High Availability Management for Applications Services in the Cloud Container-Based Platform

Since cloud computing has emerged as a commercial reality, guaranteeing high availability (HA) has become a critical requirement. Many kinds of HA mechanisms have been developed with the rapid expansion of cloud computing. However, selection and configuration are difficult in practice. In the development phase, developers must choose HA mechanisms for cloud computing based on their experience. In the use phase, users must choose and configure the HA mechanisms for their virtual machines (VMs) or services deployed on VMs. In this paper, we proposed a model-based HA configuration framework for the cloud. For the developer, we proposed an open HA mechanism library and an automatic configuration process. For users, we intend to ease the selection of HA mechanisms in the ever-changing cloud environment. We are implementing our approach in our model-based cloud platform, JadeCloud.

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

The failure prediction method of virtual machines (VM) guarantees reliability to cloud platforms. However, the uncertainty of VM security state will affect the reliability and task processing capabilities of the entire cloud platform. In this Study, a failure prediction method of VM based on AdaBoost-Hidden Markov Model was proposed to improve the reliability of VMs and overall performance of cloud platforms. This method analyzed the deep relationship between the observation state and the hidden state of the VM through the hidden Markov model, proved the influence of the AdaBoost algorithm on the hidden Markov model (HMM), and realized the prediction of the VM failure state. Results show that the proposed method adapts to the complex dynamic cloud platform environment, can effectively predict the failure state of VMs, and improve the predictive ability of VM security state.

Virtual machine (VM) technologies, especially those offered via Cloud platforms, present new dimensions with respect to performance and cost in executing parallel discrete event simulation (PDES) applications. Due to the introduction of overall cost as a metric, the choice of the highest-end computing configuration is no longer the most economical one. Moreover, runtime dynamics unique to VM platforms introduce new performance characteristics, and the variety of possible VM configurations give rise to a range of choices for hosting a PDES run. Here, an empirical study of these issues is undertaken to guide an understanding of the dynamics, trends and trade-offs in executing PDES on VM/Cloud platforms. Performance results and cost measures are obtained from actual execution of a range of scenarios in two PDES benchmark applications on the Amazon Cloud offerings and on a high-end VM host machine. The data reveals interesting insights into the new VM-PDES dynamics that come into play and also leads to counter-intuitive guidelines with respect to choosing the best and second-best configurations when overall cost of execution is considered. In particular, it is found that choosing the highest-end VM configuration guarantees neither the best runtime nor the least cost. Interestingly, choosing a (suitably scaled) low-end VM configuration provides the least overall cost without adversely affecting the total runtime.

In recent years, the technology of Cloud Computing has been developed rapidly. It makes IT infrastructure available as a service through the network. Using Cloud Computing, users can find a new effective solution for building systems and applications according to their demands. How to make the existing services reliably run on the cloud platform, and satisfy the high scalability and availability requirements becomes a hot topic. In this paper, we propose a Cloud Computing Service Platform based on OpenStack which supports service management, auto-scaling, security control and high availability, etc. To achieve the effective management of resources and ensure the quality of service, we implement the resource monitoring subsystem. It can monitor performance metrics CPU Utilization, Memory Usage and Network I/O of physical and virtual resource like. The persistent data can be used by the resource management subsystem and alarm subsystem.

High availability is the most important and challenging problem for cloud providers. However, virtual machine monitor (VMM), a crucial component of the cloud infrastructure, has to be frequently updated and restarted to add security patches and new features, undermining high availability. There are two existing live update methods to improve the cloud availability: kernel live patching and Virtual Machine (VM) live migration. However, they both have serious drawbacks that impair their usefulness in the large cloud infrastructure: kernel live patching cannot handle complex changes (e.g., changes to persistent data structures); and VM live migration may incur unacceptably long delays when migrating millions of VMs in the whole cloud, for example, to deploy urgent security patches. In this paper, we propose a new method, VMM live upgrade, that can promptly upgrade the whole VMM (KVM & QEMU) without interrupting customer VMs. Timely upgrade of the VMM is essential to the cloud because it is both the main attack surface of malicious VMs and the component to integrate new features. We have built a VMM live upgrade system called Orthus. Orthus features three key techniques: dual KVM, VM grafting, and device handover. Together, they enable the cloud provider to load an upgraded KVM instance while the original one is running and cut-and-paste'' the VM to this new instance. In addition, Orthus can seamlessly hand over passthrough devices to the new KVM instance without losing any ongoing (DMA) operations. Our evaluation shows that Orthus can reduce the total migration time and downtime by more than $99%$ and $90%$, respectively. We have deployed Orthus in one of the largest cloud infrastructures for a long time. It has become the most effective and indispensable tool in our daily maintenance of hundreds of thousands of servers and millions of VMs.

