Resources In Cloud Data Centers Research Articles

Fog Computing in Next Generation Networks: A Review

Cloud, Edge, and Fog computing has recently attracted significant attention in both industry and academia. However, finding their definition in computing paradigms and the correlation between them is difficult. In order to support modern computing systems, the cloud, edge devices, and fog computing offer high-quality services, lower latency, multi-tenancy, mobility support, and many other features. Fog/edge computing is an emerging computing paradigm that uses decentralized resources at the edge of a network to process data closer to user devices, like smartphones and tablets, as an alternative to using remote and centralized cloud data center resources. Fog networking or fogging is one of the best used models recently. By addressing this issue, this work serves as a valuable resource for those who will come after. Initially, we present an overview modern computing models and associated areas of interest research. After that, we discuss each paradigm. After that, we go into great detail about fog computing, highlighting its exceptional function as the link between edge, cloud, and IoT computing. Finally, we briefly outline open research questions and future directions in Edge, Fog, Cloud, and IoT computing.

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures.

A resource scheduling method for cloud data centers based on thermal management

With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy consumption, excessive VM consolidation may lead to local hot spots and increase the risk of equipment failure. One possible solution to this problem is to utilize thermal-aware scheduling, but existing approaches have trouble realizing the balance between SLA and energy consumption. This paper proposes a novel method to manage cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hot spots from a global perspective. Its VM consolidation process includes four phases where the VMs scheduler uses an improved ant colony algorithm (UACO) to find appropriate target hosts for VMs based on server temperature and utilization status obtained in real-time. Experimental results show that the TM-VMC approach can proactively avoid data center hot spots and significantly reduce energy consumption while maintaining low Service Level Agreement (SLA) violation rates compared to existing mainstream VM consolidation algorithms with workloads from real-world data centers.

HA-D3QN: Embedding virtual private cloud in cloud data centers with heuristic assisted deep reinforcement learning

The virtual network embedding problem is embedding virtual networks (VNs) in a substrate network so that revenue or accept ratio is maximized. Previous study usually assumes disclosed communication demand among the virtual nodes in a VN, mismatching real-world cloud computing scenarios. In this paper, we propose a new VN abstraction based on the widely used Virtual Private Cloud model, where internal communication demand is unknown to cloud providers. In contrast with the majority of existing research, we allow the co-location of the virtual nodes belonging to the same VN, and introduce the concept of switching capacity for practical resource reservation. We categorize the substrate resources in cloud data centers into additive and non-additive for the first time, and devise our algorithms accordingly. After formulating the problem, we propose a solution framework named HA-D3QN (Heuristic Assisted Dueling Double Deep Q Network). Essentially, HA-D3QN selects the best responses to different system states by combining the D3QN deep reinforcement learning structure and the candidate actions, which are generated by our proposed heuristic algorithms for addressing the exponentially large action space. Finally, we conduct extensive simulation experiments, the results of which verify the effectiveness of our approach.

Complementary in Time and Space: Optimization on Cost and Performance with Multiple Resources Usage by Server Consolidation in Cloud Data Center

The recent COVID-19 pandemic has accelerated the use of cloud computing. The surge in the number of users presents cloud service providers with severe challenges in managing computing resources. Guaranteeing the QoS of multiple users while reducing the operating cost of the cloud data center (CDC) is a major problem that needs to be solved urgently. To solve this problem, this paper establishes a cost model based on multiple computing resources in CDC, which comprehensively considers the hosts’ energy cost, virtual machine (VM) migration cost, and SLAV penalty cost. To minimize this cost, we design the following solution. We employ a convolutional autoencoder-based filter to preprocess the VM historical workload and use an attention-based RNN method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migration before the host enters an overloaded state to reduce the occurrence of SLAV. A heuristic algorithm based on the complementary use of multiple resources in space and time is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing methods, our proposed method reduces the energy consumption of the hosts and SLAV and reduces the total cost by 26.1~39.3%.

Live virtual machine migration: A survey, research challenges, and future directions

In recent years, cloud computing has emerged as a promising paradigm providing various resources in the Cloud Data Center (CDC), including computations, storage, and even platform as a service. The virtualization technology plays a pivotal role in CDC resources management and provides an enchanting feature of virtual machine (VM) migration which provides several benefits in terms of VM scheduling, fault tolerance, load balancing, energy efficiency, power management, and security. To achieve efficient VM migration, a plethora of VM migration schemes have been proposed in the literature aiming to serve the quality of service-driven user requirements. This paper surveys the most recent and state-of-the-art Artificial Intelligence (AI) based and conventional load balancing, energy-aware, SLA aware, and network-aware live VM migration schemes. Through an extensive literature review, this paper investigates the most critical aspects of conventional and AI-driven live VM migration schemes. Finally, a few open research challenges that require further consideration from the research community are highlighted.

Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment

IoT applications have become a pillar for enhancing the quality of life. However, the increasing amount of data generated by IoT devices places pressure on the resources of traditional cloud data centers. This prevents cloud data centers from fulfilling the requirements of IoT applications, particularly delay-sensitive applications. Fog computing is a relatively recent computing paradigm that extends cloud resources to the edge of the network. However, task scheduling in this computing paradigm is still a challenge. In this study, a semidynamic real-time task scheduling algorithm is proposed for bag-of-tasks applications in the cloud–fog environment. The proposed scheduling algorithm formulates task scheduling as a permutation-based optimization problem. A modified version of the genetic algorithm is used to provide different permutations for arrived tasks at each scheduling round. Then, the tasks are assigned, in the order defined by the best permutation, to a virtual machine, which has sufficient resources and achieves the minimum expected execution time. A conducted optimality study reveals that the proposed algorithm has a comparative performance with respect to the optimal solution. Additionally, the proposed algorithm is compared with first fit, best fit, the genetic algorithm, and the bees life algorithm in terms of makespan, total execution time, failure rate, average delay time, and elapsed run time. The experimental results show the superiority of the proposed algorithm over the other algorithms. Moreover, the proposed algorithm achieves a good balance between the makespan and the total execution cost and minimizes the task failure rate compared to the other algorithms.Graphical

A high-applicability heterogeneous cloud data centers resource management algorithm based on trusted virtual machine migration

With the continuous development and maturity of cloud computing technology, the scale and number of cloud data center (CDC) are also expanding. This increasingly draws attention to the problem of high energy consumption in CDCs. Dynamic virtual machine (VM) consolidation is a promising approach for reducing energy consumption. VM migration, as a VM consolidation technology, can effectively improve the utilization of physical machine (PM) and optimize the scheduling process of CDCs. However, most VM integration algorithms, in existing research, are aimed at improving the utilization of PMs. Excessive utilization of PMs may increase the competition for shared resources among the VMs running on them. As a result, the performance of these VMs deteriorates, and the execution time of cloud tasks is increased or even interrupted. This study systematically analyzes the overall architecture of CDCs. Subsequently, migration rules are established for the one-dimensional and multidimensional trusted VMs. A high- applicability heterogeneous CDC resource management algorithm based on trusted VM migration (HTVM2) is then proposed. The proposed algorithm not only solves the one-dimensional VM migration problem of homogeneous and heterogeneous CDCs but also those of multi-dimensional VMs. This improves the success rate of VM migration, reduces the energy consumption of the CDC, and improves load balancing while ensuring VM performance. Finally, the algorithm was compared with the other three algorithms outperforming them all, as demonstrated by experimental results.

Спеціалізоване програмне забезпечення для моделювання динамічної консолідації віртуальних машин

For many cloud service providers, virtual machines remain the basic technology for computing virtualization. Virtual machines are used both to host application software and to implement container virtualization. Widespread use of virtual machines develops specialized software to determine the impact of model parameters on the quality of the consolidation process, which will prevent experimental research in production to evaluate new cloud data center resource management strategies. In recent years, there were many approaches in literature that offers various sets of software tools and frameworks for modeling data center processes, providing a platform and the necessary building blocks to optimize the process of consolidation of virtual machines. Models and software tools for modeling data center resource management processes are usually not exhaustive and solve a specific problem or management task. The specialized simulation software presented in the paper allows to investigate different control modes of virtual machines dynamic consolidation, provides a wide range of logging and debugging information using text and MS Excel files, such as performance indicators and workload diagrams, and allows to determine the optimal model parameters for various modes of data center operation, minimizing the number of active physical servers and reducing the number of SLA violations.

Adaptive DRL-based Virtual Machine Consolidation in Energy-Efficient Cloud Data Center

The dramatic increasing of data and demands for computing capabilities may result in excessive use of resources in cloud data centers, which not only causes the raising of energy consumption, but also leads to the violation of Service Level Agreement (SLA). Dynamic consolidation of virtual machines (VMs) is proven to be an efficient way to tackle this issue. In this paper, we present an Adaptive Deep Reinforcement Learning (DRL)-based Virtual Machine Consolidation (ADVMC) framework for energy-efficient cloud data centers. ADVMC has two phases. In the first phase, Influence Coefficient is introduced to measure the impact of a VM on producing host overload, and a dynamic Influence Coefficient-based VM selection algorithm (ICVMS) is proposed to preferentially choose those VMs with the greatest impact for migration in order to remove the excessive workloads of the overloaded host quickly and accurately. In the second phase, a Prediction Aware DRL-based VM placement method (PADRL) is further proposed to automatically find suitable hosts for VMs to be migrated, in which a state prediction network is designed based on LSTM to provide DRL-based model more reasonable environment states so as to accelerate the convergence of DRL. Simulation experiments on the real-world workload provided by Google Cluster Trace have shown that our ADVMC approach can largely cut down system energy consumption and reduce SLA violation of users as compared to many other VM consolidation policies.

