Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

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.

CBase: Fast Virtual Machine storage data migration with a new data center structure

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.

As more and more data centers embrace end host virtualization and virtual machine (VM) mobility becomes commonplace, we explore its implications on data center networks. Live VM migrations are considered expensive operations because of the additional network traffic they generate, which can impact the network performance of other applications in the network, and because of the downtime that applications running on a migrating VM may experience. Most virtualization vendors currently recommend a separate network for VM mobility. However, setting up an alternate network just for VM migrations can be extremely costly and thus presents a barrier to seamless VM mobility. Therefore, it is apparent that VM migrations should be orchestrated in a network-aware manner with appropriate QoS controls such that they do not degrade network performance of other flows in the network while still being allocated the bandwidth they require for successful completion within the specified time lines. In this context, we present VMPatrol - a QoS framework for VM migrations. VMPatrol uses a cost of migration model to allocate a minimal bandwidth for a migration flow such that it completes within the specified time limit while causing minimal interference to other flows in the network. Our implementation and experimental evaluation of VMPatrol on real and virtual software testbeds demonstrates that automated bandwidth reservation can reduce the impact of migrations on other flows in the network to a negligible level.

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

Live Virtual Machine (VM) migration has significantly improved the flexibility of modern Data Centers (DC). However, seamless live migration of a VM between geo-distributed DCs faces several challenges due to difficulties in preserving the network configuration after the migration paired with a large network convergence time. Although SDN-based approaches can speed up network convergence time, these techniques have two limitations. First, they typically react to the new topology by installing new flow rules once the migration is finished. Second, because the WAN is typically not under SDN control, they result in sub-optimal routing thus severely degrading the network performance once the VM is attached at the new location. In this paper, we identify networking challenges for VM migration across geo-distributed DCs. Based on those observations, we design a novel long-haul VM migration scheme that overcomes those limitations. First, instead of reactively restoring connectivity after the migration, our SDN-based approach proactively restores flows across the WAN towards the new location with the help of EVPN and VXLAN overlay technologies. Second, the SDN controller accelerates the network convergence by announcing the migration to other controllers using MP-BGP control plane messages. Finally, the SDN controller resolves the sub-optimal routing problem that arises as a result of migration implementing a distributed anycast gateway. We implement our approach as extensions to the OpenDaylight controller. Our evaluation shows that our approach outperforms existing approaches in reducing the downtime by 400 ms and increasing the application performance up to 12 times.

As one of the key characteristics of virtualization, live virtual machine (VM) migration provides great benefits for load balancing, power management, fault tolerance and other system maintenance issues in modern clusters and data centers. Although Pre-Copy is a widespread used migration algorithm, it does transfer a lot of duplicated memory image data from source to destination, which results in longer migration time and downtime. This paper proposes a novel VM migration approach, named Migration with Data Deduplication (MDD), which introduces data deduplication into migration. MDD utilizes the self-similarity of run-time memory image, uses hash based fingerprints to find identical and similar memory pages, and employs Run Length Encode (RLE) to eliminate redundant memory data during migration. Experiment demonstrates that compared with Xen's default Pre-Copy migration algorithm, MDD can reduce 56.60% of total data transferred during migration, 34.93% of total migration time, and 26.16% of downtime on average.

Cloud computing has been considered the most reliable and safe technology among many cloud users. Virtual machine migration is a crucial component of cloud computing technology. A Virtual machine is transferred from one host machine to another during the migration process, which is accomplished by repeatedly copying the memory pages available in the virtual disk. The time required for the movement of secondary storage, main memory, process states, and network connectivity, as well as the maintenance of their accuracy, must be considered for the efficient migration of virtual machines from one physical host to another in the cloud service provider. The most robust mechanism for system maintenance, resilience system, balancing the load of the service provider, and power-saving is the live migration of virtual machines between the nodes in the data center. So many services are running in the virtual machines during live migration; thus, it needs to take place with the least amount of downtime possible to prevent service degradation. The memory pages in the virtual storage must be copied to the other host using the least frequently used service-based pre-copy algorithm to optimize the downtime. The effectiveness of the suggested solution was evaluated in comparison to OpenNebula and OpenStack, two cloud computing platforms. The least frequently used service-based pre-copy algorithm reduces the amount of virtual machine downtime during live migration.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

A Service Sustainable Live Migration Strategy for Multiple Virtual Machines in Cloud Data Centers

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.

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

In the present cloud computing environment, the scheduling approaches for VM (Virtual Machine) resources only focus on the current state of the entire system. Most often they fail to consider the system variation and historical behavioral data which causes system load imbalance. To present a better approach for solving the problem of VM resource scheduling in a cloud computing environment, this project demonstrates a genetic algorithm based VM resource scheduling strategy that focuses on system load balancing. The genetic algorithm approach computes the impact in advance, that it will have on the system after the new VM resource is deployed in the system, by utilizing historical data and current state of the system. It then picks up the solution, which will have the least effect on the system. By doing this it ensures the better load balancing and reduces the number of dynamic VM migrations. The approach presented in this project solves the problem of load imbalance and high migration costs. Usually load imbalance and high number of VM migrations occur if the scheduling is performed using the traditional algorithms.

With heavy demand for cloud technology, it is important to balance the cloud load to deliver seamless Quality of Services to the different cloud users. To address such issues, a new hybridized technique Artificial Neural Network based Load Balancing (ANN-LB) is introduced to calculate an optimized Virtual Machine (VM) load in cloud systems. The Particle Swarm Optimization (PSO) technique is used to perform task scheduling. The performance of the proposed ANN-LB approach has been analyzed with the existing CM-eFCFS, Round Robin, MaxMin, and MinMin algorithms based on MakeSpan, Average Resource Utilization, and Transmission Time. Calculated values and plotted graphs illustrate that the presented work is efficient and effective for load balancing. Hybridization of ANN and iK-mean methods obtains a proper load balancing among VMs and results have been remarkable.

Live migration of virtual machine (VM) is a promising technology that helps physical machines (PMs) adapt to load changes and guarantees Quality of Service (QoS) in cloud data center. Many individual-based VM migration studies ignore the association between VMs, resulting in high communication cost. Some research on multiple VMs migration migrates the VM group as a whole, which is likely to result in ineffective migration and increase the network burden. In this paper, a VM migration algorithm based on group selection (VMMAGS) is proposed, which takes into account the migration cost, communication cost, and VM heat to optimize migration performance. The appropriate VM groups are selected as migration options, and the optimal migration scheme is obtained according to the integration cost of partitions of selected VM groups. Extensive experiments show that our algorithm can effectively reduce the migration cost and communication cost, improve the system reliability compared with other related algorithms.