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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写云计算cloud computing方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写云计算cloud computing代写方面经验极为丰富，各种代写云计算cloud computing相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 计算机代写|云计算代写cloud computing代考|Comparing the State of the Art

In this section, we present a comparison among the state of the arts on energyefficient cloud resource management solutions taking advantage of our proposed taxonomy presented in previous section. Tables $2.2$ and $2.3$ present our measurements on selected papers published in 2015 through 2021 at the power management level as well as virtualization level, respectively. Table $2.2$ reveals that the selected papers are compared with each other regarding their considered goals, dynamism, and granted workload and resource types. Table $2.3$ analyzes the selected papers with each other respecting being DVFS-aware, as well as their considered management level, migration types, plus consolidation solution. used the acronyms shown in Tables 2.2, 2.3, and $2.4$ for the considered objects, the migration costs, and considered consolidation subproblems, respectively. Besides, we utilize NM (Not Mentioned) notation in Tables $2.5$ and $2.6$, where the information is not provided in the reviewed paper.

The authors [69] have proposed an energy-aware combinatorial auction-based model for the resource allocation problem in clouds. They have regarded both MEC and MBP as their goals. Besides, at the dynamism level, they have considered virtualization techniques in the software category of DPM. CPU, RAM, Disk, as well as network are the active resources that they have applied in their proposed heuristic methods. The management level in [69] can be referred to as VM, VI, and cloud. Also, they have considered two distributed datacenters and solved VP and VS problems.

The authors [54] have focused on MPC and MPL as their goals. At the dynamism level, they have used DPM techniques, including DPS and virtualization. Also, they have applied the arbitrary types of workload. They further have considered CPU, RAM, and network bandwidth as cloud resources. Their approach is DVFSaware, and their considered management levels are VM, VI, and cloud. The type of migration that they have used is pre-copy. Moreover, they have solved consolidation subproblems OPS, UPS, and VP at the server level.

## 计算机代写|云计算代写cloud computing代考|Future Scope and Conclusion

This section presents some research issues and challenges concerning energyefficient resource management methods in the cloud environments. Although notable progress has been accomplished in applying containerization to the cloud computing systems and the adaptive management of resources and applications is widely developed; there are still many research gaps and challenges in this area needed to be further investigated as discussed below.

• Multiple system resources: Due to the broad admission of multi-core CPUs, developing energy-efficient resource management approaches plays a crucial role in leveraging such architectures. To optimize a data center’s operation, it is critical to reflect and lessen all energy elements consumption, including the cooling system and power supply units, as passive resources. To add, RAM, disk storage, and network equipment as active resources are usually overlooked by researchers. From the perspective of active resources, current works mostly focus on CPU. More resource types, like memory, network, storage, and GPU need to be regarded as parameters to create more comprehensive resource management.
• Rack consolidation and geographically distributed data center: Many big data analysis applications involve analyzing a large volume of data generated in a geographical-distributed data center. Besides, plenty of data-intensive applications, such as social networks, involve large data sets in multiple geographically distributed cloud data centers. As a case in point, Facebook receives terabytes of text, image, and video data every day from users worldwide. Another noticeable future research direction goes back to the exploration of cloud environment geographically distributed data centers and rack consolidation in addition to server and VM consolidation to make it possible to provide more reliable services in greener data centers.
• System workload: Most of the current papers applied arbitrary workloads in their study; conversely, this is a crucial issue to consider other workload types in addition to arbitrary workloads containing the batch, HPC, plus real-time application workloads.
• Security and privacy: Over the years, the ever-increasing growth of cloud data centers utilized by famous corporations such as Google, Facebook, and Microsoft can result in rise of new different administrative and security. So, addressing the security concerns which are become more and more complicated by development of new containerized services, such as Distributed Denial of Service (DDoS), has become an important issue to be considered in future research directions.
• Cognitive approach contributing to Joint VM and container consolidation: Container consolidation is an evolving technology which has a great deal better performance than VM consolidation in the light of energy consumption and performance loss. Besides, the research in [89] has justified that joint VM and container consolidation outperforms individual VM or container consolidation approaches, regarding energy consumption and QoS. By contrast, applying artificial intelligence, or machine learning, to make a cognitive decision for simultaneous migration of both VM and container is a hot research topic.
• Container security: There are some levels for container ecosystem security including image, registry, orchestrator, container, and host OS. For instance, container technologies like Docker and Kubernetes accelerate the development and deployment of application; hence, their security issues play a notable role in software development and cloud industries. The research in this field can be directed in two levels including protecting a container from the security attacks of its applications and protecting a physical server from the security attacks of its containers.

# 云计算代考

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## 计算机代写|云计算代写cloud computing代考|Future – Scope -and- Conclusion

• 多系统资源:由于多核cpu的广泛使用，开发节能的资源管理方法在利用这种体系结构方面起着至关重要的作用。为了优化数据中心的运行，反映和减少所有能源元素的消耗是至关重要的，包括作为被动资源的冷却系统和电源供应单元。在添加时，RAM、磁盘存储和网络设备作为活动资源通常被研究人员所忽视。从活动资源的角度来看，目前的工作主要集中在CPU上。更多的资源类型，如内存、网络、存储、GPU等需要作为参数来创建更全面的资源管理。

• 系统工作负载:目前大多数论文在研究中采用任意工作负载;相反，除了包含批处理、HPC和实时应用程序工作负载的任意工作负载之外，考虑其他工作负载类型也是一个关键问题。
• 容器安全:容器生态系统安全有一些级别，包括镜像、注册表、协调器、容器和主机操作系统。例如，像Docker和Kubernetes这样的容器技术加速了应用程序的开发和部署;因此，它们的安全问题在软件开发和云计算行业中扮演着重要的角色。该领域的研究可以从保护容器免受其应用程序的安全攻击和保护物理服务器免受其容器的安全攻击两个层面进行

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师