Performance Analysis of a Dynamic Virtual Machine Management Method Based on the Power-Aware Integral Estimation

The latest cloud resource management research has revealed that virtual machine (VM) consolidation allows for effectively managing the physical resources of cloud data centers. However, a tremendous waste of power and physical resources has been pointed as one of the research challenges related to the development of new methods for VM management in a cloud data center in order to deliver a wide range of IT services to clients effectively. This paper investigates a problem of power-aware VM consolidation under dynamic workloads, uncertainty, and a changing number of VMs. For this purpose, the authors propose a dynamic VM management method based on a beam search that takes into account four types of resources (CPU, memory, network throughput, and storage throughput) and six quality metrics. Optimal beam search algorithm parameters for the defined problem are determined using a new power-aware integral estimation method. The SLA violation minimization allows significant improvement of SLA quality metrics, accompanied by the decreased number of VM migrations and slight deterioration in the power consumption. The proposed method is evaluated using common widespread hardware configurations and Bitbrains workload traces. The experiments show that the proposed approach can ensure the efficient use of cloud resources in terms of SLA violation and the number of VM migrations.

Resource utilization prediction technique in cloud using knowledge based ensemble random forest with LSTM model

Future computation of cloud datacenter resource usage is a provoking task due to dynamic and Business Critic workloads. Accurate prediction of cloud resource utilization through historical observation facilitates, effectively aligning the task with resources, estimating the capacity of a cloud server, applying intensive auto-scaling and controlling resource usage. As imprecise prediction of resources leads to either low or high provisioning of resources in the cloud. This paper focuses on solving this problem in a more proactive way. Most of the existing prediction models are based on a mono pattern of workload which is not suitable for handling peculiar workloads. The researchers address this problem by making use of a contemporary model to dynamically analyze the CPU utilization, so as to precisely estimate data center CPU utilization. The proposed design makes use of an Ensemble Random Forest-Long Short Term Memory based deep architectural models for resource estimation. This design preprocesses and trains data based on historical observation. The approach is analyzed by using a real cloud data set. The empirical interpretation depicts that the proposed design outperforms the previous approaches as it bears 30%–60% enhanced accuracy in resource utilization.

Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.

Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers

Cloud computing has been considered a core model of elastic on-demand resource allocation using a pay-as-you go model. One of the big challenges of this environment is to provide high quality service (QoS) through efficient and stringent management of cloud data center resources. With the increasing demand for cloud based services, the traffic volume inside cloud data centers (DC) has been increased exponentially. Accordingly, and to provide high QoS, a proper scheduling mechanism has to be followed by the cloud service provider. Furthermore, accurate scheduling is necessary for advancing the problem of energy consumption and resource utilization. In this paper, we propose an optimal resource allocation and consolidation virtual machine (VM) placement model for multi-tier applications in modern large cloud DCs. The proposed model targets to optimize the DCs’ energy and communication cost that influence the overall cloud performance through Software Defined Networking (SDN) control features. To solve the formulated multi-objective optimization problem, a novel adaptive genetic algorithm is proposed. The experimental results validate the efficacy of the proposed model through extensive simulations using synthetic and real workload traces. These results show that the proposed model jointly optimizes cloud QoS as well as energy consumption.

Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud data centers. In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however, the proposed RL approach learns optimal behavior in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we provide a comparative analysis of popular RL algorithms including SARSA and Q-learning. Our empirical results demonstrate how our approach improves energy efficiency by 25% while also reducing service violations by 63% over the popular Power-Aware heuristic algorithm.

A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain

Collaborative learning with multiple edge devices to build group intelligence is a new trend. Edge artificial intelligence (AI) computing often makes full use of various available data and resources in terminal devices, edge servers, and cloud data centers to achieve collaborative deci-sion-making. However, in order to achieve the goal, it should also guarantee the security of data storage, transmission, and management. Therefore, how to build a distributed trust system for cooperative learning in edge computing is a complex and challenging engineering problem. Block-chain can help realize collaborative learning in a distributed way without the need for third-party audit. Also, the consensus protocol plays a key technology role. In this article, a trusted consensus scheme for multi-party collaborative learning of edge AI is proposed. To improve system security, reputation-based rights are used to support fast removal of abnormal nodes. With the method of reputation rewards and punishments, the reputations of the nodes participating in the consensus process are scored and recorded on the chain according to their behavior. Nodes who commit malicious actions will be punished by reducing their reputation values. Nodes are rated according to their reputation value, and those with higher reputation rates are acknowledged with more credits and rights in consensus. Since the reputation module is loadable on X-BFT protocols, it is easy to enforce in real applications. The experimental results show that the probability of the attacker being chosen as the leader of XR-BFT is 87.5 percent lower than that of the X-BFT protocols, thus enabling our protocol to achieve trust collaborative learning in edge AI computing with more safety and efficiency.

