Efficient Virtual Machine Migration Research Articles

Secure Virtual Machine Migration and Host Overload Detection Using Modified Pelican Optimization with Variable Load Mean Function

Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system.

Pre-copy live virtual machine migration techniques in cloud computing using HDWHM algorithm

Live Virtual Machine (VM) migration is a legacy of server virtualization in cloud computing. One of the difficulties in a Virtual Machine (VM) Environment is load balancing, and the pre-copy migration strategy is both time-consuming and expensive. In this paper, we propose an efficient VM migration strategy to compute the parameters for Linear Adam Algorithm overload detection and Interquartile Range (IQR) under-loaded identification, the Central Processing Unit (CPU) utilization is sufficient. VMs require constant network traffic, storage, CPU, and memory demands. This study recommends using the novel Hamming Distance-Based Weighted Harmonic Mean (HDWHM) strategy for pre-copy live VM migration. We tested the suggested strategy using datasets from CloudSim-3.0.3 on a real PlanetLab. Simulations show that the suggested approach decreases host downtimes, energy consumption, VM migrations, and SLA degradation.

Live virtual machine migration: A survey, research challenges, and future directions

In recent years, cloud computing has emerged as a promising paradigm providing various resources in the Cloud Data Center (CDC), including computations, storage, and even platform as a service. The virtualization technology plays a pivotal role in CDC resources management and provides an enchanting feature of virtual machine (VM) migration which provides several benefits in terms of VM scheduling, fault tolerance, load balancing, energy efficiency, power management, and security. To achieve efficient VM migration, a plethora of VM migration schemes have been proposed in the literature aiming to serve the quality of service-driven user requirements. This paper surveys the most recent and state-of-the-art Artificial Intelligence (AI) based and conventional load balancing, energy-aware, SLA aware, and network-aware live VM migration schemes. Through an extensive literature review, this paper investigates the most critical aspects of conventional and AI-driven live VM migration schemes. Finally, a few open research challenges that require further consideration from the research community are highlighted.

Energy efficient virtual machine migration approach with SLA conservation in cloud computing

In the age of online workload explosion, cloud users are increasing exponentialy. Therefore, large scale data centers are required in cloud environment that leads to high energy consumption. Hence, optimal resource utilization is essential to improve energy efficiency of cloud data center. Although, most of the existing literature focuses on virtual machine (VM) consolidation for increasing energy efficiency at the cost of service level agreement degradation. In order to improve the existing approaches, load aware three-gear THReshold (LATHR) as well as modified best fit decreasing (MBFD) algorithm is proposed for minimizing total energy consumption while improving the quality of service in terms of SLA. It offers promising results under dynamic workload and variable number of VMs (1–290) allocated on individual host. The outcomes of the proposed work are measured in terms of SLA, energy consumption, instruction energy ratio (IER) and the number of migrations against the varied numbers of VMs. From experimental results it has been concluded that the proposed technique reduced the SLA violations (55%, 26% and 39%) and energy consumption (17%, 12% and 6%) as compared to median absolute deviation (MAD), inter quartile range (IQR) and double threshold (THR) overload detection policies, respectively.

Mitigating Virtualization Failures Through Migration to a Co-Located Hypervisor

Many organizations are moving their systems to the cloud, where providers consolidate multiple clients using virtualization, which creates challenges to business-critical applications. Research has shown that hypervisors fail, often causing common-mode failures that may abruptly disrupt dozens of virtual machines simultaneously. We hypothesize and empirically show that a significant percentage of virtual machines affected by a hypervisor failure are capable of continuing execution on a new hypervisor. Supported by this observation, we design a technique for recovering from hypervisor failures through efficient virtual machine migration to a co-located hypervisor, which allows virtual machines to continue executing with minimal downtime and which can be transparently applied to existing applications. We evaluate a proof-of-concept implementation using fault injection of hardware and software faults and show that it can recover, on average, 41-46% of all virtual machines, as well as having a mean virtual machine downtime of 3 seconds.

Resource utilization enhancemnet through live virtual machine migration in cloud using ant colony optimization algorithm

Cloud computing offers unlimited computational resources which are ready to use from anywhere, anytime on request. The achievement of maximized utilization of computational resources (physical and virtual) and minimized energy consumption of resources are goals of proposed system. The proposed system provides dynamic and energy efficient live VM (virtual machine) migration approach. This system reduces wastage of power by initiating sleep mode of idle physical machines results into energy saving. We propose a system consist with seven modules. (1) Resource monitor analyses energy consumption of resources. (2) Capacity distributor distributes maximum and minimum capacity for the physical machines. (3) Task allocator determines overloaded servers. (4) Optimizer analyses load on physical machine using ant colony optimization algorithm (5) Local Migration Agent calculates load of VMs to be migrated and select appropriate physical server. (6) Migration Orchestrator migrates the VM cosidering load. (7) Energy Manager initiates sleep mode for idle physical machine(PM)

Improved pre-copy algorithm using statistical prediction and compression model for efficient live memory migration

Xen hypervisor is used to execute and migrate the guests on different architectures using a pre-copy algorithm. There are three major categories to improve pre-copy using live migration algorithms: 1) reducing dirty pages; 2) predicating dirty pages; 3) compressing memory pages. The methods based on reducing dirty pages can lead to performance degradation so the new approach called combined approach (including prediction and compression) is proposed in this paper. The prediction of dirty pages during a migration is performed using auto-regressive integrated moving average (ARIMA) model. A least recently used (LRU) stack distance-based delta compression algorithm is proposed for compression model to achieve efficient virtual machine migration. The results show that ARIMA-based model is able to predict 93% in the case of high dirty pages environment. The combined approach is able to reduce 19.16% downtime and 10.76% total migration time on an average compared to Xen's pre-copy algorithm.

Communication-aware virtual machine migration in cloud data centres

The virtual machine (VM) migration technology is widely used in cloud data centres for achieving load balance, or saving energy consumption. Despite the potential benefits of the VM migrations, relatively little work has taken the inter-VM communication into account, with researchers mainly focusing on reducing the network cost of migration or the energy cost. In this paper, we focus on the VM migration problem in the context of large volume of inter-VM communication traffic. To this end, we make decisions on three problems of when, which, and to where the VMs shall be migrated. Specifically, we formulate an optimisation of minimising the number of migrations as well as the cost of both inter-VM traffic and migration traffic. To solve the optimisation, we design an efficient communication-aware VM migration algorithm and seamlessly combine three types of resources: CPU, memory and bandwidth. Finally, we conduct comprehensive experiments based on the CloudSim simulator. Extensive simulation results show that the proposed method can outperform the state-of-art RIAL and Sandpiper, in terms of the following three metrics: the number of migrations, the inter-VM communication cost, and the migration network cost.

Efficient Virtual Machine Migration with Reduced Migration Time and Downtime

Efficient Virtual Machine Migration with Reduced Migration Time and Downtime

Power efficient virtual machine migration in a scientific federated cloud

Virtualization technology enables cloud computing to provide virtual infrastructure. This virtual infrastructure is managed by centralized entity which is responsible for creation, deletion and mig...