Using Virtual Machine live migration in trace-driven energy-aware simulation of high-throughput computing systems

In the cloud environment, the transfer of data from one cloud server to another cloud server is called migration. Data can be delivered in various ways, from one data centre to another. This research aims to increase the migration performance of the virtual machine (VM) in the cloud environment. VMs allow cloud customers to store essential data and resources. However, server usage has grown dramatically due to the virtualization of computer systems, resulting in higher data centre power consumption, storage needs, and operating expenses. Multiple VMs on one data centre manage share resources like central processing unit (CPU) cache, network bandwidth, memory, and application bandwidth. In multi-cloud, VM migration addresses the performance degradation due to cloud server configuration, unbalanced traffic load, resource load management, and fault situations during data transfer. VM migration speed is influenced by the size of the VM, the dirty rate of the running application, and the latency of migration iterations. As a result, evaluating VM migration performance while considering all of these factors becomes a difficult task. The main effort of this research is to assess migration problems on performance. The simulation results in Matlab show that if the VM size grows, the migration time of VMs and the downtime can be impacted by three orders of magnitude. The dirty page rate decreases, the migration time and the downtime grow, and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VM transfer is completed. All the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

Section: Figure 1: Vm Migration Metricsmentioning

confidence: 99%

Performance Framework for Virtual Machine Migration in Cloud Computing

Alyas¹,

Ghazal²,

Alfurhood³

et al. 2023

“…Cloud computing offers dynamically provided resources displayed as one or more integrated computer resources based on limits. The application provisioner must determine the appropriate software and hardware configuration throughout the cloud provisioning process to guarantee that the Quality of Services (QoS) aim of the application services is met without sacrificing system utilization and efficiency [12].…”

Section: Figure 1: Resource Allocation Strategymentioning

confidence: 99%

Optimizing Resource Allocation Framework for Multi-Cloud Environment

Alyas¹,

Ghazal²,

Alfurhood³

et al. 2023

Cloud computing makes dynamic resource provisioning more accessible. Monitoring a functioning service is crucial, and changes are made when particular criteria are surpassed. This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service (QoS), estimating the required resources, and modifying allotted resources depending on workload and parallelism due to resources. Resource allocation is a complex challenge due to the versatile service providers and resource providers. The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service. The objective of a coherent and rational resource allocation is to attain the quality of service. It also includes identifying critical parameters to develop a resource allocation mechanism. A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment. The three main parameters of the proposed framework are data accessibility, optimization, and collaboration. Using an optimization technique, these three segments are further divided into subsets for resource allocation and long-term service quality. The CloudSim simulator has been used to validate the suggested framework. Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS. The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

“…In [12], authors identified specific variables that affect real-time migration performance using the OpenStack platform in the system and network environments, such as real-time static adaptation metric comparisons, parallel migration performance comparisons, and SDN sequencing. From a QoS perspective, response time and server response patterns, duplication, hybridization, and selfconvergence-based migration need to be evaluated.…”

Section: Related Workmentioning

confidence: 99%

Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Alyas¹,

Javed²,

Namoun³

et al. 2022

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 crossdata-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.