OpenStack Cloud Research Articles

Performance Evaluation of OpenStack Cloud Integrated with SDN Controller

The Internet is undergoing a revolution thanks to the development of a very popular recent technology trend - “Cloud Computing”. Large, small, and medium-sized businesses organizations, as well as individuals increasingly prefer to use cloud computing services due to their many advantages. Furthermore, the widespread implementation of cloud computing in large enterprises is an indicator of the technological advancement of information technology companies. It helps in the efficient allocation and utilization of infrastructure, power, and resources. In the meantime, centralized control provided by software-defined networking (SDN) allows for flexibility in operations and management. For that reason, there has been an adequate amount of research and application of combining these two objects over the years. In this study, we will evaluate network performance between virtual machines in terms of bandwidth, latency, and system performance interms of CPU, RAM, etc. in OpenStack with and without integration of SDN Controller - Tungsten Fabric (TF).

A novel multi-step-ahead approach for cloud server aging prediction based on hybrid deep learning model

Predicting software aging in cloud servers is essential for preventing cloud system breakdown and unexpected failures. However, the aging time series data in cloud servers often exhibits non-linearity and stochastic fluctuations, posing challenges for accurate prediction. To address this issue, this study proposes a novel multi-step ahead approach for cloud server aging prediction. Firstly, the original aging time series data is denoised using discrete wavelet transformation (DWT) and then reconstructed into multi-dimensional data by phase state reconstruction (PSR) to capture potential aging patterns. Subsequently, a deep long short-term memory network (DLSTM) is developed to predict the aging trend. Additionally, a stacked auto-encoder LSTM (SAE-LSTM) is designed to optimize the initial weight matrix of DLSTM to enhance prediction abilities. Finally, multi-step prediction experiments are conducted on our time series datasets from OpenStack cloud servers and a public dataset from Alibaba Cluster Trace Program. The comparative analysis of the proposed method with five baseline methods confirms its superiority in terms of prediction accuracy. For response time and free memory test sets, the range of root mean square error (RMSE) values are produced by the proposed method distributed in [0.1688–0.1931] and [139.05–146.05] respectively, with respect to the multi-step ahead prediction horizon from 1 up to 10. These results demonstrate that the proposed method can tackle the limitations of one-step ahead prediction, effectively extracting relevant aging features, and achieving low prediction accuracy. The study provides valuable insights into revealing future aging trends in cloud servers and offers a reference for implementing preventive maintenance and prognostics in cloud servers.

Rational load balancing in collaborated cloud computing environments *

Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.

Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

<div align="center"><span>Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency.</span></div>

Comparative Study of Orchestration using gRPC API and REST API in Server Creation Time: An Openstack Case

Cloud computing is the quick, simple, and economical distribution of computing services. To offer quality services in cloud infrastructure, the NFV Management and Orchestration (MANO) function is a critical task for managing both infrastructure resources and network functions. Since the demand for cloud services is rapidly increasing, cloud service providers are constantly trying to reduce operational costs. Hence the elements of MANO functions have gained critical importance in cloud infrastructure. To use MANO efficiently, there are hypervisors (OpenStack) that act as a middle layer between hardware and software that are used to provide Infrastructure-as-a-Service solutions through a set of interrelated services. These services are managed by an application programming interface (API). The existing orchestration method uses Representational state transfer (REST) API for communicating with core components of a hypervisor. These API have been integral part of creating and deploying a server. To improve the performance, this paper proposes a novel approach for orchestration based on the opensource remote procedure call framework, known as gRPC. To analyse the proposed solution, both REST and gRPC-based orchestration methods are implemented for creating and launching servers using OpenStack cloud computing platform. The server creation time is obtained for different scenarios and SFC use cases. The results show that by using gRPC, the performance in terms of server creation time of an orchestration is improved by up to 27% compared to the traditional REST-based orchestration.

