Performance Of Virtual Machines Research Articles

Hotspot resolution in cloud computing: A Γ-robust knapsack approach for virtual machine migration

Cloud providers offer virtual machines (VMs) located on physical machines (PMs) to meet the increasing demand for computational services. When the instantaneous utilized capacities of VMs exceed a PM's threshold, hotspots form and degrade VM performance. To resolve hotspots, some VMs must be live migrated to other PMs, but selecting which VMs is challenging as their utilized capacities change continuously. We propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS) that applies Γ-robustness theory to ensure PMs are hotspot-safe with a desired probability. PMRS uses a “prediction + optimization” framework that first predicts VMs' future behaviors and then formulates the problem as a Γ-robust knapsack problem (Γ-RKP) solvable with a novel MILP model. Experiments with real-trace and synthetic data demonstrate PMRS's effectiveness. Moreover, we apply PMRS in a real production environment in Huawei Cloud, and observe significant benefits in resolving existing hotspots and 94%+ potential future hotspots with minimal migration cost.

A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing

Task scheduling in cloud computing is responsible for serving the user requirements. The scheduling strategy must handle the problems of high load over virtual machines (VMs), high-cost consumption and lengthier scheduling time effectively. The greatest challenge in the cloud computing environment is achieving the intended outcome of task scheduling under the uncertain user request demands as it is responsible for assigning specific resources to requests for achieving effective task completion. However, most of the task scheduling approaches contributed to the literature mainly focused on the design and development of scheduling algorithms but ignored to explore the impact of uncertain factors such as millions of instructions per second (MIPS) and network bandwidth during the scheduling process. In this paper, A Chameleon and Remora Search Optimization Algorithm (CRSOA) is proposed for achieving efficient scheduling process by exploring the impact of MIPS and network bandwidth which directly affects the virtual machine (VM) performance. Further the work includes the uncertainty factors of task completion rate, load balance, scheduling cost and makespan in a simultaneous manner during the process of scheduling. It is formulated a multi-objective cloud task scheduling optimization model by integrating the merits of Chameleon Search Algorithm (CSA) and Remora Search Optimization Algorithm (RSOA) using a greedy methodology for simulating the real cloud computing task scheduling process. The simulation results evidently confirmed that the proposed CRSOA approach is minimizing the completion time and effective in handling the load balancing between the available VMs against other competitive metaheuristic task scheduling algorithms. The experimental investigation of this CRSOA confirmed its predominance in minimizing the makespan by 18.96%, cost by 22.18%, and degree of imbalance by 20.54%, compared to the baseline approaches with different number of tasks and VMs.

A Novel Data Management Scheme in Cloud for Micromachines

In cyber-physical systems (CPS), micromachines are typically deployed across a wide range of applications, including smart industry, smart healthcare, and smart cities. Providing on-premises resources for the storage and processing of huge data collected by such CPS applications is crucial. The cloud provides scalable storage and computation resources, typically through a cluster of virtual machines (VMs) with big data tools such as Hadoop MapReduce. In such a distributed environment, job latency and makespan are highly affected by excessive non-local executions due to various heterogeneities (hardware, VM, performance, and workload level). Existing approaches handle one or more of these heterogeneities; however, they do not account for the varying performance of storage disks. In this paper, we propose a prediction-based method for placing data blocks in virtual clusters to minimize the number of non-local executions. This is accomplished by applying a linear regression algorithm to determine the performance of disk storage on each physical machine hosting a virtual cluster. This allows us to place data blocks and execute map tasks where the data blocks are located. Furthermore, map tasks are scheduled based on VM performance to reduce job latency and makespan. We simulated our ideas and compared them with the existing schedulers in the Hadoop framework. The results show that the proposed method improves MapReduce performance in terms of job latency and makespan by minimizing non-local executions compared to other methods taken for evaluation.

Exploring Performance Degradation in Virtual Machines Sharing a Cloud Server

Cloud computing has become a leading technology for IT infrastructure, with many companies migrating their services to cloud servers in recent years. As cloud services continue to expand, the issue of cloud monitoring has become increasingly important. One important metric to monitor is CPU steal time, which measures the amount of time a virtual CPU waits for the actual CPU. In this study, we focus on the impact of CPU steal time on virtual machine performance and the potential problems that can arise. We implement our work using an OpenStack-based cloud environment and investigate intrusive and non-intrusive monitoring methods. Our analysis provides insights into the importance of CPU steal time monitoring and its impact on cloud performance.

Investigating, quantifying and controlling the co-location attack’s conditional value at risk of VM placement strategies

Co-location attacks jeopardize data privacy and performance of virtual machines (VMs) in cloud computing, which can result in unexpected monetary losses that are not supported or covered by the investments made by users and providers. The uncertainty about the VM type (attacker, target or neutral) exacerbates the difficulty of the problem. However, existing methods, particularly VM placement strategies, fail to capture the risk of significant losses. The measures of risk have a crucial role in coping with the losses that might be incurred by the involved parties in finance or the insurance industry. In this paper, we introduce the notion of risk to the co-location resistant VM placement strategies. We develop a novel framework that assesses the co-location attack risk of an assignment returned by a specific VM placement strategy. A thorough analysis of multiple existing VM placement algorithms highlights the significance of incorporating this measure as it reveals a discernible correlation pattern with respect to co-location attack coverage and resource consumption. Based on these findings, we propose a new Approximate Co-Location Resistant Placement Strategy with Risk Constraints (ACLRPS-RC) to protect the involved parties from overwhelming security losses. The simulations on CloudSim using real workloads show the ability of the new strategy to meet a given level of risk while maintaining an adequate number of used servers.

A multi‐faceted analysis of the performance variability of virtual machines

AbstractCloud computing and virtualization solutions allow one to rent the virtual machines (VMs) needed to run applications on a pay‐per‐use basis, but rented VMs do not offer any guarantee on their performance. Cloud platforms are known to be affected by performance variability, but a better understanding is still required. This article moves in that direction and presents an in‐depth, multi‐faceted study on the performance variability of VMs. Unlike previous studies, our assessment covers a wide range of factors: 16 VM types from 4 well‐known cloud providers, 10 benchmarks, and 28 different metrics. We present four new contributions. First, we introduce a new benchmark suite (VMBS) that let researchers and practitioners systematically collect a diverse set of performance data. Second, we present a new indicator, called VI, that allows for measuring variability in the performance of VMs. Third, we illustrate an analysis of the collected data across four different dimensions: resources, isolation, time, and cost. Fourth, we present multiple predictive models based on machine learning (ML) that aim to forecast future performance and detect time patterns. Our experiments provide important insights on the resource variability of VMs, highlighting differences and similarities between various cloud providers. To the best of our knowledge, this is the widest analysis ever conducted on the topic.

Optimization of virtual machines performance using fuzzy hashing and genetic algorithm-based memory deduplication of static pages

The demand for memory capacity has increased, and cloud energy usage has soared. The performance and scalability of virtualization interfaces in cloud computing are hampered by a lack of sufficient memory. To figure out this problem, a technique defined as memory deduplication is widely used to reduce memory consumption utilizing the page-sharing method. However, this method of memory deduplication using KSM has significant drawbacks, such as overhead owing to many online comparisons, which will consume so many CPU resources. In this research, a modified approach of Memory Deduplication of Static Memory Pages (mSMD), which is based on the identification of similar applications by Fuzzy hashing and clustering them using the Hierarchical Agglomerative Clustering approach, followed by similarity detection between static memory pages based on Genetic Algorithm and details stored in Multilevel shared page table, both operations performed in offline and final memory deduplication is carried out during online, is proposed for achieving performance optimization in virtual machines by reducing memory capacity requirements. When compared to existing techniques, the simulation results indicate that the proposed approach mSMD efficaciously minimizes the memory capacity required while improving performance.

VM performance-aware virtual machine migration method based on ant colony optimization in cloud environment

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency.

Efficient online migration mechanism for memory write-intensive virtual machines

Online migration of virtual machines (VMs) is indispensable for system maintenance as it helps to achieve several resource management objectives such as load balancing, proactive fault tolerance, green operation, and resource management of data centers. The migration efficiency and reliability are two major challenges in the online migration of memory write-intensive VMs. For example, pre-copy migration transfers a large amount of data and takes a long time to migrate. This study proposes an efficient and reliable adaptive hybrid migration mechanism for memory write-intensive VMs. The mechanism optimizes the data transfer mode of the common migration method and improves the performance of conventional hybrid migration. First, the virtual machine (VM) memory data to be migrated are divided into dynamic and static data based on the bitmap marking method, and the migration efficiency is improved through parallel transmission based on different networks. Second, to accelerate the migration reliability, an iterative convergence factor is proposed to evaluate the current system load state and adaptively calculate the switching time of the migration mode for adaptive hybrid migration based on the convergence factor. Through adaptive hybrid migration can achieve migration completed successfully, shorten the post-copy migration duration, and minimize the impact on the performance of VMs. Finally, this paper implements the system prototype based on a kernel-based virtual machine (KVM), and experiments are performed using multiple memory write-intensive load VMs. The results show that the proposed migration algorithm can significantly improve migration performance and complete migration quickly to solve the pre-copy migration failure problem with a memory write-intensive load. Compared with the traditional hybrid migration with only one round of pre-copy, the proposed migration algorithm reduces the total migration time and transmits data by 23.2% and 26.7%, respectively.

Performance Analysis and Comparison of the Micro Virtual Machines Provided by the Top Cloud Vendors.

Public Clouds are the backbone of the modern IT Industry. The modern tech start-ups to are heavily dependent on the Public Clouds as save the capital expenditure. Public Clouds offer a wide range of Virtual Infrastructure beginning from single core 0.25 GB RAM virtual machines to 128 cored 3904 GB RAM Virtual Servers aided with GPUs. Apart from costing, The performance of the virtual machines can impact on the services offered. The paper tries to analyse the performance of the Light virtual machines provided by the different cloud vendors and maps their ability to handle certain set of tasks. The prime objective is to compare the entry level virtual machines that are mostly used across the industry for the multiple purposes and find which is the efficient and productive. The systems under Test are the 1 GB RAM machines provided by the most popular Cloud service providers.

EVMTracer: Dynamic Analysis of the Parallelization and Redundancy Potential in the Ethereum Virtual Machine

Ethereum is one of the first blockchains executing smart contracts (financial applications directly executed on the ledger using a virtual machine). The performance of the Ethereum virtual machine has become a bottleneck caused by high volumes of transactions due to blockchain applications, including decentralized finance and non-fungible tokens. Hence, there is a need to detail the execution characteristics of the Ethereum virtual machine before its performance can be improved. This work introduces an off-line Ethereum virtual machine tracer called EVMTracer that produces runtime data dependence graphs from executed transactions as an alternative program representation. From the runtime dependence graphs, we can deduce valuable metrics about program execution characteristics, including the degree of parallelization and computational redundancies in smart contracts. Our experiments encompass all blocks up to 12M on the Ethereum mainnet. We found a geometric mean of 1.90× theoretical maximum speedup when executing the smart contracts in parallel and identified 34.97% of SLOAD instructions as redundant.

Empirical analysis of machine learning-based energy efficient cloud load balancing architectures: a quantitative perspective

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

Convolution-LSTM-Based Mechanical Hard Disk Failure Prediction by Sensoring S.M.A.R.T. Indicators

The traditional Infrastructure as a Service (IaaS) cloud platform tends to realize high data availability by introducing dedicated storage devices. However, this heterogeneous architecture has high maintenance cost and might reduce the performance of virtual machines. In homogeneous IaaS cloud platform, servers in the platform would uniformly provide computing resources and storage resources, which effectively solve the above problems, although corresponding mechanisms need to be introduced to improve data availability. Efficient storage resource availability management is one of the key methods to improve data availability. As mechanical hard disk is the main way to realize data storage in IaaS cloud platform at present, timely and accurate prediction of mechanical hard disk failure and active data backup and migration before mechanical hard disk failure would effectively improve the data availability of IaaS cloud platform. In this paper, we propose an improved algorithm for early warning of mechanical hard disk failures. We first use the Relief feature selection algorithm to perform parameter selection. Then, we use the zero-sum game idea of Generative Adversarial Networks (GAN) to generate fewer category samples to achieve a balance of sample data. Finally, an improved Long Short-Term Memory (LSTM) model called Convolution-LSTM (C-LSTM) is used to complete accurate detection of hard disk failures and achieve fault warning. We evaluate several models using precision, recall, and Area Under Curve (AUC) value, and extensive experiments show that our proposed algorithm outperforms other algorithms for mechanical hard disk warning.

Dynamic resource provisioning and secured file sharing using virtualization in cloud azure

Virtual machines (VMs) are preferred by the majority of organizations due to their high performance. VMs allow for reduced overhead with multiple systems running from the same console at the same time. A physical server is a bare-metal system whose hardware is controlled by the host operating system. A physical server runs on a single instance of OS and application. A virtual server or virtual machine encapsulates the underlying hardware and networking resources. With the existing physical server, it is difficult to migrate the tasks from one platform to another platform or to a datacentre. Centralized security is difficult to setup. But with Hypervisor the virtual machine can be deployed, for instance, with automation. Virtualization cost increases as well as a decrease in hardware and infrastructure space costs. We propose an efficient Azure cloud framework for the utilization of physical server resources at remote VM servers. The proposed framework is implemented in two phases first by integrating physical servers into virtual ones by creating virtual machines, and then by integrating virtual servers into cloud service providers in a cost-effective manner. We create a virtual network in the Azure datacenter using the local host physical server to set up the various virtual machines. Two virtual machine instances, VM1 and VM2, are created using Microsoft Hyper-V with the server Windows 2016 R. The desktop application is deployed and VM performance is monitored using the PowerShell script. Tableau is used to evaluate the physical server functionality of the worksheet for the deployed application.The proposed Physical to Virtual to Cloud model (P2V2C) model is being tested, and the performance result shows that P2V2C migration is more successful in dynamic provisioning than direct migration to cloud platform infrastructure. The research work was carried out in a secure way through the migration process from P2V2C.

Cloud-BlackBox: Toward practical recording and tracking of VM swarms for multifaceted cloud inspection

Given the widening scope of the utilization and application of cloud computing services from general to mission-critical systems such as strategic military, financial, and the information systems of governmental agencies, the need for the development of improved methods to ensure the stability and security of cloud data and services is being increasingly emphasized. Various approaches have been developed to improve the security and stability of cloud infrastructure. In particular, the continuous inspection of the memory of Virtual Machine (VM) instances in the cloud platform has been an important factor in identifying the causes of security incidents related to zero-day vulnerabilities and critical system faults in cloud infrastructure. However, despite numerous studies in the field of continuous memory inspection, it is difficult to find a practical solution that is deployable in commercial-off-the-shelf cloud platforms. For instance, continuous memory snapshots generally cause various problems such as increased VM downtime occurrences, user-obstructive latency for memory snapshots, VM performance degradation, and massive data generation. To alleviate these limitations, we propose Cloud-BlackBox, which enables the recording of the memory of VM swarms running on cloud platforms that require a very high level of stability and security, and facilitates the flexible analysis of the recorded memory on a large-scale. A VM swarm refers to an environment in which multiple VMs are run in parallel. The proposed Cloud-BlackBox method provides the following benefits. First, by clustering VM swarm kernel memory, the amount of computation required to capture memory snapshots and the size of the generated snapshot images are minimized. Further, we propose a mechanism to merge kernel memory by rapidly identifying the homogeneity of the memory layout through analysis of the underlying base image and introspection of the running VM. The application of the proposed mechanism led to a storage reduction by a factor of 12.85. Second, a cognitive-scale bitmap was designed to track changes in the memory of VM swarms. The cognitive-scale bitmap is a mechanism that can dynamically manage the tracking of memory change information by recognizing the memory usage patterns of component VMs. With the designed cognitive-scale bitmap, the time required for the collection of a memory snapshot was reduced by more than 14.85 times, and the VM input/output (I/O) performance degradation was reduced by 50%. Third, a synchronized accessible memory interchange (SAMI) mechanism is proposed to facilitate the agile in-depth analysis of large-scale memory resources. Cloud-BlackBox tracks and records memory change information. Therefore, a procedure for restoring the recorded memory to a raw-memory analyzable form is required to analyze the recorded memory. The SAMI mechanism assists the analyst in ensuring consistent memory restoration performance when arbitrarily selecting recorded memory. Furthermore, SAMI is useful for reducing the scope of analysis without memory restoration simply by analyzing recorded metadata. Consequently, the revised schemes inside Cloud-BlackBox have several applications in various fields, such as advanced detection of malicious activities, service error recovery, malware analysis, and antivirus functions. In addition, the proposed approach has been implemented on a campus-wide cloud computing service called SysCore-Cloud.

Real-Time Performance Analysis and Tuning of Embedded System Virtualization Architecture Based on KVM

Embedded systems are proceeding towards exploiting virtualization technology to have the benefits of Real-Time Operating System (RTOS) and General-Purpose Operating System (GPOS) in the same system. This combination provides both a timely and deterministic behavior and a general-purpose application codebase. There still exist concerns about the real-time responsiveness of RTOS running inside a Virtual Machine (VM). In this paper, the real-time performance of Kernel-based Virtual Machine (KVM) virtualization architecture is analyzed on a multi-core system. Here, a preemptible Linux kernel with the PREEMPT_RT patch is used for RTOS, while a standard Linux kernel is used for GPOS. The interrupt latency inside the real-time guest VM is analyzed by applying various amounts of CPU, memory, and I/O stresses on the guest and host systems. A VM resource monitoring tool ‘VM_stat’ is developed to know the resource usage of the guest VMs, which is useful for effectively tuning the system. Different real-time tuning measures are applied on the host/guest systems and the performance is analyzed.

Real-Time Analysis of Multiple Root Causes for Anomalies Assisted by Digital Twin in NFV Environment

Network Function Virtualization (NFV) is a promising paradigm that enables the employment of novel service types with lower deployment cost and faster time-to-value, but it introduces new fault management problems and challenges. In NFV environment, anomalies occurred in virtual machines (VMs) can be caused by faulty components of their hosting servers or anomaly propagation from other ones. If the performance of VMs degrades, we need to find out the root causes accurately, i.e., locate the exact faulty components, to recover the networks as soon as possible. In this paper, we first use digital twin to establish a virtual instance of the physical network to capture the real-time anomaly-fault dependency relationship. When the network environment changes, transfer learning is leveraged to utilize the learned knowledge of dependency relationship in historical periods to avoid huge time and computation cost of learning from scratch. Assisted by the learned dependency relationship, a dynamic set-covering (DSC) based root caused analysis problem is formulated and modeled with a set of parallel hidden Markov models to capture the dynamics of component states, which can best explain the sequence of anomalous VMs. We use alternating direction method of multipliers to decompose the DSC problem into a set of independent sub-problems and solve it in a distributed fashion. Since the state variables of each component in the DSC problem are coupled between any two successive time epochs, each sub-problem is solved by the Viterbi decoding and an incremental function is designed to construct the feasible solution that covers all anomalous VMs. Simulation results show the availability and superiority of the digital-twin assisted root cause analysis algorithm for NFV environment.

Cloudlet and Virtual Machine Performance Enhancement With CLARA and Evolutionary Paradigm

The standardised IT paradigm pools services together as an internet network is cloud computing. So, management of the load by cloud providers at this point is difficult and hence manifests the existence of load balancing concept. The aim of proposed algorithm is to enhance the performance by minimizing results, which includes Execution time, Makespan time, and Processing Cost, and maximizing throughput, using ABC Optimization. R code is used to execute the algorithm, and dataset is processed using Microsoft Excel 2007. In the dataset, the MIPS of VMs range from 2000-9000 and bandwidth range from 10000-50000. Finally, it is concluded that, for 3 clusters, the efficiency rate of execution time, makespan time, and processing cost lies between 18%-20% and throughput and degree of imbalance are approximately 16% and 6%, respectively, when compared with the previous work; and for 10 clusters, the efficiency rate of execution time and makespan time raises to approximately 50% with processing cost, throughput, and degree of imbalance as approximately 72%, 33%, and 4%, respectively.

A high-applicability heterogeneous cloud data centers resource management algorithm based on trusted virtual machine migration

With the continuous development and maturity of cloud computing technology, the scale and number of cloud data center (CDC) are also expanding. This increasingly draws attention to the problem of high energy consumption in CDCs. Dynamic virtual machine (VM) consolidation is a promising approach for reducing energy consumption. VM migration, as a VM consolidation technology, can effectively improve the utilization of physical machine (PM) and optimize the scheduling process of CDCs. However, most VM integration algorithms, in existing research, are aimed at improving the utilization of PMs. Excessive utilization of PMs may increase the competition for shared resources among the VMs running on them. As a result, the performance of these VMs deteriorates, and the execution time of cloud tasks is increased or even interrupted. This study systematically analyzes the overall architecture of CDCs. Subsequently, migration rules are established for the one-dimensional and multidimensional trusted VMs. A high- applicability heterogeneous CDC resource management algorithm based on trusted VM migration (HTVM2) is then proposed. The proposed algorithm not only solves the one-dimensional VM migration problem of homogeneous and heterogeneous CDCs but also those of multi-dimensional VMs. This improves the success rate of VM migration, reduces the energy consumption of the CDC, and improves load balancing while ensuring VM performance. Finally, the algorithm was compared with the other three algorithms outperforming them all, as demonstrated by experimental results.

IConSnap: An Incremental Continuous Snapshots System for Virtual Machines

The reliability of data and services hosted on a virtual machine (VM) is a top concern in cloud environments. The Continuous Snapshots can reduce the data loss in case of failures and thus is prevailing for protecting long-running systems. However, existing methods suffer from long VM downtime, long snapshot interval and significant performance loss. In this paper, we present iConsnap, a system designed to take fine-grained continuous snapshots of virtual machines without compromising VM performance. First, iConsnap adopts the copy-on-write (COW) mechanism to save the memory pages on-demand, and thus decreases the downtime to about 200 milliseconds. Second, we extend the idea of COW and propose a lazily incremental approach to save the delta data between two successive snapshots only once, thereby reducing the snapshot duration and snapshot data a lot. Third, we propose several scheduling mechanisms to mitigate the VM performance penalty issue. Last, we introduce a method combined of compression and time-aware multi-granularity reclamation strategy to reduce the storage costs without losing performance and availability. We implement iConsnap on QEMU/KVM and evaluate it through a set of experiments. The results show that iConsnap outperforms existing approaches in terms of downtime, duration, storage costs and VM performance.