SummaryIn this paper, a variety of anomaly detection strategies are provided with the application of the cloud platform center in view of the processor resources allocation according to the peak load demand, provision of the single anomaly detection strategy, and the problem of the sudden increase of the abnormal rate due to the change of the resource demand. The gray waveform detection algorithm is applied to detect the anomaly arriving at the virtual machine in the future time period, and the abnormal utility function of the virtual machine is given to balance the resource requirements and anomaly detection priority, and all the virtual machines are dynamically configured for each virtual machine with the maximization of the anomaly utility value of the virtual machine as the objective. Through the global load balancing between the same virtual machines and the redistribution of the virtual machines for multiple times, the detection quantity on the virtual machine anomaly with relatively huge increase of anomaly is further increased. Finally, the anomaly detection algorithm based on the gray waveform detection in the cloud platform center ADGWT is given. Simulation experiment results show that the proposed algorithm can effectively improve the abnormal rate of the processors in the cloud platform, and it has practical significance to improve the completion rate of the user requests.

Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different among hypervisors. As a result, the authors have the recommendation to choose the hypervisor ESX server if you need a scheme with higher ROI and lower TCO. Alternatively, Proxmox VE would be the second choice if you like to save the initial investment at first and own a pretty well management interface at console. Research limitations/implications – In the performance analysis, instead of ESX 5.0, the authors adopted ESXi 5.0 that is free software, its function is limited, and does not have the full functionality of ESX server, such as: distributed resource scheduling, high availability, consolidated backup, fault tolerance, and disaster recovery. Moreover, this paper do not discuss the security problem on VCCS which is related to access control and cryptograph in VMs to be explored in the further work. Practical implications – In the process of virtualizing the network, ESX/ESXi has restrictions on the brand of the physical network card, only certain network cards can be detected by the VM. For instance: Intel and Broadcom network cards. The newer versions of ESXi 5.0.0 and above now support parts of Realtek series (Realtek 8186, Realtek 8169, and Realtek 8111E). Originality/value – How to precisely assess the hypervisor for server/desktop virtualization is also of hard question needed to deal with it crisply before deploying new IT with VCCS on site. The authors have utilized the VMware calculator and developed an approach to server/desktop consolidation, virtualization performance, VM density, TCO, and ROIs. As a result, in this paper the authors conducted a comprehensive approach to analyze five well-known hypervisors and will give the recommendation for IT manager to choose a right solution for server virtualization.

Cloud computing provides virtual resources so that end-users or organizations can buy computing power as a public utility. Cloud service providers however must strive to ensure good QoS by offering highly available services with dynamically scalable resources. HA-OSCAR is an open source High Availability (HA) solution for HPC/cloud that offers component redundancy, failure detection, and automatic fail-over. In this paper, we describe HA-OSCAR as a cloud platform and analyze system availability of two potential cloud computing systems, OSCAR-V cluster and HA-OSCAR-V. We also explore our case study to improve Nimbus, a popular cloud IaaS toolkit. The results show that the system that deploys HA-OSCAR has a significantly higher degree of availability.

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.

