Virtual Machine Selection Research Articles

An optimization of virtual machine selection and placement by using memory content similarity for server consolidation in cloud

Optimizing the virtual machine (VM) migration is an important issue of server consolidation in the cloud data center. By leveraging the content similarity among the memory of VMs, the time and the amount of transferred data in VM migration, as well as the pressure of network traffic, can be reduced. There are two problems in server consolidation: (1) determining which VMs should be migrated from the overloaded hosts (VM selection problem) and (2) how to place these VMs to the destination hosts (VM placement problem). By exploiting the content similarity, we redefine the above two problems into one problem to minimize the transferred memory data in VM migration. Given a fixed host overloaded threshold, an approximation algorithm is proposed to solve the problem with one overloaded host and one destination host. For the case of multiple overloaded hosts and destination hosts, two heuristic algorithms are presented with fixed and dynamic overloaded threshold respectively. We conduct a real workload trace based simulation to evaluate the performance of our algorithms. The result shows that our algorithms can produce fewer transferred VM memory data and consume less energy than existing policies.

FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model

Cloud computing is a developing technology that enables on-demand network access to the users through a shared pool of cluster computing resources. However, maintaining the stability of processing several tasks in the cloud environment is a complex issue. Hence, it requires a load balancing technique that allocates the task to the Virtual Machines (VMs) without affecting the performance of the system. This paper presents a technique for load balancing, called fractional dragonfly based load balancing algorithm (FDLA), by proposing two selection probabilities and fractional dragonfly algorithm. The proposed load balancing model utilizes certain parameters of VMs and Physical Machines (PMs) to select the tasks to be reallocated in the VMs for load balancing. The selection is based on the probabilities, Task selection probability (TSP) and VM selection probability (VSP), which are newly designed. Further, the proposed fractional dragonfly algorithm that combines dragonfly algorithm (DA) with Fractional Calculus (FC) performs an optimal selection of VMs for the reallocation of the task using a newly designed fitness function. In the performance analysis of FDLA based on load and number of tasks reallocated, the proposed FDLA could achieve a minimum load of 0.2133 with 14 reallocated tasks, indicating its effectiveness in load balancing.

A machine learning model for improving healthcare services on cloud computing environment

Recently, cloud computing gained an important role in healthcare services (HCS) due to its ability to improve the HCS performance. However, the optimal selection of virtual machines (VMs) to process a medical request represents a big challenge. Optimal selection of VMs performs a significant enhancement of the performance through reducing the execution time of medical requests (tasks) coming from stakeholders (patients, doctors, etc.) and maximizing utilization of cloud resources. For that, this paper proposes a new model for HCS based on cloud environment using Parallel Particle Swarm Optimization (PPSO) to optimize the VMs selection. In addition, a new model for chronic kidney disease (CKD) diagnosis and prediction is proposed to measure the performance of our VMs model. The prediction model of CKD is implemented using two consecutive techniques, which are linear regression (LR) and neural network (NN). LR is used to determine critical factors that influence on CKD. NN is used to predict of CKD. The results show that, the proposed model outperforms the state-of-the art models in total execution time the rate of 50%. In addition, the system efficiency regarding real-time data retrieval is greatly improved by 5.2%. In addition, the accuracy of hybrid intelligent model in predicting of CKD is 97.8%. The proposed model is superior to most of the referred models in the related works by 64%.

Adaptive Energy-Aware Algorithms for Minimizing Energy Consumption and SLA Violation in Cloud Computing

In cloud computing, high energy consumption and service-level agreements (SLAs) violation are the challenging issues considering that the demand for computational power is growing rapidly, thereby requiring large-scale cloud data centers. Although, there are many existing energy-aware approaches focusing on minimizing energy consumption while ignoring the SLA violation at the time of a virtual machine (VM) selection from overloaded hosts. Also, they do not consider that the current network traffic causes performance degradation and thus may not really reduce SLA violation under a variety of workloads. In this context, this paper proposes three adaptive models, namely, gradient descent-based regression (Gdr), maximize correlation percentage (MCP), and bandwidth-aware selection policy (Bw), that can significantly minimize energy consumption and SLA violation. Energy-aware methods for overloaded host detection and VM selection from an overloaded host are necessary to improve the energy efficiency and SLA violation of a cloud data center after migrating all VM from underloaded host turn to idle host, which switch to energy-saving mode is also beneficial. Gdr and MCP are adaptive energy-aware algorithms based on the robust regression model, for overloaded host detection. A Bw dynamic VM selection policy selects VM according to the network traffic from the overloaded host under SLAs. Experimental results on the real workload traces show that the proposed algorithms reduce energy consumption while maintaining the required performance levels in a cloud data center using a CloudSim simulator to validate the proposed algorithms.

