IaaS System Research Articles

Intelligent System Optimization of course Network Teaching Based on Distributed Cloud IaaS System

This paper aims to design and implement a general IaaS cloud platform resource pool network management system to meet the management requirements of network resources in private cloud construction, using Nginx as the web server, uWSGI application server and Tornado application server to process users The non-real-time data and real-time data request of . The article analyzes the problems faced by local colleges and universities in the information big data environment by interpreting the connotation of future education and combining the experience of online course construction reform. Learning course-driven" is the foothold. It intelligently analyzes students' mastery of knowledge and teachers' teaching evaluation, and gives teachers important feedback information, so that teachers can adjust teaching strategies in a timely manner, so that students can master knowledge at a deeper level.

Performance evaluation of IaaS cloud using Stochastic Neural Network

Cloud computing is an on-demand model that computes shared and dynamic resource availability in a remote or independent location. Cloud computing provides many services online to clients in a pay-as-you-go manner. Nowadays, many organizations use cloud computing techniques with the prime motive that cost can be reduced, and resources are dynamically allocated. Performance evaluation and measurement approaches for cloud computing help the cloud services consumer to evaluate their cloud system based on performance attributes. Although the researchers have proposed many techniques and approaches in this direction in past decades, none of them has attained widespread industrial benefit. This paper proposes a novel quality evaluation methodology named Stochastic Neural Net (SNN) to evaluate the cloud quality of Infrastructure as a Service (IaaS). This model deeply measures the performance by considering every activity of the IaaS system. Based on their characteristics, these works suggest key QoS factors for individual parts and activities. The individual QoS metric makes the SNN methodology acquire accurate results regarding performance measurement. The performance evaluation result can be used to improve the cloud computing system. The proposed model is compared with other standard models. The experimental comparison shows that the proposed model is more efficient than other standard models.

Layanan Infrastruktur Komputasi Multitenant dengan OpenStack di Lingkungan MaaS

The growth of high-quality computing needs triggers the development of infrastructure that requires huge costs and resources. This research applied OpenStack in Metal as a Service (MaaS) environment to provide multitenant infrastructure services in the form of virtual machines (IaaS). The capacity of this IaaS system can be configured based on the needs of the computing resources (CPUs, memory, storage, and network interfaces). Users within the tenant can request infrastructure services as their needs without requiring human interaction with each service provider.Meningkatnya kebutuhan komputasi berkualitas tinggi mendorong pengembangan infrastruktur server yang membutuhkan biaya dan sumber daya besar. Penelitian ini mengaplikasikan OpenStack dalam lingkungan Metal as a Service (MaaS) untuk menyediakan layanan infrastruktur multitenant berupa mesin-mesin virtual sebagai Infrastructure as a Service (IaaS). Kapasitas sistem layanan IaaS ini dapat dikonfigurasi berdasarkan kebutuhan sumber daya komputasi (CPU, memori, ruang penyimpan, dan antarmuka jaringan). Pengguna dalam tenant dapat meminta layanan infrastruktur secara mandiri dengan kapasitas komputasi sesuai yang dibutuhkan.

<sc>ATOM</sc>: Efficient Tracking, Monitoring, and Orchestration of Cloud Resources

The emergence of Infrastructure as a Service framework brings new opportunities, which also accompanies with new challenges in auto scaling, resource allocation, and security. A fundamental challenge underpinning these problems is the continuous tracking and monitoring of resource usage in the system. In this paper, we present ATOM , an efficient and effective framework to automatically track, monitor, and orchestrate resource usage in an Infrastructure as a Service (IaaS) system that is widely used in cloud infrastructure. We use novel tracking method to continuously track important system usage metrics with low overhead, and develop a Principal Component Analysis (PCA) based approach to continuously monitor and automatically find anomalies based on the approximated tracking results. We show how to dynamically set the tracking threshold based on the detection results, and further, how to adjust tracking algorithm to ensure its optimality under dynamic workloads. Lastly, when potential anomalies are identified, we use introspection tools to perform memory forensics on VMs guided by analyzed results from tracking and monitoring to identify malicious behavior inside a VM. We demonstrate the extensibility of ATOM through virtual machine (VM) clustering. The performance of our framework is evaluated in an open source IaaS system.

Early fault detection in IaaS cloud computing based on fuzzy logic and prediction technique

Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (IaaS) cloud computing is a challenge due to the complexity of service providing. By definition, the availability can be maintained by coupling with the fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them adequately consider the fault detection aspect, which is critical to issue the appropriate recovery actions just in time. In this paper, based on a rigorous analysis on the nature of failures, we would like to introduce a method to early identify the faults occurring in the IaaS system. By engaging fuzzy logic algorithm and prediction technique, the proposed approach can provide better performance in terms of accuracy and reaction rate, which subsequently enhances the system reliability.

