An Efficient Virtual Machine Migration Algorithm Based on Minimization of Migration in Cloud Computing

The rapid growth in cloud computing encourages the cloud provider to create more reliable, scalable and efficient cloud environment. For providing such a scalable environment, cloud provider develops large-scale Cloud Data Centers (CDC). Such CDCs consume a large amount of electrical energy for operational purpose and cooling purpose to keep the machines operating at a right temperature, but it also emits a large amount of carbon-dioxide. Thus to decrease the energy consumption, Virtual Machines (VMs) are consolidated to fewer numbers of active servers through VM migration techniques, this leads to Service Level Agreement (SLA) violation and performance degradation. Therefore to retain the energy-performance tradeoff, we should perform an optimum number of VM migrations. There are two cases when the VM migration performed: First when a hot spot is found and Second when a cold spot is found. But in both the cases we do not directly migrate the VM instead first we check whether it is required to migrate the VM. After that, the algorithm finds an appropriate destination host on which the VM will be migrated. In this paper, we implement three approaches to select destination host: First-fit, Best-fit, and Worst-fit. Finally compare the result of a number of VM migration performed, energy consumption and SLA violations with existing one.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

The article examines the importance of container migration within the context of container placement tasks in cloud data centers. Cloud computing offers organizations significant benefits such as flexibility, scalability, and reduced costs for equipment, energy, and maintenance. Cloud providers face the challenge of not only efficiently placing containers initially but also dynamically reallocating them, along with virtual machines, in response to changing demands and workloads. This necessitates the development and application of migration algorithms. The study analyzes how migration is a critical component for ensuring optimal resource use, maintaining high service availability, and reducing service level agreement (SLA) violations. The research aims to enhance the productivity of cloud data centers in the task of container placement by utilizing container mi-gration algorithms. This objective is driven by the need for cloud providers to use their resources effectively to provide higher quality service and maintain competitive pricing. The use of migrations prevents overloading or underloading of servers and virtual machines, which is especially impor-tant in the highly dynamic environment of cloud computing. The research developed a hybrid mi-gration algorithm that includes the migration of both containers and virtual machines. Mathemati-cal aspects of the container placement task are described, and the migration algorithm is detailed. Experiments showed that implementing this algorithm reduces operational costs by 24% compared to traditional methods that do not use migration, and by 9% compared to less flexible migration strategies. There was also a demonstrated reduction in SLA violation times by 40%, indicating im-provements in service quality and reliability of cloud services. The conclusions of this study confirm that container and virtual machine migration is necessary for effective management of dynamic re-sources in cloud data centers in the context of container placement tasks.

Cloud computing provides utility oriented services and pay-as-you-go computing model for its users. Massive adoption of Cloud services leads to build a sustainable environment and also demands for green Cloud services. With the advancement in Cloud services, the issue of carbon dioxide emission becomes significant. There is also a challenge for Cloud provider to satisfy the customer by guaranteeing the services with performance and availability in Service Level Agreement (SLA). Hence, energy consumption and SLA violation become important issues for Cloud providers. These issues can be addressed in many facets viz. efficient provisioning of resources, reducing energy consumption in datacenters, server consolidation, virtual machine (VM) migration, minimizing SLA violation or also by providing SLA with green requirements. In this paper, we survey various techniques which address green SLA with Quality of Service (QoS) requirements by using different resource management and consolidation procedures. Further, we propose our work by considering the multi-criteria method for resource management which selects the host as probable migration destination having highest predicted utilization after allocating a VM on the host. Hence, we aim to address the issue of maintaining the trade-off between energy consumption and SLA violation.

Cloud computing providers have to deal with the energy-performance trade-off: minimizing energy consumption, while meeting service level agreement (SLA) requirements. This paper proposes a new heuristic approach for the dynamic consolidation of virtual machines (VMs) in cloud data centers. The fast best-fit decreasing (FBFD) algorithm for intelligent VMs allocating into hosts and dynamic utilization rate (DUR) algorithm for utilization space and VM migration are successfully proposed. We performed simulations using PlanetLab and GWDG data center workloads to compare our approach against the existing models. It has been observed that the FBFD heuristic algorithm produces better results compared to modified BFD algorithm in terms of energy consumption and SLA violation. Additionally, the time complexity of FBFD algorithm is significantly improved from the order of O(\(m\,*\,n\)) to O(\(m\,*\,\log _2{n}\)). Furthermore, leaving some rates of capacity in the physical machines by the proposed DUR algorithm for VMs to be extended reduces the number of migrations which in turn improves the energy consumption and SLA violation. Our heuristic approach is evaluated using CloudSim and the results show that it performs better than the current state-of-the-art approaches.