Load balancing is the core of virtual resource management and scheduling in cloud computing. For network applications, the cost of user would be greatly saved if load balancer could dynamically adjust cluster resources in accordance with the current applied load. The current load balancing products of cloud, such as Amazon's ELB, can be used to manage virtual machines in the cloud. The main drawbacks are still only supporting template-based deployment of new virtual machines, not supporting the trend prediction, failing to gain resources dynamically, and not sufficiently providing the elastic management of resources. Since the virtual machine for load balancing management in cloud computing can be dynamically applied and released, an algorithm of prediction-based elastic load balancing resource management (TeraScaler ELB) is presented to overcome the drawbacks. Experiments have shown that the required number of virtual machines change in compliance with the change of network load, thus TeraScaler ELB is able to dynamically adjust the processing capacity of back-end server cluster with the applied load. Besides it could make full use of the 'use on demand' feature of cloud computing, TeraScaler ELB leads to a better application of prediction based load balancing in cloud computing. It concludes that compared with the traditional elastic resource management algorithm, TeraScaler ELB is more reasonable for providing scalability and high availability.

Digital Investigation on the cloud platform is a challenging task. In the Virtual Scenario, Virtual Machines contain evidences. If once VMDK (Virtual Machine Disk file) is destroyed (deleted), it is impossible to recover your VM. At present there does not exist any mechanism that can recover a destroyed VM again which is the flaw in VM itself. All the activities on the VM is logged in VM, whereas activities of CSP (Cloud Service Provider) is logged on the server. So even if someone deleted the VM, all the evidences will be lost. This creates a disaster for the user and acts as a barrier for a forensic investigator to dig out the sensitive data of user that was stored in the Virtual Machine sometime.

Cloud computing platforms are becoming viable alternative for running high performance parallel applications. However, when running these applications in cloud platforms, VMs (virtual machines) are easily affected by the synchronization latency problem which leads to serious performance degradation. There are two main reasons. The first is that a host is unaware of the synchronization requirements of the guest OS. The second is that the synchronization requests of VMs on a physical host are unknown to VMs on the other physical host. It is a great challenge to mitigate the negative influence of virtualization on synchronization and accelerate the synchronization response of HPC applications. In this paper, we propose a \(two-phase\) synchronization-aware (TPS) scheduling algorithm to solve the problem above. The TPS algorithm takes both intra-VMs’ and inter-VMs’ synchronization demands into consideration. Spin-locks and network packets are used as the metrics to detect the synchronization demands of VMs and VMs are scheduled based on spinlock-aware and communication-aware strategies. The algorithm is implemented on the base of KVM and experiments are conducted in a cluster environment. The experimental results show that our TPS algorithm obtains a better performance for HPC applications and reduces negative impacts on non-HPC applications simultaneously. Therefore, our approach is an effective solution to the HPC synchronization issues in cloud platforms.

Checkpoint-recovery based Virtual Machine (VM) replication is an emerging approach towards accommodating VM installations with high availability. However, it comes with the price of significant performance degradation of the application executed in the VM due to the large amount of state that needs to be synchronized between the primary and the backup machines. It is therefore critical to find new ways for attaining good performance, and at the same time, maintaining fault tolerant execution. In this paper, we present a novel approach to improve the performance of services deployed over replicated virtual machines by exploiting data similarity within the VM's memory image to reduce the network traffic during synchronization. For identifying similar memory areas, we propose a bit density based hash function, upon which, we build a content addressable hash table. We present a quantitative analysis on the degree of similarity we found in various workloads, and introduce a lightweight compression method, which, compared to existing replication techniques, reduces network traffic by up to 80% and yields a performance improvement over 90% for certain latency sensitive applications.

Large-scale data centers leverage virtualization technology to achieve excellent resource utilization, scalability, and high availability. Ideally, the performance of an application running inside a virtual machine (VM) shall be independent of co-located applications and VMs that share the physical machine. However, adverse interference effects exist and are especially severe for data-intensive applications in such vir-tualized environments. In this work, we present TRACON, a novel Task and Resource Allocation CONtrol framework that mitigates the interference effects from concurrent data-intensive applications and greatly improves the overall system performance. TRACON utilizes modeling and control techniques from statistical machine learning and consists of three major components: the interference prediction model that infers application performance from resource consumption observed from different VMs, the interference-aware scheduler that is designed to utilize the model for effective resource management, and the task and resource monitor that collects application characteristics at the runtime for model adaption. We simulate TRACON with a wide variety of data-intensive applications including bioinformatics, data mining, video processing, email and web servers, etc. The evaluation results show that TRACON can achieve up to 50% improvement on application runtime, and up to 80% on I/O throughput for data-intensive applications in virtu-alized data centers.