource supply, efficient scheduling solutions must be developed to oversee these resources and meet the objectives of both cloud service providers and cloud customers. Hence an algorithm called Task Scheduling Optimization based Genetic Algorithm (TSOGA) has been proposed to dynamically allocate the resources in pursuit of scheduling the tasks in cloud data centers. The proposed module initially focuses on task scheduling process, followed by optimized running time of task execution. For data centres with dynamic resource allocation, the goal of TSOGA is to efficiently assign jobs to resources while minimizing execution time and optimizing resource utilization. Thus, to manage the data centres while achieving high levels of efficiency in resource allocation, we constructed a virtual node for our research. Incorporation of Genetic Algorithm is to determine an ideal or nearly ideal schedule for carrying out tasks using the available resources while taking into account a variety of restrictions and goals, such as minimizing execution and waiting time of task during dynamic scheduling process and efficient resource utilization.

Bi-objective optimization and reliability analysis in unreliable M/G/1 retrial queues with rational customers, reserved idle times and setup times

ABSTRACT Energy consumption arising from no-loads and failures is a common and challenging problem in the design of service systems. To analyze the impact of no-load consumption and failure consumption on the energy consumption simultaneously, this paper considers an M/G/1 retrial queue with reserved idle times, setup times and server breakdowns. First, the steady-state condition and performance measures of the system are obtained by the embedded Markov chain and the supplementary variable method. Then, the reliability indicators of the system are analyzed, and an algorithm for solving the system reliability R ( t ) is constructed based on the Monte Carlo method. In order to better optimize the system, the customer’s equilibrium joining strategy is derived, and the socially optimal joining strategy is obtained by the particle swarm optimization (PSO) algorithm. From the decision-maker viewpoint, a bi-objective optimization model is constructed to minimize the expected energy consumption and the expected waiting time of retrial customers, and solved by the non-dominated ranking genetic algorithm II (NSGA-II). Finally, we illustrate an application of the proposed analytical approach in the cloud computing data center, which makes it possible to achieve an appropriate balance between energy consumption and quality of service (QoS).

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.

Mobile Edge Computing (MEC)-Enabled UAV Placement and Computation Efficiency Maximization in Disaster Scenario

Unmanned aerial vehicles (UAVs) are considered for disaster management to provide better coverage or help first responders provide advanced services. Furthermore, UAVs combined with mobile edge computing (MEC) capabilities, have recently emerged as a support for cloud computing by deploying UAV-assisted MEC at the network edge to reduce the latency and load of cloud computing data centers. We investigate the problem of maximizing the utility function by jointly optimizing the computing power, user associations, performance, duration, and location of user equipments (UEs) in UAV-assisted MEC networks in a disaster scenario. We considered several constraints including delay, quality of service, and coverage. The optimization problem is a mixed-integer non-linear programming problem. We propose a multi-stage offloading algorithm based on a learning algorithm and an interior-point method to obtain a viable solution. The simulation results obtained demonstrate the effectiveness of our proposed algorithm compared to existing schemes.

Fog Intelligence for Secure Smart Villages: Architecture and Future Challenges

The Internet of Things (IoT) technology is considered the foundation for next-generation smart villages due to its ability to use sustainable information and communication technologies. The smart villages can enable real-time data analytics and can automate decision-making for local villagers in terms of agriculture, health care, transportation, environment, and energy. However, most of the wireless sensing devices exchange information using public networks and therefore may not be able to resist all forms of attacks. Additionally, most of the IoT devices are resource restricted and use cloud servers to process and store data. However, when IoT devices communicate with cloud computing data centers, the volume of data causes network congestion. To provide secure services close to end devices, a new network architecture called Distributed Fog Computing (DFC) can be created and integrated with IoT-based smart villages deployment. Motivated from the aforementioned discussions, this article explores the integration of DFC with IoT in improving security and privacy solutions for villagers and Consumer Electronic (CE) devices. As a case study, we also design and evaluate the performance of an Intrusion Detection System (IDS) in DFC-based smart village environment. Finally, we discuss several open security issues and challenges regarding Fog-to-Things enabled smart villages.