Today, Cloud Computing become one of the most promising an emerging technology which will has a significant impact on IT Infrastructure. Still, Cloud Computing is infancy. In the current cloud computing environment there is numerous of application, consist of millions of module, these application serve from large quantity of users and user request becomes dynamic. So there must be provision that all resources are dynamically made available to satisfy the needs of requesting a user in a situation where the resource contention fierce is occurs. In this paper, we propose scheduling heuristic to allocate resource and schedule request for cloud system. An aim of proposed scheduling heuristic is to schedule request on Virtual Machine (VM) based on agreed Service Level Agreements (SLA) and creating new VM on physical resources based on availabilities of resources which considered priority based VM allocation, Load Balancing and Preemption Mechanism. And the experimental results of proposed heuristic helps to optimize performance and at the same time reduce the possibilities of SLA violations.

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.

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

Cloud computing is a very well known technology for all business people, software developers, end-users, and so on. Significant researches are going on to balance the cloud load. The migration of heavily loaded Virtual Machines (VMs) into lightly loaded Physical Machines (PMs) balances the Cloud load. In Resource Intensity Aware Load Balancing (RIAL) method, based on the weight of resources under utilization, it selected the VMs from heavily loaded PMs for migration and chosen the lightly loaded PMs as destination. An Improved RIAL was proposed to consider both lightly and heavily loaded PMs as destination. Later it was enhanced in the proposed Power Consumption Aware- Traffic Aware- IRIAL (PT-IRIAL) method with the consideration of power consumption, temperature and traffic measures to select the VMs for migration and select PMs for destination. From all these, in this current paper, the crossover and mutation process of GA is utilized to optimally select the migration VMs and choose the destination PMs. Thus this GA based load optimization algorithm optimally maps the migration VMs with the destination PMs efficiently.

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

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.

The Cloud Computing (CC) has vast amount of data centers that consists of many computing nodes and consumes a huge amount of electrical energy. Hence the researchers found that the high service-level agreements (SLAs) violations and Energy Consumption (EC) are the major challenging issues in CC. The various traditional approaches reduced the EC, but ignored the SLA violation during the selection of Virtual Machine (VM) from overloaded hosts. In order to effectively deal with these issues, this paper proposed the Recursive Ant Colony Optimization (RACO) in 2 different workloads. The main aim of the RACO is to minimize the high EC and SLA violations by the movement of best ant, which is random and identified the distance between their movements. The algorithm consists of 3 major steps includes tracking and updating the pheromone and finally city selection. The proposed method simulated on Cloud Sim to validate the efficiency and stability of the proposed RACO and their performance compared to that of other existing techniques. The results showed that RACO reduced the EC by 40 - 42 % (approx.) which is less than the traditional ACO algorithm in Planet Lab data. HIGHLIGHTS Reduced EC and SLA Violation of Data Center are done via RACO VM Allocations are done by IQR and utilises micro, small, medium and extra-large instances Pheromone Tracking, updating and city selection are the endeavours of RACO RACO reduced EC by 40 - 42 % and SLA Violation

Cloud computing virtual machine consolidation based on stock trading forecast techniques

Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High-speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get few migrations and this paper introduces novel behavior of VM selection from overutilized PMs using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with another state-of-the-art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters, such as Service Level Agreement (SLA) violation, and energy consumption against various load variation scenarios to support elasticity. The proposed algorithm has outperformed other techniques by considerable margin in terms of QoS, and the details are presented in the results section. The simulation results demonstrate that the proposed technique exhibits 6.3% and 6.7% enhancement in terms of reduced energy consumption compared to both Cuckoo Search (CS) and general Dragonfly (DF) techniques, and 3% decrease in SLA violations in comparison to current methods. Additionally, the results reveal an 11% enhancement in VM migration compared to existing approaches.

The expeditious development in information technology visualizes that facilities for computing are one of the prime utilities of life after the other main essential utilities i.e., water, gas, and electricity. With the help of virtualization, cloud computing helps in transparently sharing of data and services between the cloud users and provides access to more than thousands of computers as a single instance. The virtual machine allocation is a challenging task in virtualization which is regulated as a crucial part of the virtual machine (VM) migration. It is done to discover the best assignment of VMs on physical machines (PMs) because it has explicit effects on resource utilization, energy efficiency, the performance of running applications, etc. So, there is a need to design an effective method for the VM placement problem. The research paper presents a virtual machine allocation technique implementing Enhanced-Modified Best Fit Decreasing (E-MBFD) Algorithm. The outcome of the proposed technique is the cross-validation of allocated virtual machines on physical machines using Artificial Neural Network (ANN). Also, the proposed technique is implemented with the merits of finding the false allocations which happen due to inefficient utilization of resources and it helps in re-allocation of these VMs. The empirical result shows that the E-MBFD technique outperforms in terms of minimized power consumption and a lesser number of service-level agreement (SLA) violations in contrast with the conventional technique.