A Nonparametric Multivariate Method for Performance Analysis of Virtual Machines in Cloud Computing Systems

The security of cloud services and underlying resources is a major concern due to vulnerabilities existing in current implementation of the virtualization. Thus, there is a need of detecting system-level attacks like viruses, worms, malware, etc. In this paper, we extend our previous work on vulnerability assessment and patching by integrating in-VM-assisted agent-based malware detection (AMD) framework for securing high-risk virtual machines (VMs) in cloud. The proposed framework has two components, viz. agent at VM and anomaly detection at hypervisor. An agent continuously looks for the new deployment of the executable in-VM and applies the signature-based detection to detect known malware. For detecting unknown attacks, it generates the profile with optimal static features for new executable. The optimal features are derived using an extended binary bat algorithm with two new fitness functions. The profile is transferred to hypervisor where anomaly detection using random forest classifier is applied. It classifies the executable to either normal or malware and generates an alert to VM user. The functionality of the proposed AMD framework is validated over cloud testbed at NIT Goa, as well as with the latest malware datasets. In addition, we analyze the VM security requirements fulfilled by the proposed framework.

Cloud service providers offer virtual machines (VMs) as services to users over Internet. As VMs are running on physical machines (PMs), PM power consumption needs to be considered. Meanwhile, VMs running on the same PM share physical resources, and there exists great resource contention, which results in VM performance degradation. Therefore, how to place VMs to reduce PM power consumption and guarantee VM performance is still one major challenge. However, existing VMPs did not study VM performance degradation, so they could not guarantee VM performance. To solve the high power consumption and VMs performance degradation problems, this paper explores the balance between saving PM power and guaranteeing VM performance, and proposes a power-aware and performance-guaranteed VMP (PPVMP). First, we investigate the relationship between power consumption and CPU utilization to build a non-linear power model, which is helpful for the following VMP. Second, we construct VM performance models to present the VM performance degradation trend. Third, based on these models, we formulate VMP as a bi-objective optimization problem, which tries to minimize PM power consumption and guarantee VM performance. We then propose an algorithm based on ant colony optimization to solve it. Finally, the results show the efficiency of our algorithm.

In this paper, a research work is carried out to improve workload management by minimize energy consumption of node, localizing traffic in a network and reduce communication delay between virtual machines in cloud environment. Cloud computing consists numerous virtual machine to store huge volume of data. The usage of virtual machines is to provide separate guest operating system which is popular now-a-days. The workload management across different virtual machines in cloud is really a difficult process. Decentralized approaches were proposed in the literature for energy efficient management of virtual machines. But still it has some issues like failed to consider the communication between virtual machines and it leads to traffic in a network. Thus it reduces the performance of work load management.

In Infrastructure-as-a-Service clouds, there exist many virtual machines (VMs) that are not used for a long time. For such VMs, many vulnerabilities are often found in installed software while VMs are suspended. If security updates are applied to such VMs after the VMs are resumed, the VMs easily suffer from attacks via the Internet. To solve this problem, offline update of VMs has been proposed, but some approaches have to permit cloud administrators to resume users' VMs. The others are applicable only to completely stopped VMs and often corrupt virtual disks if they are applied to suspended VMs. In addition, it is sometimes difficult to accurately emulate security updates offline. In this paper, we propose OUassister, which enables consistent offline update of suspended VMs. OUassister emulates security updates of VMs offline in a non-intrusive manner and applies the emulation results to the VMs online. This separation prevents virtual disks of even suspended VMs from being corrupted. For more accurate emulation of security updates, OUassister provides an emulation environment using a technique called VM introspection. Using this environment, it automatically extracts updated files and executed scripts. We have implemented OUassister in Xen and confirmed that the time for critical online update was largely reduced.

Virtualization is important in server consolidation and essential for cloud computing. Using virtualization technology, a single physical machine can provide multiple isolated virtual machine to users, which will improve the overall system resources usage. However, virtualization will also introduce new challenges and mechanisms that work functionally in traditional operating system may lead to the performance degradation of virtual machines. LHP problem arises because spin lock mechanism could not work effectively in virtualization environment. To solve LHP problem, many solutions have been proposed to reduce its performance impact on virtual machines and improve the overall system performance. A hardware assisted technology called Pause Loop Exit(PLE) has been proposed to help detecting spin lock waiter in virtual machines and then reduce the cpu time used in spinning. In this paper, we verify the necessity of adjusting PLE configuration in different scenarios. Our experiment results show that it is necessary to adjust PLE configuration when the virtual machine is running different applications and the number of virtual machines varies.

The rapid development of cloud computing expands the scale of modern cloud data centers, resulting in growing challenges of energy consumption. Load balancing of virtual machines(VMs), for the purposes of improving the utilization of physical resources and reducing energy consumption, has become a research focus in recent years. However, the existing researches mostly focus on how to maximize resource utilization and reduce energy consumption. Security issues in the context of load balancing of VMs were rarely addressed. In this paper, we research the key procedures of load balancing, VM selection and VM placement, we find that the existing schemes introduced several security problems. In consideration of some conclusions of recent researches and inevitable live migrations during load balancing, common tenants have sufficient reasons to worry about their VMs' security when they are migrated to strange hosts and/or co-reside with the VMs owned by strange tenants. In short, VMs' mobility introduced by load balance expands the attack surface. In this work, we classify and analyze related security threats and create an information leakage model for load balancing. We present a new security policy, SeLance, to secure the load balancing via avoiding above threats as far as possible. We develop exact implementations in CloudSim and OpenStack. We show that SeLance can effectively alleviate the threats introduced by load balancing, the security score can improve 46.90%-81.15%, while keeping the load balancer's original function to a great extent (± 2.5%).

