Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past.More specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs.The cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple VirtualMachines.Each virtualmachine potentially represents a different user environment such as operating system,programming environment,and applications.However,these cloud services use a large amount of electrical energy and produce greenhouse gases.To reduce the electricity cost and greenhouse gases,energy efficient algorithms must be designed.One specific area where energy efficient algorithms are required is virtual machine consolidation.With virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement requirements.This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host.The online algorithm is analyzed using a competitive analysis approach.In addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms.Our proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

Homogeneous Batch Memory Deduplication Using Clustering of Virtual Machines

Virtualization is the backbone of cloud computing,which is a developing and widely used paradigm.Byfinding and merging identical memory pages,memory deduplication improves memory efficiency in virtualized systems.Kernel Same Page Merging(KSM)is a Linux service for memory pages sharing in virtualized environments.Memory deduplication is vulnerable to a memory disclosure attack,which uses covert channel establishment to reveal the contents of other colocated virtual machines.To avoid a memory disclosure attack,sharing of identical pages within a single user’s virtual machine is permitted,but sharing of contents between different users is forbidden.In our proposed approach,virtual machines with similar operating systems of active domains in a node are recognised and organised into a homogenous batch,with memory deduplication performed inside that batch,to improve the memory pages sharing efficiency.When compared to memory deduplication applied to the entire host,implementation details demonstrate a significant increase in the number of pages shared when memory deduplication applied batch-wise and CPU(Central processing unit)consumption also increased.

A Virtual Machine Placement Strategy Based on Virtual Machine Selection and Integration

Cloud data centers face the largest energy consumption.In order to save energy consumption in cloud data centers,cloud service providers adopt a virtual machine migration strategy.In this paper,we propose an efficient virtual machine placement strategy(VMP-SI)based on virtual machine selection and integration.Our proposed VMP-SI strategy divides the migration process into three phases:physical host state detection,virtual machine selection and virtual machine placement.The local regression robust(LRR)algorithm and minimum migration time(MMT)policy are individual used in the first and section phase,respectively.Then we design a virtual machine migration strategy that integrates the process of virtual machine selection and placement,which can ensure a satisfactory utilization efficiency of the hardware resources of the active physical host.Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics.

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.

XEN Virtual Machine Technology and Its Security Analysis

This paper interprets the essence of XEN and hardware virtualization technology, which make the virtual machine technology become the focus of people＇s attention again because of its impressive performance. The security challenges of XEN are mainly researched from the pointes of view： security bottleneck, security isolation and share, life-cycle, digital copyright protection, trusted virtual machine and managements, etc. These security problems significantly affect the security of the virtual machine system based on XEN. At the last, these security measures are put forward, which will be a useful instruction on enhancing XEN security in the future.

Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

Security Architecture of Trusted Virtual Machine Monitor for Trusted Computing

With analysis of limitations Trusted Computing Group （TCG） has encountered, we argued that virtual machine monitor （VMM） is the appropriate architecture for implementing TCG specification. Putting together the VMM architecture, TCG hardware and application-oriented ＂thin＂ virtual machine （VM）, Trusted VMM-based security architecture is present in this paper with the character of reduced and distributed trusted computing base （TCB）. It provides isolation and integrity guarantees based on which general security requirements can be satisfied.

Research on optimization of virtual machine memory access based on NUMA architecture

With the rapid development of big data and artificial intelligence(AI),the cloud platform architecture system is constantly developing,optimizing,and improving.As such,new applications,like deep computing and high-performance computing,require enhanced computing power.To meet this requirement,a non-uniform memory access(NUMA)configuration method is proposed for the cloud computing system according to the affinity,adaptability,and availability of the NUMA architecture processor platform.The proposed method is verified based on the test environment of a domestic central processing unit(CPU).

A Resource Management Algorithm for Virtual Machine Migration in Vehicular Cloud Computing

In recent years,vehicular cloud computing(VCC)has gained vast attention for providing a variety of services by creating virtual machines(VMs).These VMs use the resources that are present in modern smart vehicles.Many studies reported that some of these VMs hosted on the vehicles are overloaded,whereas others are underloaded.As a circumstance,the energy consumption of overloaded vehicles is drastically increased.On the other hand,underloaded vehicles are also drawing considerable energy in the underutilized situation.Therefore,minimizing the energy consumption of the VMs that are hosted by both overloaded and underloaded is a challenging issue in the VCC environment.The proper and efcient utilization of the vehicle’s resources can reduce energy consumption signicantly.One of the solutions is to improve the resource utilization of underloaded vehicles by migrating the over-utilized VMs of overloaded vehicles.On the other hand,a large number of VM migrations can lead to wastage of energy and time,which ultimately degrades the performance of the VMs.This paper addresses the issues mentioned above by introducing a resource management algorithm,called resource utilization-aware VM migration(RU-VMM)algorithm,to distribute the loads among the overloaded and underloaded vehicles,such that energy consumption is minimized.RU-VMM monitors the trend of resource utilization to select the source and destination vehicles within a predetermined threshold for the process of VM migration.It ensures that any vehicles’resource utilization should not exceed the threshold before or after the migration.RU-VMM also tries to avoid unnecessary VM migrations between the vehicles.RU-VMM is extensively simulated and tested using nine datasets.The results are carried out using three performance metrics,namely number of nal source vehicles(nfsv),percentage of successful VM migrations(psvmm)and percentage of dropped VM migrations(pdvmm),and compared with threshold-based algorithm(i.e.,threshold)and cumulative sum(CUSUM)algorithm.The comparisons show that the RU-VMM algorithm performs better than the existing algorithms.RU-VMM algorithm improves 16.91%than the CUSUM algorithm and 71.59%than the threshold algorithm in terms of nfsv,and 20.62%and 275.34%than the CUSUM and threshold algorithms in terms of psvmm.

Virtual Machine软件在火力发电厂控制系统及课件培训中的应用

论文就Virtual Machine软件在火力发电厂Windows XP控制系统中应用遇到的问题进行了论述,并针对问题提出了相应的措施及方案,以期为Virtual Machine软件在更多领域中应用提供参考资料。

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper,we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment.In general,the different slices for different task scenarios exist in the same edge layer synchronously.A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity.In the condition,the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment.Based on the slicing and container concept,we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme(TDACP).The scheme divides the resource allocation and management work into three stages in this paper:In the first stage,it designs reasonably strategy to allocate resources to different task slices according to demand.In the second stage,it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement(SLA)of the virtual machine in different slices.In the third stage,it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers.Thus,it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost.The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy.It adjusts the number of equivalent virtual machines based on the SLA range of system parameter,and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear.The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.

An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Cloud Computing provides various services to the customer in aflex-ible and reliable manner.Virtual Machines(VM)are created from physical resources of the data center for handling huge number of requests as a task.These tasks are executed in the VM at the data center which needs excess hosts for satis-fying the customer request.The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time.This process is carried out based on various algorithms which follow a pre-defined capacity of source VM leads to the capacity issue at the destination VM.The proposed VM migration technique performs the migration process based on the request of the requesting host machine.This technique can perform in three ways namely single VM migration,Multiple VM migration and Cluster VM migration.Common Deployment Manager(CDM)is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their customer.The VM migration requests are handled with an exposure of the source host capabilities.The proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high reliabil-ity.The objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM.