VTGAN: hybrid generative adversarial networks for cloud workload prediction

Efficient resource management approaches have become a fundamental challenge for distributed systems, especially dynamic environment systems such as cloud computing data centers. These approaches aim at load-balancing or minimizing power consumption. Due to the highly dynamic nature of cloud workloads, traditional time series and machine learning models fail to achieve accurate predictions. In this paper, we propose novel hybrid VTGAN models. Our proposed models not only aim at predicting future workloads but also predicting the workload trend (i.e., the upward or downward direction of the workload). Trend classification could be less complex during the decision-making process in resource management approaches. Also, we study the effect of changing the sliding window size and the number of prediction steps. In addition, we investigate the impact of enhancing the features used for training using the technical indicators, Fourier transforms, and wavelet transforms. We validate our models using a real cloud workload dataset. Our results show that VTGAN models outperform traditional deep learning and hybrid models, such as LSTM/GRU and CNN-LSTM/GRU, concerning cloud workload prediction and trend classification. Our proposed model records an upward prediction accuracy ranging from 95.4% to 96.6%.

Two-Phase Virtual Machine Placement in Cloud Computing Data Centers Using Extreme Learning Machine Prediction-Based Triggering Method

Background: Two-phase Optimization of Virtual Machine Placement (VMP) Problem considers both the Online Incremental VMP (iVMP) phase in which the new arrival of dynamic requests of Virtual Machines VMs are attended to and the Offline VMP reconfiguration (VMPr) phase that performs placement recalculation. In the two-phase scheme, the first part of the two-phase approach is the iVMP, where virtual machines (VMs) can be built, changed, or destroyed at runtime. While the second phase focuses on raising the standard of solutions produced by the iVMP, several studies have been done in different literature to solve the VMP problem. However, the methods used tend to be over-forecast and have long runs of a linear trend. This affects the prediction and produces a less optimal solution. Objective: The following four objective functions are optimized using the proposed Extreme Learning Machine Prediction-Based Triggering Method for Virtual Machine Placement in Cloud Computing Datacenters in Two-Phases, which combines the advantages of both online (dynamic) and static (offline) VMP formulations: the length of the reconfiguration process, the amount of energy used, the way resources are used, and the financial expenses. This study suggests a brand-new strategy for deciding when to start the VMP reconfiguration phase. Results: The Method provides more accuracy to the predicted requests as well as reduces the total economic penalties for service Level Agreement(SLA) violations. An experimental comparison with the existing approach is conducted utilizing 400 cases. Conclusion: The results demonstrated that, in comparison to the benchmark approach, the proposed work obtained a minimum cost function with a 10.5% improvement

QoS and Energy-aware Resource Allocation in Cloud Computing Data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

QoS and Energy-aware Resource Allocation in Cloud Computing Data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

A SEM-STELLA approach for predicting decision-makers’ adoption of cloud computing data center

A SEM-STELLA approach for predicting decision-makers’ adoption of cloud computing data center

Data center energy conservation study utilizing loop heat pipes as a chip-level cooling technique and its industrial application

The thermal management and energy consumption for data centers became an increasing challenge due to a significant increase in their number, size and power density in modern information and digitization age. The electricity using by cooling system contributed the important fraction of energy consumption in datacenters, and it is of great significance to carry out the research of new cooling technologies to improve the cooling efficiency of datacenters. This paper initiatively presents the basic energy conservation principle of chip-level cooling technique based on the loop heat pipe (LHP) for natural cooling throughout a year. An energy-saving theoretical evaluation model based on the LHP chip-level cooling method is developed for assessing the energy utilizing efficiency and energy-saving levels in datacenters. Two kinds of real computer servers using two water-cooling LHPs with flat evaporator are introduced. The evaporator volume of a LHP is 54 × 44 × 12 mm3. The tests show that the servers work safely with the inlet water temperature of 40 °C and flow rate of 0.027 m3/h in the condenser of LHP. Lastly, the applications in two industrial data centers based on the LHP chip-level cooling technique are practiced. The annual cooling-PUE of a cloud-computing datacenter in Hohhot city is 1.053, and the annual PUE of a supercomputer-center in Beijing city is 1.15. Their operation data both show that the energy savings of the cooling system and the whole data center could be up to 81 % and 30 % respectively compared to the traditional CRAH cooling technique, which are in accordance with the predicted results of theoretical evaluation model.

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtualmachine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, unoptimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.