In general, operating systems (OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same configuration. Applications in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine (VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, vSpec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. vSpec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of vSpec on benchmarks and real-world applications.

This paper presents ALARM, an autonomic load-aware resource management algorithm that can be used to manage physical machines or virtual machines in cloud, which participate in a P2P key-value store. A lot of existing key-value stores claim that they are elastic enough to scale up or down with no downtime or interruption to applications. However, the question that when the scaling up or down should take place has still not been resolved. The situation may get worse if the data store consists of hundreds of machines, for it's unrealistic for a system administrator to monitor the system and add/remove a machine manually. Fortunately, cloud computing and virtualization technology have enabled the real-time provision of virtual machines and a way of managing virtual machines without human interference. By supervising the utilization of multiple resources (CPU, memory, network IO, etc.) in virtual machines hosting the data store, our ALARM algorithm will take effect when some of the machines become overloaded or under loaded. The experiment result shows that ALARM helps the Open Chord data store, an open-source implementation of the Chord protocol, scale up and down according to the resource usage in the virtual machines.

Service level agreement (SLA) is a contract between cloud computing customer and provider. Improving the customers' trust with cloud computing requires monitoring and ensuring the quality of the provided services continuously. Therefore, this paper provides a model for monitoring the SLA which belongs to the same customer and a specific provider of multiple virtual machines (VMs) in the same environment. The core foundations for this model are: SLA, quality of service (QoS) parameters, multiple VMs and violations' penalty assessment. The proposed model monitors the QoS parameters within multiple VMs environment dynamically and checks for any violation. Moreover; the penalty for each violation will be calculated and informed to the customer and provider at the same time. The proposed model depends on the time factor while running the VMs in sequentially manner with a short period of time as a gap between each VM. This model prevents any interception between VMs and can maintain synchronisation.

Virtual Infrastructure-as-a-Service (IaaS) clouds are emerging for secondary cloud service providers to manage their own IaaS clouds on top of existing IaaS clouds. In virtual IaaS clouds, guest virtual machines (VMs) run inside cloud VMs provided by existing IaaS clouds. Unlike traditional IaaS clouds, they can be migrated between cloud VMs co-located at the same host. However, the performance of such VM migration is low due to slow virtual networks and doubled system loads. To optimize VM migration between co-located cloud VMs, we propose zero-copy migration for virtual IaaS clouds. Zero-copy migration just relocates the memory image of a guest VM without any copy. To enable live migration with negligible downtime, it first makes the memory of a guest VM share with the destination cloud VM and thereafter completes memory relocation. We have implemented a system called VMBeam for enabling zero-copy migration in Xen. According to our experimental results, zero-copy migration could achieve high migration performance and low system loads.

Kernel control-flow integrity (CFI) of virtual machines is very important to cloud security. VMI-based dynamic tracing and analyzing methods are promising options for checking kernel CFI in cloud. However, the CFI monitors based on tracing always work at instruction or branch level and result in serious virtual machine performance degradation. To meet the performance requirements in the cloud, we present a page-level dynamic VMI-based kernel CFI checking solution. We trace VM kernel execution at page level, which means that the in-page instruction execution cannot trigger our monitor. As a result, the tracing overhead can be greatly reduced. Based on page-level execution information, we propose two policies to describe the kernel control-flow so as to build the secure kernel control-flow database in the learning stage. In the monitoring stage, we compare runtime execution information with the secure database to check kernel CFI. To further reduce the monitoring overhead, we propose two performance optimization strategies. We implement the prototype on Xen and leverage hardware events to trace VM memory page execution. Then, we evaluate the effectiveness and performance of the prototype. The experimental results prove that our system has enough detection capability and the overhead is acceptable.

In this research, an Individual Learner Big Data Based Cloud Role Player Game Learning Corresponding to Individual Learner Big Data is proposed. Based on the proposed platform, all the learning states and information (Big Data) of individual learner can be further analyzed. By using dynamic assigni ng algorithm, individual learner (student) can be separated into different levels related to the learning ability. According to dynamic assigning algorithm, student's answering times and answering correct rate of each education scenario in the proposed cloud based digital game learning system is considered. Then, suitable teaching scenario will be provided for individual user corresponding to personal learning ability. Each designed modular teaching scenario can be embedded into the game platform on the virtual machine in cloud. The information about students in the game learning system can be stored in the cloud database. In addition, the whole learning system is established as a virtual machine. System maintainer can configure the learning system easily and quickly. Based on cloud, different remote devices can connect to server for learning.

In this paper, we propose a Cloud Based Dynamical Digital Game Learning Scenario Corresponding to Individual Learner Big Data according to the proposed dynamic assigning algorithm to separate individual learner into different levels. In dynamic assigning algorithm, student's answering times and answering correct rate for each education scenario in cloud based digital game learning system is considered. Suitable teaching scenario will be provided for individual user corresponding to personal learning ability. Each designed modular teaching materials can be embedded into the game platform on the virtual machine in cloud. The information about users in the individual game learning system can be stored in the cloud database. In addition, the whole learning system is established as a virtual machine. System maintainer can configure the learning system easily and quickly. Based on cloud, different remote devices can connect to server for learning.

In recent years, the demand for disassembly is increasing. the variability of the feed product and the degree of damage that occurs during the disassembly process. In this research, the optimized mathematical programming models are proposed based on the transformed AND/OR graphic(TAOG) as a priority relation between jobs. To find the optimal solution, different steps and types of the evolution algorithm can be established via individual thread procedures and various virtual machines in cloud. The proposed improvement phase based on the fitness evaluation result according to different crossover methods can enhance the performance of the convergence.

WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment