Optimizing dynamic survivability and security of replicated data in cloud systems under co-residence attacks

Empowered by virtualization technology, service requests from cloud users can be honored through creating and running virtual machines. Virtual machines established for different users may be allocated to the same physical server, making the cloud vulnerable to co-residence attacks where a malicious attacker can steal a user's data through co-residing their virtual machines on the same server. For protecting data against the theft, the data partition technique is applied to divide the user's data into multiple blocks with each being handled by a separate virtual machine. Moreover, early warning agents (EWAs) are deployed to possibly detect and prevent co-residence attacks at a nascent stage. This article models and analyzes the attack success probability (complement of data security) in cloud systems subject to competing attack detection process (by EWAs) and data theft process (by co-residence attackers). Based on the suggested probabilistic model, the optimal data partition and protection policy is determined with the objective of minimizing the user's cost subject to providing a desired level of data security. Examples are presented to illustrate effects of different model parameters (attack rate, number of cloud servers, number of data blocks, attack detection time, and data theft time distribution parameters) on the attack success probability and optimization solutions.

In Infrastructure-as-a-Service (IaaS) clouds, users manage the systems in virtual machines (VMs) called user VMs through remote management systems (RMSes). To allow users to manage their VMs during failures inside the VMs, IaaS usually provides out-of-band remote management. This management is performed indirectly via an RMS server in a privileged VM called the management VM. However, it is discontinued when a user VM is migrated. This is because an RMS server in the management VM at the source host is terminated on VM migration. Even worse, pending data is lost between an RMS client and a user VM. In this paper, we propose D-MORE for continuing out-of-band remote management across VM migration. D-MORE provides a privileged and migratable VM called DomR and performs out-of-band remote management of a user VM via DomR. During VM migration, it synchronously co-migrates DomR and its target VM and transparently maintains the connections between an RMS client, DomR, and its target VM. We have implemented D-MORE in Xen and confirmed that a remote user could manage his VM via DomR after the VM has been migrated. Our experiments showed that input data was not lost during VM migration and the overhead of D-MORE was acceptable.

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.

Application of Information Processes Applicative Modelling to Virtual Machines Auto Configuration

Enterprise marketing information is stored in the form of message files or databases, which is increasing exponentially. Under the conditions of information failure, the backup is not timely, resulting in a decrease in storage efficiency. Therefore, research on the private cloud storage method of marketing information is performed based on grid technology. Moreover, grid basic services are provided through grid system management nodes and grid public service nodes, and local resource servers and enterprise grid application services are used to provide grid application services. Based on each server node in the model, the virtual machine usage is adjusted through the load forecasting process, and the mirroring backup process is applied to restore the virtual machine data under fault conditions. In addition, the model uses the longest path matching method to manage marketing information metadata based on metadata logical search and location metadata sub-table. Experimental results show that the load of different virtual machines in the marketing information storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that the method proposed in the paper can enhance the balance of blocking in the information storage process and improve storage efficiency.

SummaryThis paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infrastructure‐as‐a‐service (IaaS) cloud service model is mainly addressed to extend and complement the previous Trusted Computing techniques for secure VM launch and VM migration case. The VM migration solution proposed in this paper uses a Trust_Token based to guarantee that the user VMs can only be migrated and hosted on a trustworthy and/or compliant cloud platforms. The possibility to also check the compliance of the cloud platforms with the pre‐defined baseline configurations makes our solution compatible with an existing widely accepted standards‐based, security‐focused cloud frameworks like FedRAMP. Our proposed solution can be used for both inter‐ and intra‐cloud VM migrations. Different from previous schemes, our solution is not dependent on an active (on‐line) trusted third party; that is, the trusted third party only performs the platform certification and is not involved in the actual VM migration process. We use the Tamarin solver to realize a formal security analysis of the proposed migration protocol and show that our protocol is safe under the Dolev‐Yao intruder model. Finally, we show how our proposed mechanisms fulfill major security and trust requirements for secure VM migration in cloud environments.

Energy-efficient cloud systems: Virtual machine consolidation with -robustness optimization.