K-mMA VM selection in dynamic VM consolidation for improving energy efficiency at cloud data centre

Dynamic virtual machine (VM) consolidation is an alternative solution for managing and optimising energy efficiency in a cloud data centre. This research proposed VM selection method in dynamic VM consolidation based on K-means clustering technique and computational model Markov normal algorithm (K-mMA). The objective of VM selection is to select proper VM, which should be migrated away from the overloaded physical machine and to avoid oversubscribe host. The VM selection method has been tested in simulation condition using CloudSim and PlanetLabs datasets with various conditions of VM instances (homogeneous and heterogeneous). The performance metrics in this research are energy consumption (EC), SLA time per active host (SLATAH), performance degradation due migration (PDM) and SLA violation (SLAV). Results experiment has shown that K-mMA could improve energy efficiency and quality of service (QOS) at cloud data centre significantly. Compared with existing method such as CFS, MMT, RC, and MC the proposed K-mMA could improve efficiency energy in cloud data centre by optimising VM selection problem up to 3.9%, 6.8%, 5.5%, and 5.3% respectively.

K-mMA VM selection in dynamic VM consolidation for improving energy efficiency at cloud data centre

Dynamic virtual machine (VM) consolidation is an alternative solution for managing and optimising energy efficiency in a cloud data centre. This research proposed VM selection method in dynamic VM consolidation based on K-means clustering technique and computational model Markov normal algorithm (K-mMA). The objective of VM selection is to select proper VM, which should be migrated away from the overloaded physical machine and to avoid oversubscribe host. The VM selection method has been tested in simulation condition using CloudSim and PlanetLabs datasets with various conditions of VM instances (homogeneous and heterogeneous). The performance metrics in this research are energy consumption (EC), SLA time per active host (SLATAH), performance degradation due migration (PDM) and SLA violation (SLAV). Results experiment has shown that K-mMA could improve energy efficiency and quality of service (QOS) at cloud data centre significantly. Compared with existing method such as CFS, MMT, RC, and MC the proposed K-mMA could improve efficiency energy in cloud data centre by optimising VM selection problem up to 3.9%, 6.8%, 5.5%, and 5.3% respectively.

Priority-Aware Virtual Machine Selection Algorithm in Dynamic Consolidation

In the past few years, many researchers attempted to tackle the problem of decreasing energy consumption in cloud data centers. One of the widely adopted techniques for this purpose is dynamic Virtual Machine (VM) consolidation. Consolidation moves VMs between hosts to decrease energy consumption. However, it has a negative impact on performance leading to Service Level Agreement (SLA) violations. Accordingly, selecting which VM to migrate from one host to another is a challenging task since it can affect performance. Researchers came up with several solutions and policies for efficient VM selection. In this paper, we exploit the fact that many tasks and users may tolerate some performance degradation which means, the tasks running on the VMs can be of different priorities. Accordingly, we propose augmenting consolidation with the priority concept, where low priority tasks are always selected first for migration. Towards this goal, we modified the popular Minimum Migration Time VM selection algorithm using the priority concept. The efficiency of the proposed algorithm is confirmed through extensive simulations using CloudSim toolkit and a real workload. The results show that priority awareness has a positive impact on decreasing energy consumption as well as maximizing SLA obligation.

Three phase optimisation for qualified and secured VMs for resource allocation

Cloud computing services enable the users to use required computing resources for the required period which thereby permit the users to build a flexible information system. Choosing genuine and secured virtual machines (VM) among thousands of virtual machines is one of the main challenges in resource allocation to achieve higher accuracy. In this paper, VM optimisation techniques namely match making algorithm, memory aware VM and cuckoo technique is incorporated to select the best and secured virtual machine from cloud. In the proposed technique the first two are combined to select the best VM, where cuckoo is used to provide optimised resources to the finest VM. During the selection of finest VM from above techniques, it is necessary to select secured VM's to achieve an optimum result during resource allocation. To select secured VM, signature based detection and secure counters are employed to analyse network traffic and to monitor the data transactions.

