Xen-based virtual honeypot system for smart device

The virtual machine technology as new teaching aids in the teaching of computer network security has very good application prospect. At the same time, the virtual machine technology is adopted to simulate the real network teaching environment and improve teachers' teaching effect and has become a kind of effective means to ensure the quality of teaching. The virtual machine technology is adopted to enhance the teaching effect of Network Security. The installation and use of the virtual machine software VMware,setting a virtual network environment on single computer, and simulating attacks and defense on network safety, the teaching effect in both theory and practice teaching is promoted.The virtual machine technology application in computer network has very broad prospects.

Virtual machine (VM) technologies offer lots of benefits such as users' isolation, server consolidation and live migration. However, owing to the overhead incurred by indirect access to physical resources such as GPU, IO devices and VM technologies have not been widely used in high performance computing area. General purpose graphics processing unit (GPGPU) computing solution for virtual machines makes it possible that high performance computing (HPC) applications running on GPU can be ported to virtual machines (VMs) environment. A novel GPU resources management system built on VMs called VMGPURMS and a novel GPU cluster schedule policy are presented to use VM as computing node, and enable users to run jobs in the whole virtual machine environment transparently. Evaluation demonstrates that the efficiency of GPUs with the GPU cluster schedule policy could be improved by 17% over the traditional cluster schedule policy without taking GPU into consideration.

This study aims to explore the application of Virtual Reality (VR) and virtual machine technology in the restoration of ancient books, and to compare these methods with traditional restoration techniques. By elucidating the current status of ancient book restoration and the evolution of relevant technologies, the study identifies both the challenges and opportunities for cultural heritage preservation in the digital age. Additionally, the study introduces examples of virtual machine technology applications in data backup, restoration environment simulation, and cross-platform compatibility. Through case comparison analysis, the study comprehensively evaluates traditional and new technologies in terms of restoration quality, speed, and reliability. Furthermore, a cost-benefit analysis was conducted, considering labor, materials, and technology costs. The research results indicate that VR and virtual machine technology have significant advantages in enhancing restoration efficiency, reducing risks, and improving restoration accuracy. Lastly, the study anticipates future trends in restoration technology development and the potential for multi-technology integration, suggesting that intelligent and standardized approaches are crucial directions for future cultural heritage preservation.

Simulation is a common approach for assisting system design and optimization. For system-wide optimization, energy and computational resources are often the two most critical limitations. Modeling energy-states of each hardware component and time spent in each state is needed for accurate energy and performance prediction. Tracking software execution in a realistic operating environment with properly modeled input/output is key to accurate prediction. However, the conventional approaches can have difficulties in practice. First, for a complex system such as an Android smartphone, building a cycle-accurate simulation environment is no easy task. Secondly, for I/O-intensive applications, a slow simulation would significantly alter the application behavior and change its performance profile. Thirdly, conventional software profiling tools generally do not work on simulators, which makes it difficult for performance analysis of complicated software, e.g., Java applications executed by the Dalvik virtual machine. Recently, virtual machine technologies are widely used to emulate a variety of computer systems. While virtual machines do not model the hardware components in the emulated system, we can ease the effort of building a simulation environment by leveraging the infrastructure of virtual machines and adding performance and power models. Moreover, multiple sets of the performance and energy models can be selectively used to verify if the speed of the simulated system impacts the software behavior. Finally, performance monitoring facilities can be integrated to work with profiling tools. We believe this approach should help overcome the aforementioned difficulties. We have prototyped a framework and our case studies showed that the information provided by our tools are useful for software optimization and system design for Android smartphones.

In this paper, we investigate the benefits that organisations can reap by using "Cloud Computing" providers to augment the computing capacity of their local infrastructure. We evaluate the cost of seven scheduling strategies used by an organisation that operates a cluster managed by virtual machine technology and seeks to utilise resources from a remote Infrastructure as a Service (IaaS) provider to reduce the response time of its user requests. Requests for virtual machines are submitted to the organisation's cluster, but additional virtual machines are instantiated in the remote provider and added to the local cluster when there are insufficient resources to serve the users' requests. Naive scheduling strategies can have a great impact on the amount paid by the organisation for using the remote resources, potentially increasing the overall cost with the use of IaaS. Therefore, in this work we investigate seven scheduling strategies that consider the use of resources from the "Cloud", to understand how these strategies achieve a balance between performance and usage cost, and how much they improve the requests' response times.

Virtual machine technology and the ease with which VMs can be migrated within the LAN, has changed the scope of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration of virtual machines to likewise transform the scope of provisioning compute resources from a single data center to multiple data centers spread across the country or around the world. In this paper we present the CloudNet architecure as a cloud framework consisting of cloud computing platforms linked with a VPN based network infrastructure to provide seamless and secure connectivity between enterprise and cloud data center sites. To realize our vision of efficiently pooling geographically distributed data center resources, CloudNet provides optimized support for live WAN migration of virtual machines. Specifically, we present a set of optimizations that minimize the cost of transferring storage and virtual machine memory during migrations over low bandwidth and high latency Internet links. We evaluate our system on an operational cloud platform distributed across the continental US. During simultaneous migrations of four VMs between data centers in Texas and Illinois, CloudNet's optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 19GB, a 50% reduction.

Increasing the accuracy of the portrayal of all of the elements of a simulation environment has long been a prime goal of the modeling and simulation community; a goal that has remained far out of reach for many reasons. One of the greatest hurdles facing simulation developers in the effort to increase simulation accuracy is the need to segregate information across the entire simulation environment according to access restrictions in order to insure the integrity, security, and reliability requirements imposed on the data. However, this need for segregation does not mean that those with the highest access permissions should be forced to use multiple computers and displays to integrate the information that they need or that intelligent agents should be restricted in their access to the information that they need in order to adequately assist their human operators. In this paper, we present a potential solution to the problem of integrating and segregating data, which is the use of virtual machine and virtual private network technology in order to maintain segregation of data, control access, and control intercommunication.

