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In smart communities, data are aggregated, and various services are provided based on it. When considering the optimization of service delivery through cooperative processing between the edge and the cloud in smart communities, it is essential to realize network transparent edge services due to the constraints of not touching end devices in order to improve user convenience and additional cost reduction. Moreover, considering the minimization of service provision and service delivery latency at the edge, it is desirable to use field‐programmable gate arrays (FPGAs), which have lower power consumption, lower latency, and higher throughput than conventional processors. Additionally, to advance towards even lower latency, the distribution of processing load is important, and service migration is necessary for this purpose. Service migration is generally used to increase service availability, and even for edge services, service migration is important because the distribution of processing and the location where the service should be provided changes over time due to user mobility. When considering network transparency, service migration should be performed on the communication path between the end device and the cloud. Furthermore, to enhance service transparency, it is essential to maintain communication consistency for continuous service provision and minimize service downtime. Therefore, this study proposes a migration mechanism with FPGAs to satisfy these requirements. As an example of a real application, we target a network‐transparent data anonymization application to protect the privacy of data providers and evaluate the proposed architecture based on the reality of latency and other factors. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Cloud computing has grown significantly in recent years and is now widely used by both individuals and businesses. Cloud service providers need to be prepared for failures to ensure that they can respond to them appropriately by minimizing downtime and achieving business continuity. This can be accomplished through fault tolerance mechanisms which aim to predict, prevent, and recover from failures. One of the common causes of task failures in the cloud is the deterioration of physical machines. If deterioration is detected in the physical machine, the virtual machines (VMs) being hosted must be moved to healthy physical machines. This paper proposes an approach to VM allocation in the event of host deterioration or failure. Namely, an integer linear programming optimization model is proposed for the allocation of the failing VMs while minimizing VM migration time. In addition, hybrid genetic and simulated annealing (HGA and HSA) algorithms are proposed for providing efficient solutions for the optimization model. The HGA is hybridized by a procedure that penalizes infeasible solutions to reduce their chances of being selected in the offspring populations and a hill-climbing procedure that modifies feasible solutions to produce better (or more fit) ones. The HSA starts from a feasible solution and also uses elitism as the HGA in its search to ensure that good candidate solutions are preserved. To validate the quality of the obtained solutions, we compare our approach to the commercial optimization solver CPLEX. In comparison to the CPLEX solver, the results show that for small size data center problems, the proposed HGA and HSA achieve near-optimal solutions with 70.80% and 81.43% increase in speed with an average solution quality trade-off by 20.57% and 78.52%, respectively. Notably, for medium, large and very large size problems the HGA and HSA obtained solutions where the CPLEX solver could not provide solutions within acceptable time. For medium size problems HGA attains solutions with better quality (75.82%) yet with higher execution time (57.27%). For large and very large problems HGA significantly outperforms HSA in terms of solution quality (better by 84.88% and 86.46%) yet with significantly higher execution time (74.22% and 76.55 %), respectively. Thus, the designer may choose which algorithm to use based on whether the focus is on quality or execution time.

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded and underloaded hosts is of fundamental importance when energy consumption, quality of services, and service level agreements are targeted. In this paper, an energy-aware QoS-based consolidation algorithm is proposed to dynamically manage VMs in cloud datacenters. The proposed algorithm applies reinforcement learning and artificial neural networks. The first method is used to select a suitable VM for migration, while the latter helps to predict the future state of hosts and detect overloaded and underloaded hosts. We simulated the proposed algorithm using the CloudSim framework and compared it to the baselines and state-of-the-art algorithms. The results show that the proposed approach surpasses other methods in what concerns both performance and energy efficiency. [ABSTRACT FROM AUTHOR]

