A secure virtual machine migration using memory space prediction for cloud computing

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.

In recent years, the adoption of multi-cloud environments has increased due to its ability to provide better performance, cost-effectiveness, and higher availability. However, managing resources in such environments is a challenging task. Load balancing and virtual machine (VM) migration are two critical techniques for resource management in multi-cloud environments. Load balancing helps to distribute the workload evenly among the cloud resources, while VM migration enables the dynamic allocation of resources based on the changing demands. This systematic review paper presents a comprehensive survey of load balancing and VM migration strategies in multi-cloud environments. The paper first introduces the concept of multicloud environments and the challenges of resource management in such environments. Then, the paper discusses the different load balancing and VM migration techniques that have been proposed for multi-cloud environments, including their advantages and limitations.

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

Virtualization techniques effectively handle the growing demand for computing, storage, and communication resources in large-scale Cloud Data Centers (CDC). It helps to achieve different resource management objectives like load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing through Virtual Machine (VM) migration. VM migration is a resource-intensive procedure as VM’s continuously demand appropriate CPU cycles, cache memory, memory capacity, and communication bandwidth. Therefore, this process degrades the performance of running applications and adversely affects efficiency of the data centers, particularly when Service Level Agreements (SLA) and critical business objectives are to be met. Live VM migration is frequently used because it allows the availability of application service, while migration is performed. In this paper, we make an exhaustive survey of the literature on live VM migration and analyze the various proposed mechanisms. We first classify the types of Live VM migration (single, multiple and hybrid). Next, we categorize VM migration techniques based on duplication mechanisms (replication, de-duplication, redundancy, and compression) and awareness of context (dependency, soft page, dirty page, and page fault) and evaluate the various Live VM migration techniques. We discuss various performance metrics like application service downtime, total migration time and amount of data transferred. CPU, memory and storage data is transferred during the process of VM migration and we identify the category of data that needs to be transferred in each case. We present a brief discussion on security threats in live VM migration and categories them in three different classes (control plane, data plane, and migration module). We also explain the security requirements and existing solutions to mitigate possible attacks. Specific gaps are identified and the research challenges in improving the performance of live VM migration are highlighted. The significance of this work is that it presents the background of live VM migration techniques and an in depth review which will be helpful for cloud professionals and researchers to further explore the challenges and provide optimal solutions.

Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

Virtualization has been shown to be an attractive path to increase overall system resource utilization. The use of live virtual machine (VM) migration has enabled more effective sharing of system resources across multiple physical servers, resulting in an increase in overall performance. Live VM migration can be used to load balance virtualized clusters. To drive live migration, we need to be able to measure the current load imbalance. Further, we also need to accurately predict the resulting load imbalance produced by any migration.In this paper we present a new metric that captures the load of the physical servers and is a function of the resident VMs. This metric will be used to measure load imbalance and construct a load-balancing VM migration framework. The algorithm for balancing the load of virtualized enterprise servers follows a greedy approach, inductively predicting which VM migration will yield the greatest improvement of the imbalance metric in a particular step. We compare our algorithm to the leading commercially available load balancing solution - VMware's Distributed Resource Scheduler (DRS). Our results show that when we are able to accurately measure system imbalance, we can also predict future system state. We find that we can outperform DRS and improve performance up to 5%. Our results show that our approach does not impose additional performance impact and is comparable to the virtual machine monitor overhead.

Memory management is fundamental to the performance of all applications. On modern server architectures, an application's memory allocator needs to balance memory utilization against the ability to use 2MB huge pages, which are crucial for achieving high performance. This paper shows that prior C++ memory allocators are fundamentally limited because optimizing this trade-off depends on the knowledge of object lifetimes, which is information allocators lack. We introduce a two-step approach to attain high memory utilization in huge pages. We first introduce a novel machine-learning approach that predicts the lifetime of freshly allocated objects using the stack trace at the time of allocation and treats stack traces as natural language. We then present a fundamentally new type of memory allocator that exploits (potentially incorrect) object lifetime predictions to achieve high memory utilization at full huge page usage. In contrast to prior memory allocators that organize their heap around size classes and free lists, our allocator organizes the heap based on predicted lifetime classes and adjusts to mispredictions on the fly. We demonstrate experimentally that this learned lifetime-aware memory allocator (LLAMA) reduces fragmentation with huge pages by up to 78%. Our approach gives rise to a new methodology for applying ML in computer systems. In addition, similar space-time bin packing problems abound in computer science and we discuss how this approach has applications beyond memory allocation to a wide range of problems.

Reconfigurations of virtual networks in cloud environments are enabled by virtual machine (VM) migration technologies. A lot of research work has focused on the implementation of VM migration (such as pre/post-copy techniques) and the determination of the destination hosts for the VM placement. The latter is often related to virtual network embedding (VNE) because it entails the mapping of virtual network functions and traffic flows on the physical substrate. However, a lack of consideration of VNE in the research on VM migration (and vice versa) still prevails. We consider this joint problem and observe that when one regards this as a multi-step model, remarkable benefits in terms of migration time, migration traffic volume, and even migration feasibility are obtained. In this paper, we present a heuristic algorithm to accelerate the solution and show that high-quality solutions can be achieved in polynomial time with suitable parameter settings.

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.

This paper shows that performance of the virtualized cluster servers could be improved through intelligent decision over migration time of Virtual Machines (VM) between physical nodes. Virtualization technology has lots of benefits. Via virtualization, Physical server can be consolidated over fewer virtual servers. This approach has many advantages such as less hardware cost, cooling cost, etc. One of the key benefits is better load balancing by using VM migration. To migrate we need to decide which virtual machine needs to be migrated and when this relocation has to be done. Our paper tries to propose a new model of load balancing in virtualized clusters based on Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) as one of the most efficient Multi Criteria Decision Making methods (MCDM). The cluster is assumed heterogeneous; therefore lots of parameters like CPU clock speed (CPU), CPU usage (%CPU), RAM capacity (RAM), and network bandwidth (NET) are used in decision process.

Wireless Sensor Network (WSN) is a promising approach for a variety of applications. Because of limitation of energy resource, memory space and processing capability of sensor nodes, it is very difficult to implement IP-based routing protocols in WSN. Recently, many research focus on developing special routing protocols for WSNs with the main design criteria: energy efficiency, load balance and reliability. The most well known routing protocol class is hierarchical which divides network into many clusters. Each cluster is represented by a cluster head (CH) that is responsible for receiving data from all non-CH members, aggregating these data and sending to the base station (BS). In order to balance energy consumption of the entire network, CHs are not fixed but rotated. Therefore, the hierarchical protocols can decrease and balance the energy consumption and prolong network lifetime. LEACH is one of the first cluster-based for WSN, which includes distributed cluster formation. In this paper, we propose a new hierarchical routing protocol (called EL-LEACH: Energy and Load balance LEACH) which achieves energy efficiency and load balance. Our simulation result shows that our new scheme EL-LEACH achieves better energy consumption, load balance and network life time than other well known LEACH protocols.

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.

Cloud computing is the use of remote servers on the internet to store, manage, and process data. It is a demand-based service where users need to pay only for what they use. Cloud computing users are extensively distributed throughout the globe, so it is a big challenge to keep track of this huge data. Load balancing is the distribution of workloads in a smart way among multiple compute resources, like virtual servers. Compute resources can be added or removed from the load balancer according to the needs of the user. A load balancer is primarily used to optimize the use of resources, costs, and VMs, as well as to maximize throughput, reduce response time, and prevent overloading in various VMs. In this paper, multi-agent-based virtual machine migration has been proposed for dynamic load balancing in a cloud computing environment. The proposed algorithm shows better results in terms of makespan time, average response time, and data center processing time than other conventional cloud load balancing algorithms.

Virtual machine (VM) migration is a technique for transferring the execution state of a VM from one physical host to another. While VM migration is critical for load balancing, consolidation, and server maintenance in virtualized data centers, it can also increase security risks. During VM migration, an attacker with sufficient privileges can compromise a VM by modifying its memory contents during transit to subvert its applications or the guest operating system. One could maintain dedicated, and presumably more secure, control networks to carry the migration traffic, but at significant hardware and administrative complexity. Alternatively, one could encrypt the migration traffic, which eliminates the need for dedicated control networks, but might introduce performance overheads. To date, there has been no systematic study of how encryption affects VM migration, especially in high-bandwidth low-delay networks that are common within data centers. In this paper, we present a study of the impact of AES and 3DES encryption algorithms on two widely used live VM migration approaches -- pre-copy and post-copy. Our key findings are as follows. The encryption algorithm used can have a significant impact on the total migration time. The impact of encryption on downtime varies with the type of the migration technique. The overhead of encryption also depends upon the relative speeds of source and target machines. Finally, an application's performance within a VM during encrypted migration varies with the type of the application and the migration mechanism.

This paper presents a comparative study involving SHA-3 final round candidates along with recent versions of hash algorithms. The proposed comparison between hash functions is performed with respect to cycles per byte and memory space. Tests are also carried out on a PIC32-based application taking into account several input cases, thus resulting in a set of ranked algorithms in terms of their specified metrics. The outcome of this work represents a considerable contribution in data protection and information security in relation to various communication and transmission systems, serving as a handy reference for developers to select an appropriate hash algorithm for their particular use condition.