Application of Cloud Computing Technology in Computer Secure Storage

To investigate the use of cloud computing technologies in safe computer storage, firstly, it is proposed to complete the central control function by building a cloud computing data center, collect multiple platforms and network safety technologies, and then connect computers in unlike sites to confirm computer information security. Then, based on the implementation advantages of cloud computing technique in computer network safe storage, specific applications are analyzed. Finally, a cloud computing secure modeling and analysis idea based on multiqueue and multiserver is proposed. The proposed cloud security approach ensures that both data and applications are easily accessible to authorized users. One always has a consistent way to access your cloud data and applications, allowing you to address any potential security issues as soon as they arise. It has greatly improved computer security storage convenience while also greatly improving computer network storage security. After verification, with the cloud computing technology platform to carry out relevant businesses at any time, the operation effectiveness has been meaningfully enhanced by 80%. At the same time, it promotes the construction of information sharing and gives full performance to the benefits of hardware, accelerates the process of resource integration, and provides information support for the formulation of enterprise strategic plans. Combined with the actual situation, the current study discusses the development and application direction of cloud computing, so as to add new impetus to the economic growth of enterprises.

Energy efficiency in cloud computing data centers: a survey on software technologies.

Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most significant technology in IT sector. From the notion of cloud computing to its energy efficiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software-based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy efficiency in containers and problem-solving approaches used for reducing power consumption in data centers. Further, the paper also gives details about the impact of data centers on environment that includes the e-waste and the various standards opted by different countries for giving rating to the data centers. This article goes beyond just demonstrating new green cloud computing possibilities. Instead, it focuses the attention and resources of academia and society on a critical issue: long-term technological advancement. The article covers the new technologies that can be applied at the individual software level that includes techniques applied at virtualization level, operating system level and application level. It clearly defines different measures at each level to reduce the energy consumption that clearly adds value to the current environmental problem of pollution reduction. This article also addresses the difficulties, concerns, and needs that cloud data centres and cloud organisations must grasp, as well as some of the factors and case studies that influence green cloud usage.

Performance Enhancement of Cloud Datacenters Through Replicated Database Server

Cloud computing has risen as a new computing paradigm providing computing, resources for networking, and storage as a service across the network. Data replication is a phenomenon which brings the available and reliable data (e.g., maybe the databases) nearer to the consumers (e.g., cloud applications) to overcome the bottleneck and is becoming a suitable solution. In this paper, the authors study the performance characteristics of a replicated database in cloud computing data centres which improves QoS by reducing communication delays. They formulate a theoretical queueing model of the replicated system by considering the arrival process as Poisson distribution for both types of client request, such as read and write applications. They solve the proposed model with the help of the recursive method, and the relevant performance matrices are derived. The evaluated results from both the mathematical model and extensive simulations help to study the unveil performance and guide the cloud providers for modelling future data replication solutions.

Retraction Note to: A novel scheduling approach to improve the energy efficiency in cloud computing data centers

Retraction Note to: A novel scheduling approach to improve the energy efficiency in cloud computing data centers

Energy Aware Resource Provisioning for Multi-Criteria Scheduling in Cloud Computing

In cloud, scheduling task requests on heterogeneous resources has proved to be a crucial research problem, particularly in terms of performance and energy consumption. The main challenge for cloud providers is to find the best tradeoff between two contradictory aims: improving user satisfaction and reducing energy consumption. Initially, to provide greater user satisfaction, the cloud computing data centers need to allocate resources according to the different users’ Quality of Service (QoS) preferences in a dynamic manner. Furthermore, to manage energy consumption on the cloud system during the execution of applications, the energy scaling decision should be made through a proper approach. In this paper, a multi-criteria scheduling algorithm based on fuzzy AHP-TOPSIS hybrid methodology is proposed to meet these requirements, which first deploys FAHP to rank and then selects the best cloud solutions according to the user’s requirements using FTOPSIS. It is then followed by effective execution of the user’s requests on available resources. Besides, an energy aware cloud resource provisioning approach based on the integration of horizontal scaling and dynamic voltage frequency scaling (DVFS) techniques is proposed to meet QoS requirements of all task requests and to manage energy consumption. The evaluation of this paper is run through different job requests while being subject to various constraints. The simulation results show that the proposed approach decreases the energy consumption and response time while enhancing both the resource utilization and user satisfaction compared with the SHARP and the BULLET algorithms in all conditions.