: The North American Aerospace Defense Command (NORAD) and United States Northern Command (USNORTHCOM) (N-NC) crew training has been hindered by an inability to conduct dynamic training and exercises on multiple Command and Control (C2) systems. There was no common simulation injector because of stove-piped acquisitions and legacy interfaces that were incompatible with the Live-Virtual-Constructive Toolkits. The N-NC Joint Training and Exercise Directorate could not afford traditional replication or emulation of all C2 systems and their data sources, nor the inevitable sustainment costs. This paper presents a cost-effective solution to provide dynamic scenario injection into multiple C2 systems: leveraging server and desktop virtualization technology described in previous I/ITSEC papers. The virtualization process transforms stand-alone systems into functionally equivalent virtual machines (VMs). Server virtualization technology lets multiple VMs run as guests on a single host, and a host can support VMs running different operating systems. This allows entire processing strings, distributed throughout North America, to be converted into VMs on a single server. Because the VMs inherit the fidelity of the actual processors, their outputs are as authentic as the operational systems. These VMs feed processed simulation event data into actual C2 systems or equivalent VMs. Future operational system upgrades can be virtualized and then replace existing VMs without changing this infrastructure. Desktop virtualization technology allows users to run multiple VMs in separate windows on a common display. NNC exploited desktop virtualization to simplify the trainee and model operator's workspaces. They can view and manage multiple VMs with one monitor, keyboard and mouse (controlling simulations, lower echelon processing and operator interaction, and viewing C2 workstation displays).

As server consolidations based on the virtualization techniques become popular and cloud services continue to grow rapidly, more and more data centers are being built to accommodate a number of virtual clusters running various workloads. Since these virtual clusters often share the resources provided by physical machines (PMs), it is more likely that the interferences between virtual machines (VMs) affect the performance of applications running on top of the virtual clusters. While a lot of studies have proposed different virtual machine placement algorithms to investigate this issue, the problem caused by network performance variability still remains as a challenging issue. Since they usually ignore the CPU overhead to process the communications between VMs, the network bandwidth allocated to a VM cannot be fully utilized when a PM has not enough CPU resources to cover the CPU overhead for VM networking functions. This results in unpredictable application performance running on the virtual clusters. This paper proposes a virtual machine placement algorithm that considers the CPU overhead incurred to reserve network bandwidth in a virtualized data center environment. In order to decide the CPU overhead necessary to guarantee the network bandwidth allocated to a VM, a performance model based on standard linear regression using the data collected from a real environment is used. By comparing the amount of CPU resource available in the driver domain with the CPU overhead obtained from the performance model, the proposed algorithm decides whether the network bandwidth for the VM can be provided or not and selects an appropriate location for VM placement. The benchmarking results show that the proposed algorithm guarantees the network bandwidth allocated to each VM without violations when the CPU resources are shared by multiple VMs.

Security Strategy for Virtual Machine Allocation in Cloud Computing

This paper addresses the problem of simultaneously migrating a group of co-located and virtual machines (VMs), i.e, VMs executing on the same physical machine. We refer to such a mass simultaneous migration of active VMs as live gang migration. Cluster administrators may often need to perform gang migration for load balancing, system maintenance, or power savings. Application performance requirements may dictate that the total migration time, network traffic overhead, and service downtime, be kept minimal when migrating multiple VMs. State-of-the-art migration techniques optimize the migration of a single VM. In this paper, we optimize the simultaneous migration of multiple co-located VMs. We present the design, implementation, and evaluation of a de-duplication based approach to perform concurrent migration of co-located VMs. Our approach transmits memory content that is identical across VMs only once during migration to significantly reduce both the total migration time and network traffic. Using the QEMU/KVM platform, we detail a proof-of-concept prototype implementation of two types of de-duplication strategies (at page level and sub-page level) and a differential compression approach to exploit content similarity across VMs. Evaluations over Gigabit Ethernet with various types of VM workloads demonstrate that our prototype for gang migration can achieve significant reductions in both network traffic and total migration time.

In elastic cloud computing environment, multiple virtual machines may reside in the same physical machine for services consolidation. For the same residential guest domains or multi-tiered hosting services, the inter-domain communications are complex and frequent. However, traditional inter-domain communications are conducted through the virtual network interfaces of both sending and receiving virtual machines. Such TCP/IP networking communications involve lengthy communication path and frequent kernel interactions and thus downgrade the I/O performance of virtual machines. In this paper, we present an optimized inter-domain communication approach based on shared memory to improve the communication performance of multiple virtual machines residing in the same physical machine. In our approach, the sending data is shared in memory, not transferred via TCP/IP stack. The communication data are directly mapped into user space of the virtual machine and therefore it reduces useless system calls and context switches. The memory is shared via a customized shared device kernel module with bidirectional event channel resided in both communicating virtual machines. We use state flags in circular buffer to reduce wait and notify operations and thus reduce the system calls. Experiments show that our proposed approach can provide 5 time higher throughput and 2.5 times less latency compared to TCP/IP communication path.

We introduce a method for protection against a side-channel attack made possible by the use of a cloud-computing feature called memory deduplication. Memory deduplication improves the efficiency with which physical memory is used by the virtual machines (VMs) running on the same server by keeping in memory only one copy of the libraries and other software used by multiple VMs. However, this allows an attacker’s VM to find out the memory locations (and thus the operations) used by a victim’s VM, as these locations are cached and can be accessed faster than memory locations not used by the victim. To perform the attack, the malicious VM needs to execute an abnormal sequence of cache flushes, and our new method detects this by monitoring memory locations associated with sensitive (e.g., encryption) operations and using logistic regression to identify the abnormal cached operations. Furthermore, by using its own cache flushing, our method disrupts the side channel, making it more difficult for the attacker to acquire useful information. The experiments we ran using the KVM hypervisor and Ubuntu 18.04 LTS VMs on both Debian 10 and CentOS physical servers show that our method can detect attacks with 99% accuracy, and can feed fake information to an attacker with between 2–8% CPU overheads.

Scalable, high performance, and fault-tolerant distributed applications are realized with virtual machines in server cluster systems. Application processes are performed on virtual machines in each server. Processing load of virtual machines to perform application processes has to be balanced in a server cluster system to satisfy the application requirements like response time. On the other hand, a server cluster system consumes a large amount of electric energy since multiple servers consume electric energy to perform application processes. It is critical to discuss how to reduce the total electric energy consumption of a server cluster to perform application processes on virtual machines. In our previous studies, the transmission model and power consumption model of a server to perform communication processes on multiple virtual machines are proposed. In this paper, we newly propose the transmission energy consumption laxity based (TECLB) algorithm to allocate communication processes to virtual machines in a server cluster based on the proposed transmission model and power consumption model of a server so that the total energy consumption of a server cluster can be reduced. We evaluate the TECLB algorithm in terms of the total energy consumption of a server cluster and transmission time of each process compared with the basic round-robin (RR) algorithm. The evaluation results show the average total energy consumption of a server cluster is maximumly reduced to 9% of the RR algorithm.

Server virtualization more readily enables the collocation of disparate workloads on a shared physical platform. When employed on systems across a data center, the result can be a dramatic increase in server utilization and a decrease in overall power, cooling and floor space requirements. However, in an environment where workloads share the underlying platforms, achieving other desirable workload goals, such as availability and security, becomes a challenge. In particular, enforcing isolation between workloads in a large, dynamic, and virtualized data center requires strong yet easily configurable controls on the sharing of resources at the virtualization layer. Commercial hypervisors usually offer reasonable isolation of individual virtual machines (VMs). However, on hypervisor-based platforms, one cannot currently define a single policy that automatically enforces restrictions on the sharing of resources between multiple VMs or request an air gap between workloads. In this paper, we describe the design and implementation of a Hypervisor-based Mandatory Access Control (MAC) that achieves policy-driven distributed workload isolation for the IBM Power Hypervisor (PHYP). We discuss our experiences and lessons learned and examine the implications and trade-offs involved in providing MAC on a production- level, commercially-available hypervisor. Our goal is to simplify the security management of data centers through centralized security management and policy- driven distributed access control and data protection.