With the virtual machine technologies, Virtual Ad hoc Network (VAN) testbed was designed to evaluate functional correctness and communication performance of Mobile Ad hoc Network (MANET) applications. When VAN is used for large-scale testing that requires hundreds of virtual machines, storage redundancy becomes an issue. Although Content Addressable Storage (CAS) techniques were designed to address the storage redundancy issue, it incurred online hash computation overhead for every write access to disk blocks, which affects testing accuracy. We present File-level Block Sharing (FBS) that achieves the same functionality of CAS while removing the online computation overhead. By getting file-to-block mappings through read-only mounting, FBS only needs to handle the blocks belonging to newly-installed files offline and thus incurs little online overhead. Our prototype showed no online overhead statistically and low offline overhead. The prototype was developed and its overhead with respect to block-level storage deduplication was analyzed under both Ext2/3/4 and NTFS file systems.

The programming foundation course which based on Python has become an important basic course for different disciplines in universities. In experimental teaching, the main difficulty is the environment configuration and rapid deployment of Python. Virtualization technology is a common method to solve this problem. However, there are some problems, such as low resource utilization, insufficient support for concurrent, slow start, and so on. So, a new method for constructing the teaching experiment platform of the course using Docker container technology is proposed. This method can quickly realize the dynamic allocation, deployment and management of the running environment image according to the needs of students. Experimental results show that this method has obvious advantages in terms of resource utilization, concurrent users, response time, construction cost and so on, compared with virtual machine technology

The newly emergent cloud computing environments host hundreds to thousands of services on a shared resource pool. The sharing is enhanced by virtualization technologies allowing multiple services to run in different virtual machines (VMs) on a single physical node. Resource over-booking allows more services with time-varying demands to be consolidated reducing operational costs. In the past, researchers have studied dynamic control mechanisms for allocating CPU to virtual machines, when CPU is over-booked with respect to the sum of the peak demands from all the VMs. However, runtime re-allocation of memory among multiple VMs has not been widely studied, except on VMware platforms. In this paper, we present a case study where feedback control is used for dynamic memory allocation to Xen virtual machines in a consolidated environment. We illustrate how memory behaves differently from CPU in terms of its relationship to application-level performance, such as response times. We have built a prototype of a joint resource control system for allocating both CPU and memory resources to co-located VMs in real time. Experimental results show that our solution allows all the hosted applications to achieve the desired performance in spite of their time-varying CPU and memory demands, whereas a solution without memory control incurs significant service level violations.

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Network intrusions become a signification threat to network servers and its availability. A simple intrusion can suspend the organization's network services and can lead to a financial disaster. In this paper, we propose a framework called TimeVM to mitigate, or even eliminate, the infection of a network intrusion on-line as fast as possible. The framework is based on the virtual machine technology and traffic-replay-based recovery. TimeVM gives the illusion of machine. TimeVM logs only the network traffic to a server and replays the logged traffic to multiple shadow virtual machines (Shadow VM) after different time delays (time lags). Consequently, each Shadow VM will represent the server at different time in history. When attack/infection is detected, TimeVM enables navigating through the traffic history (logs), picking uninfected Shadow VM, removing the attack traffic, and then fast-replaying the entire traffic history to this Shadow VM. As a result, a typical up-to-date uninfected version of the original system can be constructed.The paper shows the implementation details for TimeVM. It also addresses many practical challenges related to how to configure and deploy TimeVM in a system in order to minimize the recovery time. We present analytical framework and extensive evaluation to validate our approach in different environments.

Advances in virtualization technology have focused mainly on strengthening the isolation barrier between virtual machines (VMs) that are co-resident within a single physical machine. At the same time, a large category of communication intensive distributed applications and software components exist, such as web services, high performance grid applications, transaction processing, and graphics rendering, that often wish to communicate across this isolation barrier with other endpoints on co-resident VMs. State of the art inter-VM communication mechanisms do not adequately address the requirements of such applications. TCP/UDP based network communication tends to perform poorly when used between co-resident VMs, but has the advantage of being transparent to user applications. Other solutions exploit inter-domain shared memory mechanisms to improve communication latency and bandwidth, but require applications or user libraries to be rewritten against customized APIs - something not practical for a large majority of distributed applications. In this paper, we present the design and implementation of a fully transparent and high performance inter-VM network loopback channel, called XenLoop, in the Xen virtual machine environment. XenLoop does not sacrifice user-level transparency and yet achieves high communication performance between co-resident guest VMs. XenLoop intercepts outgoing network packets beneath the network layer and shepherds the packets destined to co-resident VMs through a high-speed inter-VM shared memory channel that bypasses the virtualized network interface. Guest VMs using XenLoop can migrate transparently across machines without disrupting ongoing network communications, and seamlessly switch between the standard network path and the XenLoop channel. In our evaluation using a number of unmodified benchmarks, we observe that XenLoop can reduce the inter-VM round trip latency by up to a factor of 5 and increase bandwidth by a up to a factor of 6.

Virtual machine technology is rapidly gaining acceptance as a fundamental building block in enterprise data centers. It is most known for improving efficiency and ease of management. However, it also provides a compelling approach to enhancing system security, offering new ways to rearchitect todays systems and opening the door for a wide range of future security technologies. While this technology is meant to enhance the security of computer systems, some recent attacks show that virtual machine technology has many weaknesses and becomes exposed to many security threats. In this paper we present some of these threats and show how we protect these systems through intrusion detection and security policies mechanisms.

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.