The article introduces an innovative framework called Kororā, which aims to enhance the security and integrity of live virtual machine migration in a public cloud computing environment. The framework incorporates a trusted platform module to ensure the integrity of the migration process. It offers a new approach for virtual machine migration and has been specifically designed and implemented on a public infrastructure-as-a-service cloud platform.The primary research problem identified is the vulnerability of virtual machine instances to attacks during the live migration procedure. The evaluation used involves running the framework simultaneously on the same hardware components (such as I/O, CPU, and memory) and utilizing the same hypervisor's platform (Xen's open-source hypervisor). In addition, the security aspect of live migration is a crucial consideration due to the possibility of security threats across different area: data plane, control plane, and migration plane. Potential attackers may employ both passive and active attack techniques, putting the live migration at risk and resulting in a decline in performance. This poses a significant and alarming risk to the overall platform.To address the research gap, the Kororā framework emerged as a successful approach for achieving control-flow integrity by incorporating the Clark-Wilson security model proved effective in bridging the research gaps while maintaining system integrity. The primary achievement of this research is the introduction of the Kororā framework, which consists of seven agents operating within the Xen-privileged dom0 and establishing communication with the hypervisor. Overall, the finding indicate that the suggested framework offers an effective defence mechanism for moving a virtual machine from one host to another host with minimal disruption to normal operation with enhanced integrity.

- KVM (Kernel-based Virtual Machine) is one of the popular, free and open-source virtual machine managers for Linux distributions. Many people or companies use plain KVM virtualization, but in many cases, they have no measures in place for ensuring availability if a host fails. The existing method to set up a high availability cluster using basic KVM virtualization is a difficult procedure for someone unfamiliar with the process. A growing number of powerful strategies are furnished with network interconnection and are related to the Internet of Things (IoT). In this research work proposes a simple method for ensuring the high availability of KVM hypervisor with the help of the nested virtualization technique. In the proposed work, the KVM hypervisor will run on another high availability enabled hypervisor. For the performance evaluation, the parameters like CPU performance, I/O performance and memory performance were evaluated. That got the better results in the proposed system than others. The proposed evaluation demonstrates that the overhead in relations of network throughput and CPU accomplishment is low so it related that the proposed concept is a evocative step in the direction of additional resilient forthcoming IoT devices.

The growing demand for cloud services from users, organizations are planning to build more and more data centers to meet the demand. This is expected to increase energy consumption, which in turn will lead to an increase in operating costs and CO2 emissions. Virtual machine live migration technology can be used to reduce the amount of energy consumed by virtual machines. By doing so, virtual machines can be migrated from one server to another and therefore cause a delay in delivering the services to the users. Service Level Agreement (SLA) violation is one of the important parameters to be considered for better utilization of cloud services. By applying the proposed algorithm, SLA violations will be reduced through the process of selecting the host to which the Virtual Machine will be migrated according to the remaining power of the host, and thus the SLAs will be more enforced in the long run. The proposed algorithm has been implemented in CloudSim. In the comparison of the results achieved with the existing algorithms and the proposed algorithm, it is found that the proposed algorithm is capable of reducing the violation of the SLA by 13.6% when compared to the existing algorithms.

Cloud Radio Access Networks (Cloud-RANs) have recently emerged as a promising architecture to meet the increasing demands and expectations of future wireless networks. Such an architecture can enable dynamic and flexible network operations to address significant challenges, such as higher mobile traffic volumes and increasing network operation costs. However, the implementation of compute-intensive signal processing Network Functions (NFs) on the General Purpose Processors (General Purpose Processor) that are typically found in data centers could lead to performance complications, such as in the case of overloaded servers. There is therefore a need for methods that ensure the availability and continuity of critical wireless network functionality in such circumstances. Motivated by the goal of providing highly available and fault-tolerant functionality in Cloud-RAN-based networks, this paper proposes the design, specification, and implementation of live migration of containerized Baseband Units (BBUs) in two wireless network settings, namely Long Range Wide Area Network (LoRaWAN) and Long Term Evolution (LTE) networks. Driven by the requirements and critical challenges of live migration, the approach shows that in the case of LoRaWAN networks, the migration of BBUs is currently possible with relatively low downtimes to support network continuity. The analysis and comparison of the performance of functional splits and cell configurations in both networks were performed in terms of fronthaul throughput requirements. The results obtained from such an analysis can be used by both service providers and network operators in the deployment and optimization of Cloud-RANs services, in order to ensure network reliability and continuity in cloud environments.

Checkpoint/Restore techniques had been thoroughly used by the High Performance Computing (HPC) community in the context of failure recovery. Given the current trend in HPC to use containerization to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads, as well as efficient and reliable sharing and management of HPC Cloud infrastructures, there is a need to integrate Checkpoint/Restore with containerization in such a way that the freeze time of the application is minimal and live migrations are practicable. Whereas current Checkpoint/Restore tools (such as CRIU) support several options to accomplish this, most of them are rarely exploited in HPC Clouds and, consequently, their potential impact on the performance is barely known. Therefore, this paper explores the use of CRIU's advanced features to implement diskless, iterative (pre-copy and post-copy) migrations of containers with external network namespaces and established TCP connections, so that memory-intensive and connection-persistent HPC applications can live-migrate. Our extensive experiments to characterize the performance impact of those features demonstrate that properly-configured live migrations incur low application downtime and memory/disk usage and are indeed feasible in containerized HPC Clouds. • We carry out fully-featured seamless live migrations of runC containers. • We minimize the downtime and disk utilization through diskless, iterative migrations. • We migrate containers with established TCP connections transparently to the clients. • We characterize the facets of live migrations in representative HPC benchmarks. [ABSTRACT FROM AUTHOR]

Live migration is an essential feature in virtual infrastructure and cloud computing datacenters. Using live migration, virtual machines can be online migrated from a physical machine to another with negligible service interruption. Load balance, power saving, dynamic resource allocation, and high availability algorithms in virtual data-centers and cloud computing environments are dependent on live migration. Live migration process has six phases that result in live migration cost. Several papers analyze and model live migration costs for different hypervisors, different kinds of workloads and different models of analysis. In addition, there are also many other papers that provide prediction techniques for live migration costs. It is a challenge for the reader to organize, classify, and compare live migration overhead research papers due to the broad focus of the papers in this domain. In this survey paper, we classify, analyze, and compare different papers that cover pre-copy live migration cost analysis and prediction from different angels to show the contributions and the drawbacks of each study. Papers classification helps the readers to get different studies details about a specific live migration cost parameter. The classification of the paper considers the papers' research focus, methodology, the hypervisors, and the cost parameters. Papers analysis helps the readers to know which model can be used for which hypervisor and to know the techniques used for live migration cost analysis and prediction. Papers comparison shows the contributions, drawbacks, and the modeling differences by each paper in a table format that simplifies the comparison. Virtualized Data-center and cloud computing clusters admins can also make use of this paper to know which live migration cost prediction model can fit for their environments. [ABSTRACT FROM AUTHOR]

Efforts were exerted to enhance the live virtual machines (VMs) migration, including performance improvements of the live migration of services to the cloud. The VMs empower the cloud users to store relevant data and resources. However, the utilization of servers has increased significantly because of the virtualization of computer systems, leading to a rise in power consumption and storage requirements by data centers, and thereby the running costs. Data center migration technologies are used to reduce risk, minimize downtime, and streamline and accelerate the data center move process. Indeed, several parameters, such as non-network overheads and downtime adjustment, may impact the live migration time and server downtime to a large extent. By virtualizing the network resources, the infrastructure as a service (IaaS) can be used dynamically to allocate the bandwidth to services and monitor the network flow routing. Due to the large amount of filthy retransmission, existing live migration systems still suffer from extensive downtime and significant performance degradation in cross-data-center situations. This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold, thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement (SLA). In this research, we analyzed and evaluated the findings observed through simulating different parameters, like availability, downtime, and outage of VMs in data center processes. This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine. [ABSTRACT FROM AUTHOR]

Soon after the proliferation of User-Equipments (UEs) that work with cloud computing, there was an explosion in the volume of generated data. Furthermore, emerging IoT applications, e.g. virtual reality, augmented reality, video streaming, intelligent transportation, and healthcare, require low latency and real-time data, processing and communication. Fog/Edge computing is a novel criterion in which distributed fog/edge nodes provide resources in close proximity to end devices due to their limited resources. Instead of sending large amounts of data to cloud servers, fog/edge nodes could use local resources to analyze, filter, and process data. Mobile Edge Computing (MEC) can significantly reduce processing delays and network traffic between UEs and servers, especially when user mobility is taken into account. This paper proposed a new approach to migration in fog/edge computing to reduce downtime. Our simulations demonstrate less downtime, packet loss, migration time, and latency compared to current techniques. [ABSTRACT FROM AUTHOR]

With the increasing demand for openness, flexibility, and monetization, the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) — the core network (CN) component of 4G LTE and 5G non-standalone mobile networks — reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration of these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. This paper’s first contribution is the proposal of a framework that enables migration of containerised virtual EPC components using an open-source migration solution which does not fully support the mobile network protocol stack yet. The second contribution is an experimental-based comprehensive analysis of live migration in two virtualization technologies — VM and Container — with the additional scrutinization on the container migration approach. The presented experimental comparison accounts for several system parameters and configurations: flavor (image) size, network characteristics, processor hardware architecture model, and the CPU load of the backhaul network components. The comparison reveals that the live migration completion time and also the end-user service interruption time of the virtualized EPC components is reduced approximately by 70% in the container platform when using the proposed framework.

Edge computing is a promising alternative to cloud computing for offloading computationally heavy tasks from resource-constrained mobile user devices. Placed at the edge of the network, edge computing is particularly advantageous to delay-limited applications for having a short distance to end-users. However, when a mobile user moves away from the service coverage of the associated edge server, the advantage gradually vanishes, increasing response time. Although service migration has been studied to address this problem focusing on minimizing the service downtime, both zero-downtime and the amount of traffic generated as a result of migration need further study. In this paper, an optimal live migration for containerized edge computing service is studied. This paper presents three zero-downtime migration techniques based on state duplication and state reproduction techniques, and then, proposes an optimal migration technique selection algorithm that jointly minimizes the response time and network traffic during migration. For validation and performance comparison, the proposed migration techniques are implemented on off-the-shelf hardware with Linux operating system. The evaluation results showed that compared with a naive migration, the optimal approach reduced the response time and network load by at least 74.75% and 94.79%, respectively, under considered scenarios.

Virtual Machines are the key technology in cloud computing. In order to upgrade, repair or service the physical machine where a Virtual Machine is hosted, a common practice is to live-migrate the Virtual Machine to a different server. This involves copying all the guest memory over the network, which may take a non-negligible amount of time. In this work, we propose a technique to speed up the migration time by reducing the amount of guest memory to be transferred with the help of the guest OS. In particular, during live-migration, an eBPF program is injected in the guest kernel to obtain, and send to the Virtual Machine Monitor, the list of guest page frames that are currently unused. The VMM can then safely skip these pages during the copy. We have integrated this technique in the live-migration implementation of QEMU (Bellard, 2005), and we show the effects of our work in some experiments comparing the results against the QEMU default implementation. [ABSTRACT FROM AUTHOR]

Cloud service providers employ virtualization technologies in order to deliver energy efficiency and optimize resource utilization in cloud datacenters. In this way, Virtual Machine (VM) consolidation is an efficient technique for reducing energy related costs and environmental concerns. This technique consists of placing as much as possible VMs on as less as possible Physical Machines (PM)s by means of live VM migration. However, VM consolidation approaches bring advantages, they cannot efficiently guarantee performance isolation of co-located VMs resulting in operational interference issues. Therefore, it is necessary to be aware of workload performance degradation issues during VMs consolidation in a datacenter. In this paper, we propose a novel joint profit and interference-aware scheduling scheme (PIAS) to efficiently consolidate VMs that host multi-tier application workloads in Infrastructure-as-a-Service (IaaS) cloud datacenter. The PIAS scheme considers profit and cost of power consumption, operational interference of VMs, resource utilization, and service level agreements (SLA)s as well as amount of transferred memory pages during live VM migration for consolidating VMs. In this regard, a stochastic dynamic programming is introduced to model the operational behavior of VM. Also, an optimization problem is introduced to maximize provider's profit and minimize the overall execution cost of application workloads. We simulate a series of experiments based on real workloads traces for different IaaS cloud datacenter configurations. The PIAS scheme on average provides better results than other competitors and the results demonstrate how our approach at least improves profit, energy efficiency, and service down time by 40%, 29%, and 35%, respectively. [ABSTRACT FROM AUTHOR]

Live migration of virtual machines proves to be inexorable in providing load balancing among physical devices and allowing scalability and flexibility in resource allocation. The existing approaches exhibit different policies, distinct performance characteristics, and side effects such as power consumption and performance degradation. Therefore, determining the most optimal live migration algorithm in certain situations remains an open challenge. In this work, a new prediction-based model to manage the live migration process of VMs is introduced. Our adaptive model dynamically identifies the optimal live migration algorithm for a given performance metric based on a prior diagnosis of the system. The model is developed by considering the assumption of different workloads alongside certain resource constraints for any of the currently available migration algorithms. The proposed model consists of an ensemble-learning strategy that involves linear and non-parametric regression methods to predict six live migration key metrics, provided by the operator and/or the user, for each live migration algorithm. Our model allows considering the best combination which is constituted of the algorithm-metric pair to migrate a VM. The experimental results show that the proposed model allows to significantly alleviate the service level agreement violation rate by between 31 % and 60 % , along with decreasing the total CPU time required for the prediction process. [ABSTRACT FROM AUTHOR]

As a lightweight virtualization solution, container technology can provide resource-limiting capabilities and can run multiple isolated process sets under a single kernel instance. Multitenant preemption leads to competition in computing, storage, and network resources, resulting in degraded computing service performance. Virtualization service migration can provide a solution to the problem of resource shortage in supercomputing systems. However, the resource overhead and delay in the migration process also reduce the efficiency of high-performance computers. To solve this problem, this dissertation designs a migration tool. Firstly, this dissertation proposes a method of container migration. Secondly, this dissertation optimizes the startup of containers from checkpoints and proposes a multicontainer migration strategy, reducing the migration time by 30% compared to sequential migration. The migration method in this paper provides valuable experience for service migration in supercomputers and data centers. [ABSTRACT FROM AUTHOR]

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform. [ABSTRACT FROM AUTHOR]

With the striking rise in penetration of Cloud Computing, energy consumption is considered as one of the key cost factors that need to be managed within cloud providers' infrastructures. Subsequently, recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources, where the energy consumption and the operational costs areminimized. However, tomake better cost decisions in these strategies, the performance and energy awareness should be supported at both Physical Machine (PM) and Virtual Machine (VM) levels. Therefore, in this paper, a novel hybrid approach is proposed, which jointly considered the prediction of performance variation, energy consumption and cost of heterogeneous VMs. This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance, in which the power consumption and resource usage are utilized for estimating the VMs' total cost. Specifically, the service performance variation is handled by detecting the underloaded and overloaded PMs; thereby, the decision(s) is made in a cost-effectivemanner. Detailed testbed evaluation demonstrates that the proposed approach not only predicts theVMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation, with a high prediction accuracy on the basis of historical workload patterns. [ABSTRACT FROM AUTHOR]