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Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center.

In conventional virtualized systems, a hypervisor can access the memory pages of guest virtual machines without any restriction, as the hypervisor has a full control over the address translation mechanism. In this paper, we propose Secure MMU, a hardware-based mechanism to isolate the memory of guest virtual machines from unauthorized accesses even from the hypervisor. The proposed mechanism extends the current nested paging support for virtualization with a small hardware cost. With Secure MMU, the hypervisor can flexibly allocate physical memory pages to virtual machines for resource management, but update nested page tables only through the secure hardware mechanism, which verifies each mapping change. With the hardware-rooted memory isolation among virtual machines, the memory of a virtual machine in cloud computing can be securely protected from a compromised hypervisor or co-tenant virtual machines.

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 Machines (VMs) and network virtualization technologies have been widely used for cloud computing. Active VMs can move to other execution environments, an action called "live migration." VMs must have the same IP address before and after migration. Therefore, live migration must be in the same segment. For this reason, network virtualization technologies are used to make a wide area L2 network between data centers (DC). Such L2 networks have a redundant route problem resulting from the distance between a particular VM and the default gateway. To solve this problem, each DC has a default gateway and VMs use the nearest default gateway. However, if live migration is used, the VMs' nearest default gateway changes. In this paper, we explain these problems, and propose solutions to change to the proper default gateway.

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.

Increasing demand for Cloud infrastructure and services leads to the challenges for management and maintenance of large data Center. Data center is fully equipped with huge number of resources. Those resources consumes energy in spite of their partial or full utilization. As a result data center consumes lots of energy, which in turn increases the total cost of operation and carbon footprint in environment. These concern leads to “Green Computing”, i.e. to reduce total operational cost, Carbon Footprint in environment and efficient usage of the computing resources. In data center main processing element is virtual machine (VM), which is an instance of computing and storage resources, handles computational processes. Hence, it is important to reduce energy consumed by VM. As the workload distribution is varying in data Center as per the need, the number of VMs configured in the host are uneven, but host consumes maximum energy every time, irrespective of the workload. This leads to wastage of computational resources. This paper is intended to analyze such issues and specifically prove an algorithm which, significantly reduces energy consumption in data center, while ensuring SLA, when VM is in migration from one host to another in the data center.

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.

Recent developments in virtualization and communication technologies have transformed the way data centers are designed and operated by providing new tools for better sharing and control of data center resources. In particular, Virtual Machine (VM) migration is a powerful management technique that gives data center operators the ability to adapt the placement of VMs in order to better satisfy performance objectives, improve resource utilization and communication locality, mitigate performance hotspots, achieve fault tolerance, reduce energy consumption, and facilitate system maintenance activities. Despite these potential benefits, VM migration also poses new requirements on the design of the underlying communication infrastructure, such as addressing and bandwidth requirements to support VM mobility. Furthermore, devising efficient VM migration schemes is also a challenging problem, as it not only requires weighing the benefits of VM migration, but also considering migration costs, including communication cost, service disruption, and management overhead. This chapter provides an overview of VM migration benefits and techniques and discusses its related research challenges in data center environments. Specifically, the authors first provide an overview of VM migration technologies used in production environments as well as the necessary virtualization and communication technologies designed to support VM migration. Second, they describe usage scenarios of VM migration, highlighting its benefits as well as incurred costs. Next, the authors provide a literature survey of representative migration-based resource management schemes. Finally, they outline some of the key research directions pertaining to VM migration and draw conclusions.

Cloud computing systems routinely process users' confidential data, but the underlying virtualization software in use today is not constructed to minimize the exposure of such data. For instance, virtual machine (VM) checkpointing can drastically prolong the lifetime and vulnerability of confidential data without users' knowledge by storing such data as part of a persistent snapshot. A key requirement for minimizing the exposure of any data is the ability to cleanly isolate such data for either exclusion or processing. Traditional mechanisms for memory taint tracking are expensive whereas those for isolating application footprint in VM-based sandboxes are not transparent. In this paper, we propose a transparent and lightweight mechanism for isolating a confidential application's memory footprint in a VM. The key idea is for a parent VM to spawn a child VM, called a Privacy-preserving Virtual Machine (PPVM) within which the confidential application executes. Hypervisor features, such as VM checkpointing, that need to exclude the memory of a confidential application can safely ignore the child VM's memory footprint. Alternatively, features such as checkpoint encryption or malware tracking can operate only on the child VM's memory. We implement memory isolation for PPVM through a lightweight VM fork operation that uses copy-on-write to reduce the memory and filesystem overhead of the PPVM. Transparency is achieved through a confidential shell that allows the parent VM to spawn the confidential application in the PPVM and exercise control over it during runtime. We demonstrate the effectiveness of PPVM through its use with VM checkpointing, which can safely checkpoint the parent VM while excluding or encrypting the associated PPVM. We show that our PPVM implementation achieves effective memory isolation with low overheads on memory, CPU, and network performance.

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

CBase: Fast Virtual Machine storage data migration with a new data center structure

The big data era is presenting unprecedented opportunities for generating new revenues in various sectors ranging from health care, economics, life science, to manufacturing. Datacenters (DCs) are widely deployed to provision various application services as well as to process big data. Since more and more servers are installed in DCs, the cost of electricity incurs a financial burden for the DC operators. Many DCs are equipped with renewable energy to reduce the electricity bill. However, the locations of the energy demands do not match the locations of the renewable energy generation. This mismatch may be addressed by virtual machine (VM) migration. The DCs, which lack renewable energy, can migrate their workloads to other DCs, which have abundant renewable energy. In addition, DC operators always want to maximize revenue and minimize operation cost. In this paper, the problem of maximizing revenue and minimizing operation cost of a green DC network enabled with and without VM migration is formulated by integer linear programming. Simulation results show that optimal results can be reached for small size problems. Two heuristic algorithms are proposed to efficiently solve large size problems. To our best knowledge, this is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration.

In modern data centres (DCs), energy consumption and resource management are challenging tasks. So in large virtualized DCs, performance of applications is highly dependent on energy-aware DC architecture and smooth network communication between virtual machines (VMs), while minimizing the communication burden to avoid congestion, latency etc. The communication cost of a network can be reduced by minimizing the VMs migration between physical machines (PMs). Therefore customer as well as service providers need cloud computing solutions that not only minimize operational costs, but also total network load. Minimizing the use of energy/network communication overhead with maximum resource utilization in large DCs is a challenging task as computing applications and data are growing so quickly for which increasing larger servers and disks are required to process them fast enough within the required time period. In order to minimize network load and maximize resource utilization in cloud DCs, we simulated our proposed network load-aware scheduling algorithm that ensures minimum VMs migration while delivering the negotiated Quality-of-Service (QoS). Our simulation also shows results with different bit rates.

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.