Virtual Machine Resource Allocation Research Articles

Backup Resource Allocation of Virtual Machines With Two-Stage Probabilistic Protection

In a cloud, protection through backup of virtual machines contained in physical machines (PMs) reduces damage to users due to failure of PMs, such as hardware malfunctions. However, from a resource cost perspective, it is necessary to reduce the amount of capacity for backup while limiting the probability of unsuccessful protection. Existing studies suggest the method of sharing backup capacity among primary resources, but the amount of capacity reduction required to protect is limited. This paper proposes a backup resource allocation model with two-stage probabilistic protection to minimize the total required backup capacity for multiple simultaneous failures of PMs. In probabilistic protection, backup resources are allocated so that the probability of backup failure does not exceed a given survivability parameter which represents the acceptable probability of backup failure. Probabilistic protection which achieves efficient sharing of backup capacity enables flexible allocation and reduces the required backup capacity. In order to increase the flexibility of backup capacity allocation, the proposed model extends the probabilistic protection to two stages. By dividing the protection into two stages, the weight of probability between stages can be adjusted, enabling more effective capacity sharing. Since it is uncertain which primary PMs fail, we apply robust optimization to the probabilistic protection. By using a table that takes into account the survivability parameter and the failure probability of PMs, the proposed model is formulated as a mixed integer linear programming problem. We prove the NP-hardness of considered problem. A heuristic is introduced to solve the optimization problem. The proposed model can reduce the total required backup capacity compared to the models with dedicated protection and one-stage probabilistic protection. The model can also provide protection in a range of survivability parameters that the model with one-stage probabilistic protection cannot satisfy.

Hybrid COOT-Reverse Cognitive Fruit Fly Optimization-Based Big Data Services and Virtual Machine Allocation for Cloud Storage System

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques.

Smart city implementation based on Internet of Things integrated with optimization technology

A smart city is a system that uses technology means to create, implement, and promote environmentally friendly solutions to the problems caused by growing urbanisation. The smart city is a key answer that will allow us to continue with our regular lives during and after the epidemic. To better serve its citizens, a smart city takes advantage of modern technology and innovative solutions in areas including healthcare, educational, energy provision, transport, and communications. Applications of the Internet of Things have led to the development of "Smart Cities" all around the globe. In order to further the development of smart cities toward effective sharing and interconnection, this study will examine the smart city data technology based on the Internet of Things (IoT) and cloud computing (CC). Initially, we gather IoT data based on the smart cities. The gathered data is normalised. Machine learning sets the way for the IoT, which allows machines to communicate with one another without any human intervention. So we utilize machine learning algorithm called Advanced Random Forest (ARF). For multi-criteria optimization, we suggest Adaptive cloud computing virtual machine resource allocation technique (ACC-VMRA). We analyse and simulate the use of IoT and CC technologies in a smart city to verify its efficacy. The experimental finding demonstrates the recommended optimization algorithm's superior efficiency to other currently used techniques.

Towards optimal virtual machine placement methods in cloud environments

The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends.

Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty

Backup Resource Allocation of Virtual Machines for Probabilistic Protection under Capacity Uncertainty

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm.

Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users' costs, and save users' computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale.

Fair and Stable Matching Virtual Machine Resource Allocation Method

In order to unify the management and scheduling of cloud resources, cloud platforms use virtualization technology to re-integrate multiple computing resources in the cloud and build virtual units on physical machines to achieve dynamic provisioning of resources by configuring virtual units of various sizes. Therefore, how to reasonably determine the mapping relationship between virtual units and physical machines is an important research topic for cloud resource scheduling. In this paper, we propose a fair cloud virtual machine resource allocation method of using the stable matching theory. Our allocation method considers the allocation of resources from both user’s demand and cloud computing resource provider’s request. When multiple users apply for resources, firstly select a user by user priority, and then deal with this user’s task. Because the user priority is dynamic, so as to avoid a user’s long-term share of resources. This strategy makes user task scheduling is relatively fair. On the basis of weighing the fair allocation of user resources, the stable matching between physical machines and virtual machines is achieved. Our simulation experiments especially given that the main focus of the paper is not to develop a very novel algorithm, but to demonstrate our virtual machine resource allocation method, which effectively improves the average utilization rate of computing resources and reduces the operating costs of cloud providers.

Auction-Based VM Allocation for Deadline-Sensitive Tasks in Distributed Edge Cloud

Edge cloud computing is a new paradigm in which the computation and storage services of remote cloud data centers are moved to Edge Cloud Nodes (ECNs) in network edges. Compared to traditional cloud data centers, ECNs are geographically close to mobile users so the communication latency is significantly reduced. In this paper, we study the problem of allocating Virtual Machine (VM) resources in geo-distributed ECNs to mobile users by using the auction theory. First, we treat mobile users and ECNs as the buyers and sellers of the VM resource auction, respectively. Then, we model the VM resource allocation problem as an <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -to-one weighted bipartite graph matching problem with 0-1 knapsack constraints. Since this problem is NP-hard, we design a greedy approximation algorithm to determine the winners of the auction, based on which we propose a truthful Auction-based VM resource Allocation (AVA) mechanism to solve the problem. Moreover, we prove that the AVA mechanism not only achieves an approximately optimal solution for winner selection, but also has the properties of truthfulness, individual rationality, and computational efficiency. Finally, we conduct extensive simulations on real traces to verify the significant performances of the proposed AVA mechanism.

Smart city information processing under internet of things and cloud computing.

This study is to explore the smart city information (SCI) processing technology based on the Internet of Things (IoT) and cloud computing, promoting the construction of smart cities in the direction of effective sharing and interconnection. In this study, a SCI system is constructed based on the information islands in the smart construction of various fields in smart cities. The smart environment monitoring, smart transportation, and smart epidemic prevention at the application layer of the SCI system are designed separately. A multi-objective optimization algorithm for cloud computing virtual machine resource allocation method (CC-VMRA method) is proposed, and the application of the IoT and cloud computing technology in the smart city information system is further analysed and simulated for the performance verification. The results show that the multi-objective optimization algorithm in the CC-VMRA method can greatly reduce the number of physical servers in the SCI system (less than 20), and the variance is not higher than 0.0024, which can enable the server cluster to achieve better load balancing effects. In addition, the packet loss rate of the Zigbee protocol used by the IoT gateway in the SCI system is far below the 0.1% indicator, and the delay is less than 10 ms. Therefore, the SCI system constructed by this study shows low latency and high utilization rate, which can provide experimental reference for the later construction of smart city.

RETRACTED ARTICLE: Energy and CPU utilization of cloud virtual machine resource allocation using dynamic heuristic mitigate migration algorithm

Cloud computing systems face new challenges every day due to the large and complex structure of system clusters and the data processed by the systems. The ability to obtain resources flexibly is considered to be the main reason for adopting cloud computing systems. Resiliency primarily supports facility growth and shrinks virtual resources according to the needs of dynamic cloud consumers. The cloud infrastructure promises to provide efficient resource utilization for data centers. The CPU is one of the many resources a client can use in a cloud environment. However, ensuring resource allocation flexibility is one of the major challenges in cloud environments. Develop a Dynamic Heuristic Mitigate Migration (DHMM) to increase the utilization of resources that are effectively allocated between virtual machines via cloud servers. In this proposed method DHMM focuses the storage migration and CPU utilization approaches to improve the cloud performance. In order to evaluate the performance, the proposed algorithm is compared to the prior art. Experimental results show that the proposed framework proves that it has been found very effective to improve resource utilization in a cloud computing environment.

Solving fully dynamic bin packing problem for virtual machine allocation in the cloud environment by the futuristic greedy algorithm

Many scientists apply fully dynamic bin packing problem solving for resource allocation of virtual machines in cloud environments. The goal of problem-solving is to reduce the number of allocated hosts (bins) and virtual machines (items) migration rates for reducing energy consumption. This study demonstrates a greedy futuristic algorithm (proposed algorithm) for fully dynamic bin packaging with an average asymptotic approximation ratio of 1.231, better than other existing algorithms. The proposed algorithm identifies inappropriate local selections using special futuristic conditions to prevent them as much as possible. Eventually, suitable choices determine and discard the improper ones. The proposed algorithm illustrates an asymptotic approximation ratio of (t/ (t-1)) OPT, where the value of t depends on the distribution of the arrived and departed items. Also, OPT is the number of bins by an optimal solution. Finally, in experiments of datasets using a maximum utilization of 80% of each bin, the average migration rate is 0.338. Using the proposed method for allocating resources in the cloud environment can allocate hosts to a virtual machine using almost optimal use. This allocation can reduce the cost of maintaining and purchasing hosts. Also, this method can reduce the migration rate of virtual machines. As a result, decreasing migration improves the energy consumption cost in the cloud environment.