Optimal Load Balancing in Cloud Environment of Virtual Machines

Cloud resource scheduling is gaining prominence with the increasingtrends of reliance on cloud infrastructure solutions. Numerous sets of cloudresource scheduling models were evident in the literature. Cloud resource scheduling refers to the distinct set of algorithms or programs the service providersengage to maintain the service level allocation for various resources over a virtualenvironment. The model proposed in this manuscript schedules resources of virtual machines under potential volatility aspects, which can be applied for anypriority metric chosen by the server administrators. Also, the model can be flexible for any time frame-based analysis of the load factor. The model discussed inthis manuscript relies on the Bollinger Bands tool for understanding the potentialvolatility aspects of a Virtual Machine. The experimental study of the model compared to the contemporary load balancing model called STLB (Starvation Threshold-based Load Balancing) refers to a simple and potential model that can bemore pragmatic for sustainable ways of load balancing.

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

Load balancing is vital for the efficient and long-term operation of cloud data centers.With virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consolidation.However,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability.Therefore,we provide a new approach,called Prepartition,for load balancing.It partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM request.In this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained manner.Simulations with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_makespan.We also extend Prepartition to online load balancing.Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.

A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems

Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM)management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.

Design and verification of a lightweight reliable virtual machine monitor for a many-core architecture

Virtual machine monitors （VMMs） play a central role in cloud computing. Their reliability and availability are critical for cloud computing. Virtualization and device emu- lation make the VMM code base large and the interface be- tween OS and VMM complex. This results in a code base that is very hard to verify the security of the VMM. For exam- ple, a misuse of a VMM hyper-call by a malicious guest OS can corrupt the whole VMM. The complexity of the VMM also makes it hard to formally verify the correctness of the system＇s behavior. In this paper a new VMM, operating sys- tem virtualization （OSV）, is proposed. The multiprocessor boot interface and memory configuration interface are virtu- alized in OSV at boot time in the Linux kernel. After booting, only inter-processor interrupt operations are intercepted by OSV, which makes the interface between OSV and OS sim- ple. The interface is verified using formal model checking, which ensures a malicious OS cannot attack OSV through the interface. Currently, OSV is implemented based on the AMD Opteron multi-core server architecture. Evaluation re- sults show that Linux running on OSV has a similar perfor- mance to native Linux. OSV has a performance improvement of 4%-13% over Xen.

Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment

Virtualization technology has been widely used to virtualize single server into multiple servers, which not only creates an operating environment for a virtual machine-based cloud computing platform but also potentially improves its efficiency. Currently, most task scheduling-based algorithms used in cloud computing environments are slow to convergence or easily fall into a local optimum. This paper introduces a Greedy Particle Swarm Optimization（G＆PSO） based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a particle swarm optimization algorithm derived from a virtual machine-based cloud platform. The archived experimental results show that the algorithm exhibits better performance such as a faster convergence rate, stronger local and global search capabilities, and a more balanced workload on each virtual machine. Therefore, the G＆PSO algorithm demonstrates improved virtual machine efficiency and resource utilization compared with the traditional particle swarm optimization algorithm.

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine （VM） is becoming a major issue for cloud providers. The existing virtual machine placement （VMP） solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization （MLU）. On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.

I-Neat:An Intelligent Framework for Adaptive Virtual Machine Consolidation

With the increasing use of cloud computing,high energy consumption has become one of the major challenges in cloud data centers.Virtual Machine(VM)consolidation has been proven to be an efficient way to optimize energy consumption in data centers,and many research works have proposed to optimize VM consolidation.However,the performance of different algorithms is related with the characteristics of the workload and system status;some algorithms are suitable for Central Processing Unit(CPU)-intensive workload and some for web application workload.Therefore,an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms.Neat is an open-source dynamic VM consolidation framework,which is well integrated into OpenStack.However,it cannot conduct dynamic algorithm scheduling,and VM consolidation algorithms in Neat are few and basic,which results in low performance for energy saving and Service-Level Agreement(SLA)avoidance.In this paper,an Intelligent Neat framework(I-Neat)is proposed,which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system.The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms.The algorithm library is designed based on a template,and in addition to the algorithms of Neat,I-Neat adds six new algorithms to the algorithm library.Furthermore,the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code.Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.