Energy efficient virtual machine consolidation for cloud data centres using analytic hierarchy process

Ever increasing demand of a cloud computing immensely increases the consumption of energy and power. Data centres consume 1.1% to 1.5% of overall electricity consumed in the world which is growing by 12% every year. There are many electric components present inside it and therefore, it needs a huge amount of electricity to power and cool down the electric components which results in emissions of high carbon dioxide. Minimisation of energy consumption in the data centres is very important for environmental sustainability and it can be minimised by using lesser number of cloud resources and improving the utilisation of these resources. Dynamic consolidation of virtual machines (VMs) plays an important role and an effective method for the reduction of energy consumption. In the current study, a peculiar technique based on analytic hierarchy process for the selection of VM for migration has been proposed to minimise the total energy consumption and SLA violation of cloud environment.

Energy Efficient Resource Allocation During Initial Mapping of Virtual Machines to Servers in Cloud Datacenters

Energy consumption has been identified as one of the key research challenges during recent time in Cloud computing. Proper placement of Virtual Machines (VMs) on servers may address the issue. The process of placing VMs on servers can be divided into two phases viz. (a) Mapping of VMs on servers during the phase and (b) subsequent VM selection, migration and placement during consolidation phase. If the initial mapping is not efficient, subsequent operations may lead to unnecessary VM migrations, which in turn, may result into increase in migration cost and increase in SLA violations. In this research, the authors aim to improve the resource utilization to address these issues by keeping (i) the number of live server as minimal as possible for achieving energy efficiency, and (ii) the live server, as busy as possible by efficiently utilizing them. The authors conducted series of experiments with existing default technique and various other approaches. The results of our experiments make us conclude that there is a scope of improvement in the default mapping technique currently being used in CloudSim.

Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters

Resource over provisioning in cloud computing consumes energy excessively. Energy-aware dynamic virtual machine consolidation (DVMC) reduces energy consumption without compromising service level agreement. In this paper, we put forward a new framework of DVMC for green cloud computing. In particular, we propose a new virtual machine (VM) placement policy, namely, space aware best fit decreasing (SABFD) and a new migration VM selection policy, namely, high CPU utilization-based migration VM selection (called HS). Thorough simulations are carried out to evaluate the performances of different energy-aware DVMC plans based on real-world workload traces, with DVMC plans as various combinations of host overload detection, migration VM selection, and VM placement policies. The simulation results show that DVMC plans with SABFD policy or with HS policy outperforms alternative DVMC plans. What is more, a DVMC plan with both SABFD and HS policies makes the best performance.

ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications

In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes. We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity. Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement. Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme.

Distributed Autonomous Virtual Resource Management in Datacenters Using Finite-Markov Decision Process

To provide robust infrastructure as a service, clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, in this paper, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of service level agreement (SLA) violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time and energy) and delay by quickly identifying VMs and destination PMs to migrate. We further propose enhancement methods for higher performance. First, we propose a cloud profit oriented reward system in the MDP model so that when the MDP tries to maximize the rewards for load balance, it concurrently improves the actual profit of the datacenter. Second, we propose a new MDP model, which considers the actions for both migrating a VM out of a PM and migrating a VM into a PM, in order to reduce the overhead and improve the effectiveness of load balancing. Our trace-driven experiments show that our algorithm outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency, and long-term load balance maintenance. Our experimental results also show the effectiveness of our proposed enhancement methods.

Energy-aware virtual machine allocation and selection in cloud data centers

Data centers evolve constantly in size, complexity, and power consumption. Energy management in cloud data centers is a critical and challenging research issue. It becomes necessary to minimize the operational costs as well as environmental impact and to guarantee the service-level agreements for the services provided by the data centers. We propose a modified discrete particle swarm optimization based on the characteristic particle swarm optimization for the initial placement of virtual machines and a novel virtual machine selection algorithm for optimizing the current allocation based on memory utilization, bandwidth utilization, and size of the virtual machine. By means of simulations, we observe that the proposed method not only saves the energy significantly than the other approaches, but also minimizes the violations of service-level agreements.

Energy-efficient virtual machine consolidation algorithm in cloud data centers

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine (VM) consolidation algorithm named PVDE (prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement (SLA) violations.