Enabling Attribute-based Access Control for OpenStack Cloud Resources through Smart Contracts

The increasing complexity of cloud computing has prompted a greater emphasis on protecting the privacy, integrity, and security of data stored and processed in the cloud. Data privacy is safeguarded through access control, but existing models such as Role Based Access Control (RBAC) and Attribute Based Access Control (ABAC) rely on a centralized server. If this server is compromised, it poses significant risks to data security. To address this issue, there is a need for Distributed ABAC (DABAC) system for OpenStack services based on blockchain. The unique features of blockchain enables access control systems which ensures data integrity and privacy. Additionally, blockchain offers a level of transparency for both the resource owner and a user. In this work, we propose a smart contract based ABAC system. We implemented the proposed work using an Ethereum blockchain and OpenStack cloud. Furthermore, we evaluated two consensus algorithms for scalability analysis of DABC mechanism. The results demonstrate that DABAC performs better than ABAC in ensuring fine-grade access control with proof of stake consensus algorithm providing better scalability.

Research on the Innovative Model of Artificial Intelligence Technology Facilitating Sports Management in Colleges and Universities

Abstract The challenge of managing information in collegiate physical education has progressively escalated, thereby amplifying the complexities inherent in the sports management domains at higher education institutions. The advent of artificial intelligence technology heralds a promising avenue for the evolution of sports management frameworks within these settings. This study delineates the development of a specialized cloud platform tailored for the management of collegiate sports, encompassing aspects such as teaching and research management, quality assurance in education, general teaching affairs, practical instruction, and support for auxiliary teaching processes. This platform is engineered using the OpenStack cloud architecture, and it meticulously explores advanced scheduling algorithms for virtual machines to enhance platform efficiency. Systematic testing of the platform reveals critical performance metrics; notably, prior to optimization, the CPU utilization of the educational system spikes to 100% with a Time to First Byte (TTFB) reaching indefinite timeouts when concurrent accesses surpass 9, 000, leading to a system shutdown and cessation of service availability. Post-optimization, the platform robustly sustains business accessibility even under similar stress conditions. Furthermore, the empirical usage data of the sports management systems indicate predominant utilization for fulfilling work-related needs (0.796), psychological requirements (0.74), and the pursuit of enhanced operational efficiency (0.732).

Decentralized News-Retrieval Architecture Using Blockchain Technology

Trust is a critical element when it comes to news articles, and an important problem is how to ensure trust in the published information on news websites. First, this paper describes the inner workings of a proposed news-retrieval and aggregation architecture employed by a blockchain-based solution for fighting disinformation; this includes a comparison between existing information retrieval solutions. The decentralized nature of the solution is achieved by separating the crawling (i.e., extracting the web page links) from the scraping (i.e., extracting the article information) and having third-party actors extract the data. A majority-rule mechanism is used to determine the correctness of the information, and the blockchain network is used for traceability. Second, the steps needed to deploy the distributed components in a cloud environment seamlessly are discussed in detail, with a special focus on the open-source OpenStack cloud solution. Lastly, novel methods for achieving a truly decentralized architecture based on community input and blockchain technology are presented, thus ensuring maximum trust and transparency in the system. The results obtained by testing the proposed news-retrieval system are presented, and the optimizations that can be made are discussed based on the crawling and scraping test results.

High Throughput VMs Placement With Constrained Communication Overhead and Provable Guarantees

Placement of VMs in the cloud is one of the most fundamental problems in systems research. Traditionally, placement algorithms assume that the schedulers have complete information about the currently available resources at each host. However, this assumption is in many cases unrealistic, as gathering fresh status information from each of the thousands of hosts in a large data center incurs excessive communication overhead, which results in long queueing delays. Efforts to resolve this problem by employing several parallel schedulers typically exhibit collisions when several schedulers are simultaneously trying to place VMs on the same host. Our work analyzes the performance of various placement algorithms and provides empirical evidence that using multiple randomized schedulers obtains high throughput, while significantly decreasing both the communication overhead, and the number of collisions between schedulers. We, therefore, introduce Adaptive Partial State Random (APSR) – an efficient parallel random resource management algorithm that samples only from a small number of hosts and dynamically adjusts the degree of parallelism to provide provable guarantees on the probability of collisions between distinct schedulers. We formally analyze APSR, evaluate it on real workloads, and integrate it into the popular OpenStack cloud management platform. Our evaluation shows that APSR matches the throughput provided by other parallel schedulers, while achieving up to 13x lower decline ratio and a reduction of over 85% in communication overheads.