A hierarchical control framework of load balancing and resource allocation of cloud computing services

Service providers must guarantee Quality of Service (QoS) requirements of the co-hosted applications in a data center and simultaneously achieve optimal utilization of their infrastructure under varying workload. This paper presents a hierarchical control framework that aims at compromising antagonistic objectives inside a data center. The local control level tackles simultaneously the problems of resource allocation and admission control of virtual machines while the upper level addresses together the load balancing of the incoming requests and placement of virtual machines into a cluster of physical servers. Numerical results show that the cooperation of the two control layers guarantees the satisfaction of the system’s constraints and the user’s requirements towards the fluctuations of incoming requests.

Virtualization of Heterogeneous HPC-clusters Based on OpenStack Platform

A.G. Feoktistov, I.A. Sidorov, V.V. Sergeev, R.O. Kostromin, V.G. Bogdanova Matrosov Institute for System Dynamics and Control Theory of SB RAS (Lermontova str., 134, Irkutsk, 664033, Russia) E-mail: agf@icc.ru, ivan.sidorov@icc.ru, vvsergeev@mail.ru, kostromin@icc.ru, bvg@icc.ru. Феоктистов Александр Геннадьевич, к.ф.-м.н., доцент, лаборатория параллельных и распределенных вычислительных систем, Институт динамики систем и теории управления им. В.М. Матросова СО РАН (Иркутск, Российская Федерация) Сидоров Иван Александрович, к.ф.-м.н., доцент, лаборатория параллельных и распределенных вычислительных систем, Институт динамики систем и теории управления им. В.М. Матросова СО РАН (Иркутск, Российская Федерация) Сергеев Вадим Викторович, аспирант, лаборатория параллельных и распределенных вычислительных систем, Институт динамики систем и теории управления им. В.М. Матросова СО РАН (Иркутск, Российская Федерация) Костромин Роман Олегович, аспирант, лаборатория параллельных и распределенных вычислительных систем, Институт динамики систем и теории управления им. В.М. Матросова СО РАН (Иркутск, Российская Федерация) Богданова Вера Геннадьевна, к.ф.-м.н., доцент, лаборатория параллельных и распре деленных вычислительных систем, Институт динамики систем и теории управления им. В.М. Матросова СО РАН (Иркутск, Российская Федерация)

Power and QoS Aware Multi-level Resource Coordination and Scheduling in Virtualized Servers

Modern cloud data centers are virtualized for resource multiplexing and services consolidations. Virtual machines (VMs) residing in the same server cluster share the same hardware resources and power supply while they may have different QoS requirements for their services and applications. Moreover, the power consumption of the server cluster is highly dynamic since different virtual machines have different workloads due to different services requests. Cluster level power and QoS coordination is crucial for data center level energy efficiency coordination as well as high quality service provisioning. In this paper we propose the power and QoS aware multi-level resource coordination and scheduling in virtualized servers, i.e., the cluster-level power control layer, the VMs resource allocation layer, and the QoS optimization layer. This three-level controlling framework schedules and allocates hardware resource for QoS guarantee and cluster level power management. We use dynamic frequency scaling for QoS mitigating when power budget changes. The experiment results show that the proposed multi-level coordinated control architecture consumes 5.36% and 6.96% less power for web servers and computing intensive virtual machines, respectively while it can guarantee the response time of web server and execution time of computing tasks no more than those without the proposed controlling approach.