Joint routing and mobile VM selection algorithm in multihop C‐RAN networks

SummaryBy combining the cloud radio access network (C‐RAN) and mobile cloud computing, the communication and computation cooperation is convenient to implement, and the mobile operators are able to provide better service for mobile users as well as decrease their construction funds. However, owing to the deficient bandwidths and far distances, some mobile devices are not able to access mobile cloud through C‐RAN directly. In this paper, we propose a multihop C‐RAN network to access mobile cloud that organizes the user equipment (UE) in the C‐RAN into self‐organized network, regarding UEs as relay routers to access the mobile cloud. Under the proposed architecture, the UEs far from remote radio heads are able to access C‐RAN by ad hoc network while the UEs near the remote radio heads will access C‐RAN directly. Besides, considering the benefit of both operators and mobile users, we propose a joint routing and mobile virtual machine (VM) selection algorithm based on the proposed multihop C‐RAN networks where the route is selected for task data transmission, and the VM in the mobile cloud is selected for task execution, which minimizes the total energy cost under the premise of satisfying the task response time required by user. The simulation result shows that the proposed algorithm outperforms conventional separate routing and VM selection on energy saving.

Auto-scaling web applications in clouds: A cost-aware approach

The elasticity feature of cloud computing and its pay-per-use pricing entice application providers to use cloud application hosting. One of the most valuable methods, an application provider can use in order to reduce costs is resource auto-scaling. Resource auto-scaling for the purpose of preventing resource over-provisioning or under-provisioning is a widely investigated topic in cloud environments. The Auto-scaling process is often implemented based on the four phases of MAPE loop: Monitoring (M), Analysis (A), Planning (P) and Execution (E). Hence, researchers seek to improve the performance of this mechanism with different solutions for each phase. However, the solutions in this area are generally focused on the improvement of the performance in the three phases of the monitoring, analysis, and planning, while the execution phase is considered less often. This paper provides a cost saving super professional executor which shows the importance and effectiveness of this phase of the controlling cycle. Unlike common executors, the proposed solution executes scale-down commands via aware selection of surplus virtual machines; moreover, with its novel features, surplus virtual machines are kept quarantined for the rest of their billing period in order to maximize the cost efficiency. Simulation results show that the proposed executor reduces the cost of renting virtual machines by 7% while improves the final service level agreement of the application provider and controls the mechanism's oscillation in decision-making.

Location-aware brokering for consumers in multi-cloud computing environments

The variety and complexity in cloud marketplaces is growing, making it difficult for cloud consumers to choose cloud services from multiple providers in an economic and suitable way by taking into account multiple objectives and constraints. In this paper, we present an extension of CloudSim implementing cloud management functionality to enable the assessment of consumer-oriented brokering schemes. The underlying discrete-event simulation framework allows evaluating their performance in more realistic operating conditions in a repeatable manner. We integrate brokering mechanisms to support a multi-criteria location-aware selection of virtual machines in multi-cloud environments by implementing a greedy heuristic and two large neighborhood search metaheuristics. Based on microbenchmarks of real cloud offerings and a diverse set of scenarios and workloads, we conduct simulation experiments to assess the performance of our approaches. The results show that approximately 10 – 12% of the total costs can be saved by using a large neighborhood search approach compared to the greedy heuristic. Finally, we analyze and discuss the trade-off between costs and latency as well as the impact of region constraints, showing, e.g., that latency improvements often come at a high price and a greater regional flexibility can lead to latency improvements while solely optimizing costs. Using real data of cloud marketplaces, we show that the proposed CloudSim extension can support decision makers as a tool for assessing cloud portfolios and market dynamics.

AutoMigrate: a framework for developing intelligent, self-managing cloud services with maximum availability

Cloud services are on-demand services provided to end-users over the Internet and hosted by cloud service providers. A cloud service consists of a set of interacting applications/processes running on one or more interconnected VMs. Organizations are increasingly using cloud services as a cost-effective means for outsourcing their IT departments. However, cloud service availability is not guaranteed by cloud service providers, especially in the event of anomalous circumstances that spontaneously disrupt availability including natural disasters, power failure, and cybersecurity attacks. In this paper, we propose a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. The framework connects an autonomic computing agent to the cloud to automatically migrate cloud services based on anticipated cloud disruption. The autonomic agent employs a modular design to facilitate the incorporation of different techniques for deciding when to migrate cloud services, what cloud services to migrate, and where to migrate the selected cloud services. We incorporated a virtual machine selection algorithm for deciding what cloud services to migrate that maximizes the availability of high priority services during migration under time and network bandwidth constraints. We implemented the framework and conducted experiments to evaluate the performance of the underlying techniques. Based on the experiments, the use of this framework results in less down-time due to migration, thereby leading to reduced cloud service disruption.

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

SummaryPrivate Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.