Live Migration Of Virtual Machines Research Articles

Optimised pre-copy live VM migration approach for evaluating mathematical expression by dependency identification

Live virtual machine (VM) migration is widely used technique for resource consolidation and fault tolerance. KVM and Xen use iterative pre-copy approaches which work well in practice for commercial applications. In this paper, we propose a novel idea for pre-copy live VM migration process and present a detailed performance analysis of the migration process. We show that the migration downtime can be reduced significantly when compared with the previously proposed algorithms. The main idea is to identify the independent instructions from the set of instructions that are still executing at source server. These independent instructions are transmitted to the destination server and start executing without waiting for source server to transmit all the instructions. We present a novel algorithm able to attain minimal downtime and minimal total execution time.

Location privacy attacks and defenses in cloud-enabled internet of vehicles

As one of the promising branches of the Internet of Things, the cloud-enabled Internet of Vehicles (CE-IoV) is envisioned to serve as an essential data sensing, exchanging, and processing platform with powerful computing and storage capabilities for future intelligent transportation systems. The CE-IoV shows great promise for various emerging applications. In order to ensure uninterrupted and high-quality services, a vehicle should move with its own VM via live VM migration to obtain real-time location-based services. However, the live VM migration may lead to unprecedented location privacy challenges. In this article, we study location privacy issues and defenses in CE-IoV. We first present two kinds of unexplored VM mapping attacks, and thus design a VM identifier replacement scheme and a pseudonym-changing synchronization scheme to protect location privacy. We carry out simulations to evaluate the performance of the proposed schemes. Numerical results show that the proposed schemes are effective and efficient with high quality of privacy.

Analyzing Virtual Machine Live Migration in Application Data Context

Virtualization plays a very vital role in the big cloud federation. Live and Real-time virtual machine migration is always a challenging task in virtualized environment, different approaches, techniques and models have already been presented and implemented by many re- searchers. The aim of this work is to investigate various parameters of Real-time and live data migration of virtual machines in stateful and data context at the application level. The migration of one virtual machine to another requires some time depending on the network bandwidth, guest availability, hardware limitation overcomes, resource allocation, server reallocation, hypervisor compatibility and many more. To enhance and ensure the performance and optimization of the time this work presents the some analysis in the form of different time stacks in multiple piece of data stored in the virtual machines. To optimize the migration time virtual machine checkpoints are used in order to achieve the better results by using the xen hypervisor memory technique which dynamically allows the migration of the configured memory while the allocated memory could be discarded for a while. By this the bad memory remains un-migrated only the good memory consisting the used data would be migrated by means of Real-time.

A Study on Security Vulnerability on Cloud Platforms

Cloud computing provides an in-built platform to the users with easy and on-demand access to different system level services for creating virtual machines (VM) with efficient utilization of hardware, computing and network resources. This allows users to remotely execute large number of applications across various domains such as, health-care, utility services, e-governance, etc. There are different cloud operating platforms for creation and management of virtual machines through various system or application services. For example, openstack, oracle virtualbox, Microsoft HyperV are widely used operating platform. Virtual machine migration (VMM) is one of such important system services that used for smooth the progress of system maintenance, load management, fault tolerance, secure and safe service offerings. Virtual Machine live migration service transfers an active VM from one physical machine to another across different data centres. It involves a sequence of operations in iteration for migrating execution context of a VM to the destination machine. These operations are dependent on schedule, availability of resources and overall timing constraints. On the other hand, the migration process involves transferring the control data over a communication channel with a shared storage. Due to the above mentioned complexities of VM migration process, any security failures during the migration may cause transfer of incorrect VM instance to a data centre and which in turn may lead to exposure of the data centre to the attackers. This may be manifested as catastrophic system failure, degradation of system performance, load balancing and in long run may result collapse of the data centre. In this paper we studied various security vulnerability in different cloud platforms. These vulnerability will help the security developers to design a secured cloud platform.

Management of Virtual Machine as an Energy Conservation in Private Cloud Computing System

Cloud computing is a service model that is packaged in a base computing resources that can be accessed through the Internet on demand and placed in the data center. Data center architecture in cloud computing environments are heterogeneous and distributed, composed of a cluster of network servers with different capacity computing resources in different physical servers. The problems on the demand and availability of cloud services can be solved by fluctuating data center cloud through abstraction with virtualization technology. Virtual machine (VM) is a representation of the availability of computing resources that can be dynamically allocated and reallocated on demand. In this study the consolidation of VM as energy conservation in Private Cloud Computing Systems with the target of process optimization selection policy and migration of the VM on the procedure consolidation. VM environment cloud data center to consider hosting a type of service a particular application at the instance VM requires a different level of computing resources. The results of the use of computing resources on a VM that is not balanced in physical servers can be reduced by using a live VM migration to achieve workload balancing. A practical approach used in developing OpenStack-based cloud computing environment by integrating Cloud VM and VM Placement selection procedure using OpenStack Neat VM consolidation. Following the value of CPU Time used as a fill to get the average value in MHz CPU utilization within a specific time period. The average value of a VM’s CPU utilization in getting from the current CPU_time reduced by CPU_time from the previous data retrieval multiplied by the maximum frequency of the CPU. The calculation result is divided by the making time CPU_time when it is reduced to the previous taking time CPU_time multiplied by milliseconds.

Survey on Mechanisms for Live Virtual Machine Migration and its Improvements

Survey on Mechanisms for Live Virtual Machine Migration and its Improvements

Survey on Mechanisms for Live Virtual Machine Migration and its Improvements

Survey on Mechanisms for Live Virtual Machine Migration and its Improvements

Enhancing Availability of Services Using Software-Defined Networking

The immense growth of client requirements imposed on data centre and cloud providers results in a conflict with traditional networking concepts lacking the required agility. In order to promote flexibility, which data centre providers promise to their clients, this discrepancy needs to be resolved, for instance by employing the novel concept of Software-Defined Networking (SDN). This paper utilises this concept in order to minimise service downtime while performing live virtual machine migration. The work is aimed at small/medium-sized data centres and hence the findings are based on real communication patterns found in such environments. Results show that packet loss is slightly diminished while available throughput is increased thanks to the proactive approach taken during network topology changes when compared to the traditional approach based on L2 forwarding.

Collaborative Agents for Distributed Load Management in Cloud Data Centers Using Live Migration of Virtual Machines

Load management in cloud data centers must take into account 1) hardware diversity of hosts, 2) heterogeneous user requirements, 3) volatile resource usage profiles of virtual machines (VMs), 4) fluctuating load patterns, and 5) energy consumption. This work proposes distributed problem solving techniques for load management in data centers supported by VM live migration. Collaborative agents are endowed with a load balancing protocol and an energy-aware consolidation protocol to balance and consolidate heterogeneous loads in a distributed manner while reducing energy consumption costs. Agents are provided with 1) policies for deciding when to migrate VMs, 2) a set of heuristics for selecting the VMs to be migrated, 3) a set of host selection heuristics for determining where to migrate VMs, and 4) policies for determining when to turn off/on hosts. This paper also proposes a novel load balancing heuristic that migrates the VMs causing the largest resource usage imbalance from overloaded hosts to underutilized hosts whose resource usage imbalances are reduced the most by hosting the VMs. Empirical results show that agents adopting the distributed problem solving techniques are efficient and effective in balancing data centers, consolidating heterogeneous loads, and carrying out energy-aware server consolidation.

An Optimization of Pre-copy Approach for Live Migration of Virtual Machines

An Optimization of Pre-copy Approach for Live Migration of Virtual Machines

LISP controller: a centralized LISP management system for ISP networks

SummaryAs the current Internet architecture is suffering from scalability issues, the network research community has proposed alternative designs for the Internet architecture. Among those solutions that adopt the idea of locator/identifier split paradigm, the locator/identifier separation protocol (LISP) has been considered as the most promising solution because of its incrementally deployable feature. Despite various advantages provided by LISP, many ISPs are still conservative to adopt LISP into their production network because the standard LISP does not fully satisfy ISP's requirements on LISP‐enabled services. In this paper, we define ISP's requirements on LISP‐enabled commercial services and describe limitations of the standard LISP from an ISP's perspective. Also, we propose LISP controller, a centralized LISP management system. By using LISP controller, we evaluate three ISP's representative LISP use cases: traffic engineering, virtual machine live migration, and vertical handover. The results show that the proposed LISP controller provides centralized management, controllability, and fast map entry update, without any modifications on the standard LISP. LISP controller allows an ISP to control and manage its LISP‐enabled services while satisfying ISP's requirements. Copyright © 2015 John Wiley & Sons, Ltd.

Minimum-energy bandwidth management for QoS live migration of virtual machines

Live virtual machine (VM) migration aims at enabling the dynamic balanced use of the networking/computing physical resources of virtualized datacenters, so to lead to reduced energy consumption. However, the bandwidth consumption and latency of current state-of-the-art live VM migration techniques still reduce the experienced benefits to much less than their potential. Motivated by this consideration, in this paper, we analytically characterize, prototype in software and test through field trials the optimal bandwidth manager for intra-datacenter live migration of VMs. The goal is the minimization of the migration-induced communication energy under service level agreement (SLA)-induced hard constraints on the total migration time, downtime, slowdown of the migrating applications and overall available bandwidth. For this purpose, after recognizing that the resulting (nonconvex) optimization problem is an instance of Geometric Programming, we solve it by resorting to suitably developed adaptive version of the so-called primal–dual gradient-based iterations and, then, we analytically characterize its feasibility conditions. Hence, we prototype the resulting bandwidth manager atop an intra-datacenter wired test-bed, and, then, test and compare its energy performance through extensive field trials. The carried out field trials point out that: (i) the energy savings attained by the proposed bandwidth manager over the state-of-the-art ones currently utilized by Xen, KVM and VMware hypervisors are over 40% and approach 66% under strict QoS constraints; (ii) the proposed bandwidth manager is capable to quickly adapt to the abrupt changes possibly experienced by the dirty rates of the running applications and/or the round trip times of the utilized (possibly, congested) TCP/IP connections; and, (iii) its actual implementation may be carried out in a distributed and scalable way, and it consumes less than 1.5% of the CPU computing power per migrated VM.

A new technique for efficient live migration of multiple virtual machines

Datacenter virtualization technologies have attracted a lot of attention to enable various cloud computing services and to facilitate virtual machine (VM) migration. VM migration can help service providers to achieve the goals of saving energy, enhancing resource efficiency and quality of service (QoS). In order to ensure the QoS, the migration time and the downtime of VM should be considered while implementing the VM migration. Most researches focus on the issue of single VM migration by using the post-copy migration strategy or pre-copy migration strategy. However, there is few research that focuses on the problem of live migration for multiple VMs. Therefore, in this paper, we first propose an improved serial migration strategy and introduce the post-copy migration scheme into it. We then propose the m mixed migration strategy that is based on the improved serial migration strategy and the parallel migration strategy. Furthermore, we develop queuing models (i.e., the M/M/C/C and the M/M/C queuing models) to quantify performance metrics, such as the blocking ratio and average waiting time of each migration request. We evaluate the performance of the proposed migration strategy by conducting mathematical analysis, the numerical results show that our proposed strategy outperforms the existing approach.

Cost-efficient live VM migration based on varying electricity cost in optical cloud networks

Information and Communication Technology (ICT) proliferation has led to widespread use of cloud-computing services. ICT constitutes a significant portion of the worldwide energy consumption, and reduction in operating energy expenditures is a high priority for cloud-service providers. Spatial and temporal variations in electricity price can be exploited to reduce the operating cost. One approach is to employ live VM migration for moving, at the right time of the day, virtualized workloads (i.e., VMs) towards those datacenters (DCs) with cheaper energy price. Our approach for cost-efficient VM migration based on varying electricity prices significantly reduces the energy cost to operate cloud services.

Decision-making framework for user-based inter-cloud service migration

Cloud computing has rapidly become the most effective computing paradigm for today’s increasingly technology-dependent society. The emerging concepts of federated clouds with support for interoperability between different cloud providers and open standards in cloud middleware have opened up new challenges in cloud service management. One of the emerging research areas in cloud computing is the possibility of live virtual machine migration between different clouds. This is of importance when the quality of a cloud service currently used by a user degrades or a new cloud service is developed which is better in terms of quality, performance and cost than the current service being used. In such scenarios, the user needs to make a decision as to whether to continue with the currently used service or migrate to the newly available service. In our previous work, we presented a decision-making approach that assists a cloud service user in selecting a cloud service provider based on the QoS of its services. In this paper, we extend our previous work in the pre-interaction time phase and discuss the decision-making process involved in the migration from one cloud service to another cloud service through inter-cloud virtual machine migration.

Use of proactive and reactive hotspot detection technique to reduce the number of virtual machine migration and energy consumption in cloud data center

The increasing demand of cloud computing motivates the researchers to make cloud environment more efficient for its users and more profitable for the providers. Though virtualization technology helps to increase the resource utilization, still the operational cost of cloud gradually increases mainly due to the consumption of large amount of electrical energy. So to reduce the energy consumption virtual machines (VM) are dynamically consolidated to lesser number of physical machines (PMs) by live VM migration technique. But this may cause SLA violation and the provider is penalized. So to maintain an energy-performance trade-off, the number of VM migration should be minimized. VM migration primarily takes place in two cases: for hotspot mitigation and to switch off the underutilized nodes by migrating all its VMs. If a host is found to be overloaded then instead of immediately migrating some of its VMs we can check whether the migration is really required or not. For this we have proposed a load prediction algorithm to decide whether the migration will be performed or not. After the decision has been taken the algorithm finds a suitable destination host where the VM will be shifted. For this we have proposed a novel approach to decide whether a particular host is suitable as destination depending on its probable future load. We have simulated our algorithms in CloudSim using real world workload traces and compared them with the existing benchmark algorithms. Results show that the proposed methods significantly reduce the number of VM migration and subsequent energy consumption while maintaining the SLA.

Improving consolidation of virtual machine based on virtual switching overhead estimation

In virtualized data centers, live virtual machine (VM) migration can increase energy efficiency by consolidating VMs on fewer servers. This problem is usually considered a Bin Packing Problem with server capacity constraints, such as CPU, memory and network bandwidth. In order to minimize the communication traffic within the data center network, existing research works used correlation-based strategy to consolidate VMs onto servers, which means that VMs with inter-traffic are consolidated as closely as possible, e.g. within a server or a rack. However, this strategy increases the traffic load of virtual switches on servers, and it causes a certain number of CPU cycles of servers to move traffic through virtual switches. A lack of consideration for the virtual switching overhead may increase the risk that VMs are not allocated enough resources, and consequently reduce VMs׳ performance. In this work, we conduct experiments to estimate the virtual switching overhead on server CPU resource, and based on the experiment results, we propose a virtual-switching-aware VM consolidation algorithm to address this problem. Experiments on representative data center workloads show that the overhead can occupy 10–30% of server׳s CPU resources. Additionally, our algorithm shows a much lower server capacity violation probability as compared with the baseline algorithm.

Anomaly detection and identification scheme for VM live migration in cloud infrastructure

Virtual machines (VM) offer simple and practical mechanisms to address many of the manageability problems of leveraging heterogeneous computing resources. VM live migration is an important feature of virtualization in cloud computing: it allows administrators to transparently tune the performance of the computing infrastructure. However, VM live migration may open the door to security threats. Classic anomaly detection schemes such as Local Outlier Factors (LOF) fail in detecting anomalies in the process of VM live migration. To tackle such critical security issues, we propose an adaptive scheme that mines data from the cloud infrastructure in order to detect abnormal statistics when VMs are migrated to new hosts. In our scheme, we extend classic Local Outlier Factors (LOF) approach by defining novel dimension reasoning (DR) rules as DR-LOF to figure out the possible sources of anomalies. We also incorporate Symbolic Aggregate ApproXimation (SAX) to enable timing information exploration that LOF ignores. In addition, we implement our scheme with an adaptive procedure to reduce chances of performance instability. Compared with LOF that fails in detecting anomalies in the process of VM live migration, our scheme is able not only to detect anomalies but also to identify their possible sources, giving cloud computing operators important clues to pinpoint and clear the anomalies. Our scheme further outperforms other classic clustering tools in WEKA (Waikato Environment for Knowledge Analysis) with higher detection rates and lower false alarm rate. Our scheme would serve as a novel anomaly detection tool to improve security framework in VM management for cloud computing.

Energy Efficient Multi-Level Network Resources Management in Cloud Computing Data Centers

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

Agent-based load balancing in Cloud data centers

Cloud data centers are generally composed of heterogeneous commodity servers hosting multiple virtual machines (VMs) with potentially different specifications and fluctuating resource usages. This may cause a resource usage imbalance within servers that may result in performance degradation and violations to service level agreements. This work proposes a collaborative agent-based problem solving technique capable of balancing workloads across commodity, heterogeneous servers by making use of VM live migration. The agents are endowed with (i) migration heuristics to determine which VMs should be migrated and their destination hosts, (ii) migration policies to decide when VMs should be migrated, (iii) VM acceptance policies to determine which VMs should be hosted, and (iv) front-end load balancing heuristics. The results show that agents, through autonomous and dynamic collaboration, can efficiently balance loads in a distributed manner outperforming centralized approaches with a performance comparable to commercial solutions, namely Red Hat, while migrating fewer VMs.