A Cartesian Genetic Programming Based Parallel Neuroevolutionary Model for Cloud Server’s CPU Usage Prediction

Cloud computing use is exponentially increasing with the advent of industrial revolution 4.0 technologies such as the Internet of Things, artificial intelligence, and digital transformations. These technologies require cloud data centers to process massive volumes of workloads. As a result, the data centers consume gigantic amounts of electrical energy, and a large portion of data center electrical energy comes from fossil fuels. It causes greenhouse gas emissions and thus ensuing in global warming. An adaptive resource utilization mechanism of cloud data center resources is vital to get by with this huge problem. The adaptive system will estimate the resource utilization and then adjust the resources accordingly. Cloud resource utilization estimation is a two-fold challenging task. First, the cloud workloads are sundry, and second, clients’ requests are uneven. In the literature, several machine learning models have estimated cloud resources, of which artificial neural networks (ANNs) have shown better performance. Conventional ANNs have a fixed topology and allow only to train their weights either by back-propagation or neuroevolution such as a genetic algorithm. In this paper, we propose Cartesian genetic programming (CGP) neural network (CGPNN). The CGPNN enhances the performance of conventional ANN by allowing training of both its parameters and topology, and it uses a built-in sliding window. We have trained CGPNN with parallel neuroevolution that searches for global optimum through numerous directions. The resource utilization traces of the Bitbrains data center is used for validation of the proposed CGPNN and compared results with machine learning models from the literature on the same data set. The proposed method has outstripped the machine learning models from the literature and resulted in 97% prediction accuracy.

Memory-aware resource management algorithm for low-energy cloud data centers

The continuous advancement of cloud computing technology has driven the vigorous development of cloud data centers. This manifests itself not only in increasing numbers, but also in rapid expansion scale. At the same time, the problems of high energy consumption, high cost and high carbon emissions began to stand out. These factors have become a bottleneck restricting the further development of cloud computing technology. As a result of the application of virtualization technology, mature cloud service providers use virtual machines instead of physical machines to provide users with computing, storage and other services. Therefore, the scheduling algorithm of cloud tasks and virtual machines has been widely studied by academia as a core problem. However, most of the current cloud data center resource management algorithms take CPU utilization as the main consideration, and increase the CPU utilization through virtual machine integration to reduce the energy consumption of cloud data centers. However, these algorithms will cause waste and low utilization of other resources in the cloud data center due to excessive consideration of CPU utilization. This paper systematically analyzes the mapping relationship between cloud tasks, virtual machines and physical machines. At the same time, the performance of cloud tasks, virtual machines, and physical machines is modeled in the cloud data center. By comprehensively considering the CPU and memory characteristics of cloud tasks, the memory priority cloud task mapping rule is established. Based on the rule, the memory-aware resource management algorithm for low-energy cloud data centers (MALE) is proposed. The algorithm maps cloud tasks and deploys virtual machines according to the memory requirements of the cloud tasks, thereby achieving the goal of simultaneously reducing the total fee of cloud users and the energy consumption of cloud data centers. Finally, the algorithm is compared with the other three algorithms. The experimental simulation results show that the effect of the proposed algorithm in this paper is significantly better than the comparison algorithm for the total fee of cloud users and the energy consumption of the cloud data center.

CPU-Memory Aware VM Consolidation for Cloud Data Centers

The unbalanced usage of resources in cloud data centers cause an enormous amount of power consumption. The Virtual Machine (VM) consolidation shuts the underutilized hosts and makes the overloaded hosts as normally loaded hosts by selecting appropriate VMs from the hosts and migrates them to other hosts in such a way to reduce the energy consumption and to improve physical resource utilization. Efficient method is needed for VM selection and destination hosts selection (VM placement). In this paper, a CPU-Memory aware VM placement algorithm is proposed for selecting suitable destination host for migration. The VMs are selected using Fuzzy Soft Set (FSS) method VM selection algorithm. The proposed placement algorithm considers both CPU, Memory, and combination of CPU-Memory utilization of VMs on the source host. The proposed method is experimentally compared with several existing selection and placement algorithms and the results show that the proposed consolidation method performs better than existing algorithms in terms of energy efficiency, energy consumption, SLA violation rate, and number of VM migrations.