Bespoke Virtual Machine Orchestrator: An Approach for Constructing and Reconfiguring Bespoke Virtual Machine in Private Cloud Environment

A cloud-computing company or user must create a virtual machine to build and operate a cloud environment. With the growth of cloud computing, it is necessary to build virtual machines that reflect the needs of both companies and users. In this study, we propose a bespoke virtual machine orchestrator (BVMO) as a method for constructing a virtual machine. The BVMO builds resource volumes as core assets to meet user requirements and builds virtual machines by reusing and combining these resource volumes. This can increase the reusability and flexibility of virtual-machine construction. A case study was conducted to build a virtual machine by applying the proposed BVMO to an actual OpenStack cloud platform, and it was confirmed that the construction time of the virtual machine was reduced compared with that of the existing method.

Cost‐ and performance‐aware resource selection for parallel software on heterogeneous cloud

SummaryCloud providers offer flexible infrastructures and on‐demand services, including the capability to deploy low cost virtual resources of many different types. However, the diversity of cloud resources followed by the important trade‐off between cost and performance makes the resource selection a challenging task for users in the case of parallel communication‐intensive software. The paper presents cost‐ and performance‐aware resource selection for parallel discrete element method (DEM) software as a service (SaaS) on heterogeneous OpenStack cloud. The developed resource selection uses preliminary application‐specific benchmarks of size smaller than targeted problems and the performance prediction based on speedup of parallel computations to obtain Pareto optimal solutions and to select the best configuration of containers from user's perspective. Hybrid parallelization of DEM software is developed by using OpenCL for shared‐memory multi‐core architectures and MPI for internode communications on distributed‐memory computer clusters. Round up and proportional pricing schemes are examined and compared from a user's perspective. Lower cost of computations obtained by using the proportional pricing scheme is always preferable for users. However, the difference approaches 1.0% of the cost calculated by using proportional pricing scheme, when long lasting computations are performed. The prediction tends to underestimate the execution time of DEM SaaS, but its accuracy is sufficient to obtain the same Pareto optimal solutions by using measured and predicted execution times. Pareto front and linear scalarization propose to select configurations of containers capable of exploiting higher memory bandwidth, which is specific to memory bandwidth bound DEM computations.

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization.

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.

Adaptation of Parallel SaaS to Heterogeneous Co-Located Cloud Resources

Cloud computing has received increasing attention due to its promise of delivering on-demand, scalable, and virtually unlimited resources. However, heterogeneity or co-location of virtual cloud resources can cause severe degradation of the efficiency of parallel computations because of a priori unknown application-specific performance metrics, load imbalance, and limitations of memory bandwidth. This paper presents the runtime adaptation of parallel discrete element method (DEM) Software as a Service (SaaS) to heterogeneous or co-located resources of the OpenStack cloud. The computational workload is adapted by using weighted repartitioning and runtime measured performance of parallel computations on Docker containers. The high improvement in performance up to 48.7% of the execution time is achieved, applying the runtime adapted repartitioning when the load imbalance is high enough. The low load imbalance leads to the close values of computational load, when small variations in the system load and performance can cause oscillations in subsets of particles. Memory stress tests cause heterogeneity of non-isolated containers, which reduces the performance of memory bandwidth bound DEM SaaS on the co-located resources. The runtime adapted repartitioning handles the constant and periodically variable performance of non-isolated containers and decreases the total execution time of DEM SaaS.

Automating the Deployment of Cyber Range with OpenStack

Abstract Cyber Range is an experimental platform based on virtualization technology to construct a controlled simulation environment, providing a real-world simulation environment for cybersecurity personnel to conduct various practical exercises. The problem is that generating a virtual environment satisfying the requirements is labor-intensive and time-consuming. To resolve the above problem, this paper proposes a system to automate the deployment of a cyber range. In our method, the first step is to collect virtual machines (VMs) related to cybersecurity and extract relevant features. Then, machine learning is used to classify VMs to reduce the cost of manual VMs selection. Lastly, leveraging the popular OpenStack cloud platform as the deployment platform enhances the applicability of the cyber range. When it comes time to deploy a virtual environment, the instructor only needs to provide some brief description information of a virtual environment. Then, the system will automatically parse the description file to complete the automated deployment of the virtual environment. This system has been successfully applied to the cyber range of Sichuan University for daily teaching tasks.

OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure

Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures.

Run-time failure detection via non-intrusive event analysis in a large-scale cloud computing platform

Cloud computing systems fail in complex and unforeseen ways due to unexpected combinations of events and interactions among hardware and software components. These failures are especially problematic when they are silent, i.e., not accompanied by any explicit failure notification, hindering the timely detection and recovery.In this work, we propose an approach to run-time failure detection tailored for monitoring multi-tenant and concurrent cloud computing systems. The approach uses a non-intrusive form of event tracing, without manual changes to the system’s internals to propagate session identifiers (IDs), and builds a set of lightweight monitoring rules from fault-free executions. We evaluated the effectiveness of the approach in detecting failures in the context of the OpenStack cloud computing platform, a complex and “off-the-shelf” distributed system, by executing a campaign of fault injection experiments in a multi-tenant scenario. Our experiments show that the approach detects the failure with an F1 score (0.85) and accuracy (0.77) higher than the ones provided by the OpenStack failure logging mechanisms (0.53 and 0.50) and two non-session-aware run-time verification approaches (both lower than 0.15). Moreover, the approach significantly decreases the average time to detect failures at run-time (∼114 seconds) compared to the OpenStack logging mechanisms.

Strong Temporal Isolation Among Containers in OpenStack for NFV Services

In this paper, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.

Comparative Analysis of Weighted Ensemble and Majority Voting Algorithms for Intrusion Detection in OpenStack Cloud Environments

Comparative Analysis of Weighted Ensemble and Majority Voting Algorithms for Intrusion Detection in OpenStack Cloud Environments

The Effects of High-Performance Cloud System for Network Function Virtualization

Since ETSI introduced the architectural framework of network function virtualization (NFV), telecom operators have paid more attention to the synergy of NFV and cloud computing. With the integration of the NFV cloud platform, telecom operators decouple network functions from the dedicated hardware and run virtualized network functions (VNFs) on the cloud. However, virtualization degrades the performance of VNF, resulting in violating the performance requirements of the telecom industry. Most of the existing works were not conducted in a cloud computing environment, and fewer studies focused on the usage of enhanced platform awareness (EPA) features. Furthermore, few works analyze the performance of the service function chain on a practical cloud. This paper facilitates the OpenStack cloud with different EPA features to investigate the performance effects of VNFs on the cloud. A comprehensive test framework is proposed to evaluate the verification of functionality, performance, and application testing. Empirical results show that the cloud system under test fulfills the requirements of service level agreement in Rally Sanity testcases. The throughput of OVS-DPDK is up to 8.2 times as high as that of OVS in the performance test. Meanwhile, the hardware-assisted solution, SR-IOV, achieves the throughput at near the line rate in the end-to-end scenario. For the application test, the successful call rate for the vIMS service is improved by up to 14% while applying the EPA features on the cloud.

Optimization of Innovation and Entrepreneurship Education and Training System in Colleges and Universities Based on OpenStack Cloud Computing

With economic globalization and rapid development of science and technology, many colleges and universities pay more and more attention to the cultivation of students’ innovative thinking and creativity, and innovation and entrepreneurship education has also become an important part of the education system. Due to the current unevenness of teachers in innovation and entrepreneurship education in colleges and universities, high training cost, and lack of strong atmosphere, this paper optimizes the innovation and entrepreneurship education and training system in colleges and universities through OpenStack cloud computing. This paper optimizes the cloud computing platform according to the OpenStack virtual machine and the multiobjective ant colony improvement algorithm and then designs the innovation and entrepreneurship education and training system. The multiobjective ant colony improvement algorithm uses the way ants find food to find the best information resource route from the traces left by the information trend in the cloud platform. In order to test the effectiveness of these methods, this paper uses the simulation method to test. The results show that, sometimes, the load utilization rate of the innovation and entrepreneurship education and training system in colleges and universities exceeds 80%, which is in line with the expected settings. Through the OpenStack cloud computing platform, it can provide a good innovation and entrepreneurship training environment for more users at low cost and low risk and promote the development of innovation and entrepreneurship education.