MITTS

Memory bandwidth severely limits the scalability and performance of multicore and manycore systems. Application performance can be very sensitive to both the delivered memory bandwidth and latency. In multicore systems, a memory channel is usually shared by multiple cores. Having the ability to precisely provision, schedule, and isolate memory bandwidth and latency on a per-core basis is particularly important when different memory guarantees are needed on a per-customer, per-application, or per-core basis. Infrastructure as a Service (IaaS) Cloud systems, and even general purpose multicores optimized for application throughput or fairness all benefit from the ability to control and schedule memory access on a fine-grain basis. In this paper, we propose MITTS (Memory Inter-arrival Time Traffic Shaping), a simple, distributed hardware mechanism which limits memory traffic at the source (Core or LLC). MITTS shapes memory traffic based on memory request inter-arrival time, enabling fine-grain bandwidth allocation. In an IaaS system, MITTS enables Cloud customers to express their memory distribution needs and pay commensurately. For instance, MITTS enables charging customers that have bursty memory traffic more than customers with uniform memory traffic for the same aggregate bandwidth. Beyond IaaS systems, MITTS can also be used to optimize for throughput or fairness in a general purpose multi-program workload. MITTS uses an online genetic algorithm to configure hardware bins, which can adapt for program phases and variable input sets. We have implemented MITTS in Verilog and have taped-out the design in a 25-core 32nm processor and find that MITTS requires less than 0.9% of core area. We evaluate across SPECint, PARSEC, Apache, and bhm Mail Server workloads, and find that MITTS achieves an average 1.18× performance gain compared to the best static bandwidth allocation, a 2.69× average performance/cost advantage in an IaaS setting, and up to 1.17× better throughput and 1.52× better fairness when compared to conventional memory bandwidth provisioning techniques.

Minimizing Average Startup Latency of VMs by an Optimized VM Templates Caching Mechanism Based on K-Medoids Clustering in an IaaS System with Multi-cluster of Servers

Minimizing Average Startup Latency of VMs by an Optimized VM Templates Caching Mechanism Based on K-Medoids Clustering in an IaaS System with Multi-cluster of Servers

VIRTUAL APPLIANCE CREATION AND OPTIMIZATION IN CLOUD

The large scale IaaS systems could store virtual appliances in several repositories. The deployment time could heavily vary depending on the connection properties of the repository storing the appliance. A virtual appliance is a virtual machine image designed to run on a virtualization platform i.e. Virtual Box, Xen, VMware Workstation. Virtual appliance delivery requires the modification of the underlying IaaS systems. IaaS is the virtual delivery of computing resources in the form of hardware, networking, and storage services This concept will reduce the variance in deployment time by introducing the concept of online active repositories and appliance optimization. To provide efficient delivery time in IaaS and to increase the efficiency of IaaS (Infra structure as a service) system To calculate the delivery time when deployed it in the virtualized platform Combining the both online and manual repositories for calculating the delivery time To construct the appliance in online using various online repositories The constructed appliance is deployed in the virtualized platform (using virtual box). And the appliance is optimized for increasing the efficiency and decreasing the delivery time. The delivery time of the online appliance is compared with the appliance which has been created manually.

Minimizing Average Startup Latency of VMs by Clustering-based Template Caching Scheme in an IaaS System

Nowadays main infrastructure-as-a-service (IaaS) systems have been widely exploiting the template-based VM creation and template caching techniques to reduce the startup latency of user VM and service response time. Because the new VMs created from the templates do not always have the same software system as needed, further reconfigurations, that is, installing the missing software components and removing the undesired ones, must be made before the VMs can be used by users. The reconfiguration time is also an important source of the VM startup latency. However, current template caching solutions based on the traditional caching strategy select some templates which are frequently used recently to cache without considering the reconfiguration time of various user VMs created in IaaS data center. In this paper, we exploit a modified k-means clustering method to optimize the selecting of templates to be cached and theoretically prove the selected ones can minimize the average reconfiguration time and then the average startup latency of all user VMs under the same limits on the number of templates which cache space can accommodate. The correlative simulation experiments also prove the effectiveness of our approach.

Designing and Applying an Education IaaS System based on OpenStack

This paper designs and implements an IaaS (Infrastructure as a Service) system by using OpenStack. To bring deduplication storage of VM images, this paper uses LiveDFS as the file system in image storage backend. And in order to offer stable persistent volumes to VM instances, this paper develops a distributed block-level storage system called ORTHRUS which is integrated into this IaaS system. Additionally, this paper deploys Ganglia monitoring system in the IaaS system to monitor all the physical nodes VM instances. At last, this paper designs and implements a high-performance service, a net disk service and an education management service on this IaaS system.

Virtual Infrastructure Management in Private and Hybrid Clouds

One of the many definitions of is that of an infrastructure-as-a-service (IaaS) system, in which IT infrastructure is deployed in a provider's data center as virtual machines. With IaaS clouds' growing popularity, tools and technologies are emerging that can transform an organization's existing infrastructure into a private or hybrid cloud. OpenNebula is an open source, virtual infrastructure manager that deploys virtualized services on both a local pool of resources and external IaaS clouds. Haizea, a resource lease manager, can act as a scheduling back end for OpenNebula, providing features not found in other cloud software or virtualization-based data center management software.