Virtual Machine Resource Allocation of Probabilistic Optimization Based on SME Algorithm

To further enhance optimizing effect of virtual machine resource allocation, virtual machine resource allocation algorithm of probabilistic optimization based on SME-FFD (Simulated Evolution – First Fit Decreasing ) is proposed aiming at NP hard optimization problem in the process of virtual machine resource allocation in cloud computing. First of all, an optimization evaluation scheme of virtual machine resource allocation is proposed, and strong climbing optimization ability of simulated evolutionary algorithm is adopted to carry out iterative evolution to selection, evaluation and ranking of virtual machine resource allocation ; after that, based on SME algorithm obtaining resource allocation ordering, the secondary allocation on virtual machine and physical host resources ranked is conducted using FFD to improve efficiency and effectiveness of resource allocation; in the end, experimental comparison is conducted in CloundSim grid lab in the University of Melbourne and gridbus cloud simulation platform, the results show that CPU usage rate of proposed algorithm reaches up to 47%, memory usage rate reaches up to 56%, therefore, it may effectively reduce physical machine usage quantity and realize the goal of energy consumption.

Energy aware resource allocation of cloud data center: review and open issues

The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.

Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

The next generation video surveillance systems are expected to face challenges in providing computation support for an unprecedented amount of video streams from multiple video cameras in a timely and scalable fashion. Cloud computing offers huge computation resources for large-scale storage and processing on demand, which are deemed suitable for video surveillance tasks. Cloud also provides quality of service guaranteed hardware and software solutions with the virtual machine (VM) technology using a utility-like service costing model. In cloud-based video surveillance context, the resource requests to handle video surveillance tasks are translated in the form of VM resource requests, which in turn are mapped to VM resource allocation referring to physical server resources hosting the VMs. Due to the nature of video surveillance tasks, these requests are highly time-constrained, heterogeneous and dynamic in nature. Hence, it is very challenging to actually manage the cloud resources from the perspective of VM resource allocation given the stringent requirements of video surveillance tasks. This paper proposes a computation model to efficiently manage cloud resources for surveillance tasks allocation. The proposed model works on optimizing the trade-off between average service waiting time and long-term service cost, and shows that long-term service cost is inversely proportional to high and balanced utilization of cloud resources. Experiments show that our approach provides a near-optimal solution for cloud resource management when handling the heterogeneous and unpredictable video surveillance tasks dynamically over next generation network.

Hybrid Genetic Algorithm based Proficient Resource Management and Scheduling in Grid Computing

Grid networking is an aggregation of geographically dispersed computing, storage, and network resources, coordinated to deliver improved performance, higher quality of service, better utilization, and easier access to data. It enables virtual, collaborative organizations, sharing applications and data in an open, heterogeneous environment. Scheduling is the process that selects which job in the queue should be considered next. Grid Scheduling is the process of making scheduling decisions involving allocating jobs to resources over multiple administrative domains. The goal of scheduling is to minimize the make-span by finding an optimal solution. In the present Grid Networking 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. In existing system is based only on future load prediction mechanism. Based on this factor VM resource allocation is done. During this VM migration, there is no suitable criterion for unique identification and location of VM that means which VM is migrated and where to be migrated. In this paper a Grid Booster Algorithm is using. In this system VM allocation is based on resource weight [a value indicates capacity of each resource]. Based on these weights a VM resource allocation mechanism has proposed, which is considering both resource weight and future prediction. To produce a better approach for solving the problem ofVM resource migration in a Grid Networking environment, this paper demonstrates Acclimatize Genetic Algorithm based VM resource migration strategy that focuses on system load balancing.

Energy Efficient VM Live Migration and Allocation at Cloud Data Centers

Aiming at data center virtual machines Migration, allocating resource dynamically in order to reduce energy is a significant problem in cloud. This energy doesn't cause only the decrease of cloud provider's profit but also emit a large amount of carbon dioxide. This paper studies the resource allocation and live migration of Virtual Machines (VMs). It proposes a Double Threshold Migration (DTM) algorithm which takes into consideration an upper and a lower threshold of CPU utilization. These Thresholds let one select a number of VMs to do the migration. The live migration of the VMs reduces the high utilization of the servers and set on off state the unused physical machines (PMs). To solve the problem of the VM placement, the work applies a modification of the Best Fit Decreasing (MBFD) algorithm. Experiment results show that the proposed approach improve resource utilization, reduce the energy consumption and maintain the SLA (Service Level Agreement) violations with the energy constraint.

Efficient Virtual Machine Resource Management for Media Cloud Computing

Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee.