Dynamic Guest Memory Resizing - Paravirtualized Approach

In this paper we develop a model which represents the addressing of resources by processes executing on a virtual machine. The model distinguishes two maps: the o-map which represents the map visible to the operating system software running on the virtual machine, and the f-map which is invisible to that software but which is manipulated by the virtual machine monitor running on the real machine. The o-map maps process names into resource names and the f-map maps virtual resource names into real resource names. Thus, a process running on a virtual machine addresses its resources under the composed map f o o. In recursive operation, f maps from one virtual machine level to another and we have f o f o ... o f o o.The model is used to describe and characterize previous virtual machine designs. We also introduce and illustrate a general approach for implementing virtual machines which follows directly from the model. This design, the Hardware Virtualizer, handles all process exceptions directly within the executing virtual machine without software intervention. All resource faults (VM-faults) generated by a virtual machine are directed to the appropriate virtual machine monitor without the knowledge of processes on the virtual machine (regardless of the level of recursion).

Since a virtual machine independently uses its own caching policy, redundant disk operations exacerbate the I/O virtualization overhead when virtual machines access large amounts of data on shared storage. This paper presents XHive, an efficient cooperative caching system that is implemented at the virtualization layer, for consolidated environments. Our proposed scheme globally manages buffer caches of consolidated virtual machines in order to accommodate a shared working set in machine memory. A singlet, which is a block cached solely by a virtual machine, is preferentially given more chances to be cached in machine memory by XHive, when it is evicted by a guest operating system. For efficient use of limited memory, singlets are cached in memory that is collaboratively donated from idle memory of virtual machines. Our evaluation shows that XHive significantly reduces disk I/O operations for shared working sets, thereby achieving high read performance and scalability. Improved scalability enables a high degree of workload consolidation with respect to virtual machines that have shared working sets.

While modern datacenters are increasingly adopting virtual machines (VMs) to provide elastic cloud services, they still rely on traditional TCP for congestion control. In virtualized datacenters, TCP endpoints are separated by a virtualization layer and subject to the intervention of the hypervisor's scheduling. Most previous attempts focused on tuning the hypervisor layer to try to improve the VMs' I/O performance, and there is very little work on how a VM's guest OS may help the transport layer to adapt to the virtualized environment. In this paper, we find that VM scheduling delays can heavily contaminate RTTs as sensed by VM senders, preventing TCP from correctly learning the physical network condition. After giving an account of the source of the problem, we propose PVTCP, a ParaVirtualized TCP to counter the distorted congestion information caused by VM scheduling on the sender side. PVTCP is self-contained, requiring no modification to the hypervisor. Experiments show that PVTCP is much more effective in addressing incast congestion in virtualized datacenters than standard TCP.

SummaryBurst is a common pattern in the user's requirements, which suddenly increases the workload of virtual machines (VMs) and reduces the performance and energy efficiency of cloud computing systems (CCS). Virtualization technology with the ability to migrate VMs attempts to solve this problem. By migration, VMs can be dynamically consolidated to the users' requests. Burst temporarily increases the workload. Ignoring this issue will lead to incorrect decisions regarding the migration of VMs. It increases the number of migrations and Service Level Agreement Violations (SLAVs) due to overload. This may cause waste of resources, increase in energy consumption, and misplaced VMs. Therefore, a burst‐aware method for these issues is proposed in this paper. The method consists of two algorithms: one for determining the migration time and the other for the placement of VMs. We use the PlanetLab real dataset and CloudSim simulator to evaluate the performance of the proposed method. The results confirm the advantages of the method regarding performance compared to benchmark methods.

Cloud security is one of the biggest challenge in today's technological world. Researchers have proposed some solutions for cloud security. Virtual Machine (VM)-level solutions are configured and controlled at VM. They are less robust and can be easily subverted by attackers. In this paper, we propose an out-VM monitoring security approach named as Malicious Network Packet Detection (MNPD) which monitors the VMs from outside at both network and virtualization layer in cloud environment. MNPD performs the behavioral analysis of network traffic at Cloud Networking Server (CNS); providing primary defense from intrusions at network level. MNPD does the VM traffic validation at hypervisor of Cloud Compute Server (CCoS) to detect spoofing attacks, originated from VMs. The non-spoofed packets are further analyzed using behavior analysis of network traffic to detect any abnormality in the virtual traffic; providing second level of defense from intrusions at virtualization level. MNPD employs statistical learning technique (Random Forest) with ensemble of feature selection approach to learn the behavior of traffic patterns. MNPD does not involve overhead incurred in monitoring extensive memory writes or instruction-level traces. It is a more secure solution to detect attacks which never pass through physical interface and hence not detected by traditional IDS. The proposed approach has been validated with latest datasets (UNSW-NB and ITOC) and results seem to be promising.

Cloud Computing is a flexible, cost-effective, and proven delivery platform for providing information technology services over the Internet. However, cloud computing presents an added level of risk because essential services are often outsourced to a third party, which makes it harder to maintain data security and privacy, support data and service availability, and demonstrate compliance. While existing cloud computing threat detection and prevention techniques are improving, they are still not effective enough to meet the needs of next-generation systems, such as secure dynamic resource pools, high usability, and secure virtual machine migration. This paper aims to provide an understanding of the different potential attack vectors created by virtualization in a public Infrastructure-as-a-Service cloud, in particular, the security associated with the live virtual machine migration of jobs. The vectors will be investigated, concentrating on the threats emerging from various potential attackers (internal/external) to cloud systems. The overarching aim of this study, therefore, is to design and develop a secure live virtual machine migration framework that enables a virtual trusted platform module for multiple virtual machines running on a hardware platform while maintaining the cloud system's integrity.

The key computing element as in cloud data center, virtual machines should be able to migrate from one location to another to meet the necessities of the cloud users and the defined policies of the cloud computing system. The portability is an important issue when a virtual machine migrates across IP subnets. Its focuses on the portability management in cloud computing systems, and proposes a portability- oriented cloud data center network architecture based on the identity decoupling method of the portability-driven networks. In cloud data center network, a mobile node refers to a virtual machine, and the portability behavior mainly refers to virtual machine migration. In the proposed architecture, a virtual machine could implement live migration between IP subnets without service interruption. Evaluation shows that the proposed scheme can solve portability issues effectively in virtual machine migration among IP subnets.  

This paper addresses the impact of Virtual Memory Streaming (VMS) technique in provisioning virtual machines (VMs) in cloud environment. VMS is a scaling virtualization technology that allows different virtual machines rapid scale, high performance, and increase hardware utilization. Traditional hypervisors do not support true no-downtime live migration, and its lack of memory oversubscription can hurt the economics of a private cloud deployment by limiting the number of VMs on each host. VMS brings together several advanced hypervisor memory management techniques including granular page sharing, dynamic memory footprint management, live migration, read caching, and a unique virtual machine cloning capability. An architecture model is described, together with a proof-of-concept implementation, that VMS dynamically scaling of virtualized infrastructure with true live migration and cloning of VMs. This paper argues that VMS for Cloud allows requiring significantly reduced server memory and reducing the time for virtualized resource scaling by instantly adding more virtual machines.

Cloud computing is deployed a large set of virtualized computing resources in different infrastructures and various development platforms. One of the significant issues in cloud computing system is the scheduling of virtual resources and virtual machines (VMs). To address this issue, this paper proposed an efficient approach for virtual machines scheduling in VM management also called EVMSA (Efficient Virtual Machines Scheduling Algorithm) that provides the effective and efficient resource allocation. The proposed approach is going to test the evaluation on Eucalyptus open source private cloud architecture. The contributions of this paper are: the proposed scheduling algorithm will provide to improve the resource utilization such as CPU, Memory, and Disk. Then it will also minimize the turnaround time of VMs.

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.

Hardware virtualization is a major component of large scale server and data center deployments due to their facilitation of server consolidation and scalability. Virtualization, however, comes at a high cost in terms of system main memory utilization. Current virtual machine (VM) memory management solutions impose a high performance penalty and are oblivious to the operating regime of the system. Therefore, there is a great need for low-impact VM memory management techniques which are aware of and reactive to current system state, to drive down the overheads of virtualization. We observe that the host machine operates under different memory pressure regimes, as the memory demand from guest VMs changes dynamically at runtime. Adapting to this runtime system state is critical to reduce the performance cost of VM memory management. In this paper, we propose a novel dynamic memory management policy called Memory Pressure Aware (MPA) ballooning. MPA ballooning dynamically allocates memory resources to each VM based on the current memory pressure regime. Moreover, MPA ballooning proactively reacts and adapts to sudden changes in memory demand from guest VMs. MPA ballooning requires neither additional hardware support, nor incurs extra minor page faults in its memory pressure estimation. We show that MPA ballooning provides an 13.2% geomean speed-up versus the current ballooning techniques across a set of application mixes running in guest VMs; often yielding performance nearly identical to that of a non-memory constrained system.

Energy consumption is a primary concern for datacenters' management. Numerous datacenters are relying on virtualization, as it provides flexible resource management means such as virtual machine (VM) checkpoint/restart, migration and consolidation. However, one of the main hindrances to server consolidation is physical memory. In nowadays cloud, memory is generally statically allocated to VMs and wasted if not used. Techniques (such as ballooning) were introduced for dynamically reclaiming memory from VMs, such that only the needed memory is provisioned to each VM. However, the challenge is to precisely monitor the needed memory, i.e., the working set of each VM. In this paper, we thoroughly review the main techniques that were proposed for monitoring the working set of VMs. Additionally, we have implemented the main techniques in the Xen hypervisor and we have defined different metrics in order to evaluate their efficiency. Based on the evaluation results, we propose Badis, a system which combines several of the existing solutions, using the right solution at the right time. We also propose a consolidation extension which leverages Badis in order to pack the VMs based on the working set size and not the booked memory.

This paper advocates virtualization technology as a methodology to solve the security problems that an organization has to face when contributes with its resources to a grid. In particular, this technology makes it possible to increase the overall security of any system by inserting a set of controls into the code that implements one virtual machine. In this way, a secure cooperation among virtual machine can be implemented. This generalizes the current approach that exploits virtualization only for the confinement of alternative programming environments resulting from the partitioning of a physical machine into a set of non-cooperating virtual machines. The ability to support cooperation among virtual machines may be used to define networks of cooperating virtual machines to execute distributed applications. The paper describes a general purpose approach to security based upon virtual networks of cooperating virtual machines and applies it to one of the most challenging problems: that of securing a grid environment

Virtualization has been used in cloud computing to build multiple Virtual Machines (VMs) on a single physical machine, which enables computing resources partition and improving the physical resources utilization efficiency. However, the use of virtualization brings new challenge in reliability modeling for cloud computing. For example, when server failure occurs, all VMs co-located on the server cannot be operated, which is a typical common cause failure (CCFs). Since the task is executed by VMs, the states of VMs decide whether cloud computing can accomplish the given task. This paper proposes a new reliability model for cloud computing considering both server failures and VMs failures. Both fault tree and state-space models are used to solve this problem, and to reduce the computational complexity, a simplified method is provided to reduce the number of states.

Derivative clouds, light weight application containers provisioned in virtual machines, are becoming viable and cost-effective options for infrastructure and software-based services. Ubiquitous dynamic memory management techniques in virtualized systems are centralized at the hypervisor and are ineffective in nested derivative cloud setups. In this paper, we highlight the challenges in management of memory resources in derivative cloud systems. Hypervisor caching, an enabler of centralized disk cache management, provides flexible memory or non-volatile memory management at the hypervisor to improve the resource usage efficiency and performance of applications. Existing hypervisor caching solutions have limited effectiveness in nested setups due to their nesting agnostic design, centralized management model and lack of holistic view of memory management. We propose DoubleDecker, a decentralized disk caching framework, realized through guest OS and hypervisor cooperation, with support for efficient memory management in derivative clouds. The DoubleDecker hypervisor caching framework, an integral part of our proposed solution, provides interfaces for differentiated cache partitioning and management in nested setups and is equipped to handle both memory and SSD based caching stores. We demonstrate the flexibility of DoubleDecker to handle dynamic and changing memory provisioning requirements and its capability to simultaneously provision memory across multiple levels. Such multi-level configurations cannot be explored by centralized designs and are a key feature of DoubleDecker. Our experimentation with DoubleDecker demonstrates that application performance can be consistently improved due to the flexible policy framework for disk caching. With our setup, we report an average performance improvement of 4x and a maximum of 11x.