TRAM: Technique for resource allocation and management in fog computing environment

The Journal of Supercomputing - The traditional cloud computing technology provides services to a plethora of applications by providing resources. These services support numerous industries for...     

智能家居环境中计算资源的分配方法

针对智能家居环境中计算资源分配的问题,提出了一种利用边缘计算的资源分配方法.首先,将原本全部上传云端的语音控制信息分割开,搭建了将一部分上传云端计算,剩下的部分采用边缘计算的系统模型;然后,对系统模型进行计算资源分配使得损耗的效用函数值达到最小值;最后,对系统模型进行仿真.仿真实验表明,效用函数的表达式呈开口向上的二次...     

A smart healthcare reward model for resource allocation in smart city

Today, cities face many significant challenges, and the smart city concept is a promising means to address typical traditional city problems. The wireless e-health technologies is an evolving topic in the area of telemedicine nowadays. Mobile telecommunication and the use of multimedia technologies are the core of providing better access to healthcare personnel on the move. These technologies provide equal access to medical information and expert care leading to a better and a more efficient use of resources. Mobile and Fog computing technologies can also cope with many challenges in smart healthcare resources of mobility, scalability, efficiency, and reliability. Optimal healthcare systems are particularly critical in cities, due to the highly concentrated populations. This high population increases the potential for harm and damage in the case of negligence or improper treatment. This can lead to infections and disease outbreaks, which could become epidemic situations and require containment, which is very costly. Motivated by the need for better usage and management of healthcare resources, which is crucial for reliable healthcare delivery, this paper introduces a model that can provide improved delivery and utilization of resources. The quality reward-based model was developed to study and react to the satisfaction factors of healthcare systems, and proposes an optimization-based algorithm called the Maximum Reward Algorithm (MRA), that enhances the use and delivery of healthcare resources. The algorithm has been tested with multiple experiments and simulations, and has proved that it can provide reliability, efficiency and achieves 50.1% to 77.2% performance improvement.

     

Improving quality-of-service in fog computing through efficient resource allocation

In today's world, large group migration of applications to the fog computing is registered in the information technology world. The main issue in fog computing is providing enhanced quality of service (QoS). QoS management consists of various method used for allocating fog-user applications in the virtual environment and selecting suitable method for allocating virtual resources to physical resource. The resources allocation in effective manner in the fog environment is also a major problem in fog computing; it occurs when the infrastructure is build using light-weight computing devices. In this article, the allocation of task and placement of virtual machine problems is explained in the single fog computing environment. The experiment is done and the result shows that the proposed framework improves QoS in fog environment.     

云环境下公平性优化的资源分配方法

针对云数据中心资源分配不均、效率不高、资源错位等问题,为了满足不同用户的需求,达到多种资源分配的公平性,实现资源的高效利用,提出了全局优势资源公平（GDRF）分配算法。GDRF算法采用多轮分配方式,即先通过用户已分配资源量确定分配资格,每轮再通过全局优势资源共享比和全局优势资源权重来确定具体的分配用户,分配过程充分考虑了资源的匹配情况,采用了max-min fairness思想的渐进填充方式,并且将多资源分配公平性统一度量模型运用到了算法中。实验基于一个Google集群数据模型与基于占优资源的多资源联合公平分配算法作了比较。实验结果表明,GDRF算法分配的虚拟机总量提高了12%,资源总利用率提高了0.5个百分点,公平评估值提高了约15%,并且该算法的资源组合分配的适应度较高,使得用户需求和供给更匹配。     

分布式计算中新兴古典经济学的资源分配方法

为了提高分布式计算环境下资源分配的效率,提出一种基于新兴古典经济学的资源分配方法,其重点关注如何提高整个系统的性能,使得客户得到的整体效用最大。通过将资源分配问题转化成专业化分工问题,应用超边际分析求出分配方案的最优解,从而进行分配策略制定和分配结构的动态调整。仿真试验证明,该方法能够有效地对分布式计算环境下的资源进行分配。     

Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment

Energy-efficient resource allocation within clusters and data centers is important because of the growing cost of energy. We study the problem of energy-constrained dynamic allocation of tasks to a heterogeneous cluster computing environment. Our goal is to complete as many tasks by their individual deadlines and within the system energy constraint as possible given that task execution times are uncertain and the system is oversubscribed at times. We use Dynamic Voltage and Frequency Scaling (DVFS) to balance the energy consumption and execution time of each task. We design and evaluate (via simulation) a set of heuristics and filtering mechanisms for making allocations in our system. We show that the appropriate choice of filtering mechanisms improves performance more than the choice of heuristic (among the heuristics we tested).     

An efficient and authenticated key establishment scheme based on fog computing for healthcare system

Because of its closeness to users, fog computing responds faster than cloud computing. Thus, it has been deployed to various applications, such as healthcare system. Recently, to ensure the secure communication of the fog-based healthcare system, Jia et al. proposed an authenticated key agreement scheme. Moreover, in view of the high computation cost existing in Jia et al.’s scheme, Ma et al. presented an efficient one using elliptic curve cryptography. In this paper, we observe that both the two schemes may potentially risk ephemeral key compromise attacks and need improving. Therefore, to overcome this potential risk, we propose a new authenticated scheme based on Jia et al.’s scheme using elliptic curve computational Diffie-Hellman hypothesis and hash functions. Additionally, we provide provable security under the adopted adversarial model and ProVerif simulation, and also analyze the performance in terms of computation and communication costs by comparisons. The analysis results show that the improved scheme resists the common attacks, reduces computation overhead, and has a certain significance.     

移动边缘计算环境下的动态资源分配策略

在通讯设备爆炸式增长的时代,移动边缘计算作为5G通讯技术的核心技术之一,对其进行合理的资源分配显得尤为重要。移动边缘计算的思想是把云计算中心下沉到基站部署(边缘云),使云计算中心更加靠近用户,以快速解决计算资源分配问题。但是,相对于大型的云计算中心,边缘云的计算资源有限,传统的虚拟机分配方式不足以灵活应对边缘云的计算资源分配问题。为解决此问题,提出一种根据用户综合需求变化的动态计算资源和频谱分配算法(DRFAA),采用"分治"策略,并将资源模拟成"流体"资源进行分配,以寻求较大的吞吐量和较低的传输时延。实验仿真结果显示,动态计算资源和频谱分配算法可以有效地降低用户与边缘云之间的传输时延,也可以提高边缘云的吞吐量。     

雾计算在智能电网需求调度中的应用

需求侧管理的主要目的是允许对消费者能源需求进行控制和调度,主要是利用云进行操作,但由于物理距离和数据量大,会造成延迟问题,雾计算的出现缓解了这些问题,因此提出一种可用于能源需求调度的云-雾计算架构,实现降低智能建筑的总能源成本.主要工作包括两部分:基于分布式和博弈的需求调度方法,以及用于选择将执行此分布式方法的雾节点的...     

一种智能电视终端在线自适应多资源调度方法

针对智能电视终端面临的运行时资源冲突和过载控制问题,提出一种在线自适应多资源调度方法(OARSA).该方法根据资源特点和应用优先级对独占型资源进行动态优先级抢占式调度,避免了资源冲突;采用反馈控制算法对共享型资源进行QoS调度,直到实现QoS比例公平性,并采用QoS比例压缩法消除资源过载.实验结果表明,OARSA具备过载控制能力,仅以系统总体QoS下降4.5％为代价,解决了资源冲突,并提高了系统重负载下的响应性能,且算法耗时比现有方法降低约30％,更加适用于智能电视终端的资源调度.     

The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment

Resource allocation is a complicated task in cloud computing environment because there are many alternative computers with varying capacities. The goal of this paper is to propose a model for task-oriented resource allocation in a cloud computing environment. Resource allocation task is ranked by the pairwise comparison matrix technique and the Analytic Hierarchy Process giving the available resources and user preferences. The computing resources can be allocated according to the rank of tasks. Furthermore, an induced bias matrix is further used to identify the inconsistent elements and improve the consistency ratio when conflicting weights in various tasks are assigned. Two illustrative examples are introduced to validate the proposed method.     

基于鲁棒优化的云医疗资源配置问题

医疗资源配置优化是云医疗系统高效运行的核心决策,然而,由于这种新型互联网医疗服务系统具有多组织协同、上下转诊以及诊疗时间不确定等特点,上述问题可以描述为需求不确定情形下核心医生服务时间分配优化问题.构建一个以最小化最大医疗服务成本为目标函数的云医疗资源鲁棒配置优化模型,通过引入决策者对患者诊疗时间和转诊概率两种不确定性...     

改进GA的边缘计算任务卸载与资源分配策略

针对现有边缘计算计算卸载算法存在的延迟较大且负载不均衡的问题,提出一种移动边缘计算中基于改进遗传算法的计算卸载与资源分配算法.基于提出的移动边缘计算网络构建系统模型,其中包括能耗、平均服务延迟、执行时间以及负载均衡模型.以能耗、延迟、负载均衡最小化为优化目标,利用改进的遗传算法进行求解,其中采用染色体一维表现形式、交叉和变异算子提高算法的性能.利用iFogSim和Google集群对所提算法进行模拟仿真实验,结果表明,算法种群数量和最大迭代次数的合理值分别是60和25,所提算法得到的计算卸载和资源分配策略在能耗、负载均衡、延迟和网络使用率方面的表现均优于其它算法.     

智能电网中两阶段网络切片资源分配技术

为满足网络切片在智能电网中的多样化需求,提出了一个在智能电网中基于云-边协同的切片资源分配模型.为优化网络切片分配,提出一种两阶段的切片分配模型:在第一阶段中,以用户体验最优为目标,建立了本地边缘网络的资源分配问题的优化模型,并采用拉格朗日乘子法对此最优问题进行了求解;在第二阶段中,首先将网络切片资源分配系统建模成Ma...     

A smart surface inspection system using faster R-CNN in cloud-edge computing environment

Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNN-based image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster R-CNN algorithm in the cloud-edge computing environment. The faster R-CNN as a CNN-based object detection method can efficiently identify defects in complex product images and the cloud-edge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time.     

Credit-based scheme for security-aware and fairness-aware resource allocation in cloud computing

Cloud computing systems include different types of participants with varied requirements for resources and multiple tasks; these varying requirements must be considered in the design of fairness-aware resource allocation schemes for better resources sharing. However, some participants may be malicious with a goal to damage the resource allocation fairness and increase their own utility. Hence, the resource scheduling policy must guarantee allocation fairness among the participants; further, it must ensure that fairness is not affected by the malicious usage of resources, that could cause resource exhaustion and lead to denial of service. In order to address this challenge, we propose a credit-based mechanism for resource allocation that will avoid the malicious usage of resources and, simultaneously, guarantee allocation fairness. In our scheme, a credit factor is introduced for each participant in order to evaluate the history of resource utilization and determine future resource allocation. Our model encourages a participant to release the occupied resources in timely manner after the completion of a task and imposes a punishment for malicious occupation of resources. We prove the fairness of our model and provide linear and variable gradient approaches to determine the credit factor for different scenarios. We simulate our model and perform experiments on a real cloud computing platform. The results prove the rationality, effectiveness and correctness of our approaches.     

Performance analysis based resource allocation for green cloud computing

Cloud computing has become a new computing paradigm that has huge potentials in enterprise and business. Green cloud computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. To maximize utilization and minimize total cost of the cloud computing infrastructure and running applications, resources need to be managed properly and virtual machines shall allocate proper host nodes to perform the computation. In this paper, we propose performance analysis based resource allocation scheme for the efficient allocation of virtual machines on the cloud infrastructure. We experimented the proposed resource allocation algorithm using CloudSim and its performance is compared with two other existing models.