Improved Harris Hawks Optimization Algorithm Based Data Placement Strategy for Integrated Cloud and Edge Computing

Cloud computing is considered to facilitate a more cost-effective way to deploy scientific workflows.The individual tasks of a scientific work-flow necessitate a diversified number of large states that are spatially located in different datacenters,thereby resulting in huge delays during data transmis-sion.Edge computing minimizes the delays in data transmission and supports the fixed storage strategy for scientific workflow private datasets.Therefore,this fixed storage strategy creates huge amount of bottleneck in its storage capacity.At this juncture,integrating the merits of cloud computing and edge computing during the process of rationalizing the data placement of scientific workflows and optimizing the energy and time incurred in data transmission across different datacentres remains a challenge.In this paper,Adaptive Cooperative Foraging and Dispersed Foraging Strategies-Improved Harris Hawks Optimization Algorithm(ACF-DFS-HHOA)is proposed for optimizing the energy and data transmission time in the event of placing data for a specific scientific workflow.This ACF-DFS-HHOA considered the factors influencing transmission delay and energy consumption of data centers into account during the process of rationalizing the data placement of scientific workflows.The adaptive cooperative and dispersed foraging strategy is included in HHOA to guide the position updates that improve population diversity and effectively prevent the algorithm from being trapped into local optimality points.The experimental results of ACF-DFS-HHOA confirmed its predominance in minimizing energy and data transmission time incurred during workflow execution.

Graphical-based data placement algorithm for cloud workflow

When workflow task needs several datasets from different locations m cloud, data transfer becomes a challenge. To avoid the unnecessary data transfer, a graphical-based data placement algo- rithm for cloud workflow is proposed. The algorithm uses affinity graph to group datasets while keeping a polynomial time complexity. By integrating the algorithm, the workflow engine can intelligently select locations in which the data will reside to avoid the unnecessary data transfer during the initial stage and runtime stage. Simulations show that the proposed algorithm can effectively reduce data transfer during the workflow＇ s execution.

Optimal Data Placement and Replication Approach for SIoT with Edge

Social networks(SNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called Social Internet of Things(SIot).The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources,and this task demands an efficient storage procedure.For this kind of large volume of data storage,the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency.The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently.For efficient storage of these data,we use edge server,which is part of the cloud server,in this study.Thus,the data are distributed and stored with quick access,which will reduce the latency with response.The proposed data placement approach learns with machine learning(ML)algorithm called radial basis kernel function assisted with support vector machine(RBF-SVM)to classify the data center for storing the user and friend’s data from the SIoT devices.These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots.The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning(GP)method to meet the individual user’s demand of low latency with minimum costs.This way will keep the SIoT data placement efficient and effective over time.Accordingly,this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach.(i)Rather than storing the user data in a single cloud,this study uses the edge server closest to the SIoT devices for faster access with reduced response time.(ii)The classification algorithm called RBF-SVM is used to find storage for user for reducing data replication.(iii)Dynamic GP is introduced for data placement with reduced latency and minimum cost to fulfil the dynamic nature of the SN.The simulation result of this approach obtains reduced latency of 130 ms and minimum cost compared with those of the existing data placement approaches.Therefore,our proposed data placement with ML-based learning on edge provides promising results in terms of efficiency,effectiveness,and performance with reduced latency and minimum cost.

Efficient Location-Aware Data Placement for Data-Intensive Applications in Geo-distributed Scientific Data Centers

Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have a high frequency of data access by scientists worldwide. Multiple associated data items distributed in different scientific data centers may be requested for one data processing task, and data placement decisions must respect the storage capacity limits of the scientific data centers. Therefore, the optimization of data access cost in the placement of data items in globally distributed scientific data centers has become an increasingly important goal.Existing data placement approaches for geo-distributed data items are insufficient because they either cannot cope with the cost incurred by the associated data access, or they overlook storage capacity limitations, which are a very practical constraint of scientific data centers. In this paper, inspired by applications in the field of high energy physics, we propose an integer-programming-based data placement model that addresses the above challenges as a Non-deterministic Polynomial-time（NP）-hard problem. In addition we use a Lagrangian relaxation based heuristics algorithm to obtain ideal data placement solutions. Our simulation results demonstrate that our algorithm is effective and significantly reduces overall data access cost.

Capability-Aware Data Placement for Heterogeneous Active Storage Systems

By moving computations from computing nodes to storage nodes, active storage technology provides an efficient for data-intensive high-performance computing applications. The existing studies have neglected the heterogeneity of storage nodes on the performance of active storage systems. We introduce CADP, a capability-aware data placement scheme for heterogeneous active storage systems to obtain high-performance data processing. The basic idea of CADP is to place data on storage nodes based on their computing capability and storage capability, so that the load-imbalance among heterogeneous servers can be avoided. We have implemented CADP under a parallel I/O system. The experimental results show that the proposed capability-aware data placement scheme can improve the active storage system performance significantly.

Novel Data Placement Algorithm for Distributed Storage System Based on Fault-Tolerant Domain

The 3-replica redundancy strategy is widely used to solve the problem of data reliability in large-scale distributed storage systems. However, its storage capacity utilization is only 33%. In this paper, a data placement algorithm based on fault-tolerant domain (FTD) is proposed. Owing to the fine-grained design of the FTD, the data reliability of systems using two replicas is comparable to that of current mainstream systems using three replicas, and the capacity utilization is increased to 50%. Moreover, the proposed FTD provides a new concept for the design of distributed storage systems. Distributed storage systems can take FTDs as the units for data placement, data migration, data repair and so on. In addition, fault detection can be performed independently and concurrently within the FTDs.

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation.Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network.These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner.We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption.Measured data is modeled as a Gaussian random variable with a changeable variance.A gird model is used to approximate the problem.We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search.Our experiments demonstrate that the algorithm is correct in a certain tolerance,and it is also efficient and scalable.

A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing

With the development of Computerized Business Application, the amount of data is increasing exponentially. Cloud computing provides high performance computing resources and mass storage resources for massive data processing. In distributed cloud computing systems, data intensive computing can lead to data scheduling between data centers. Reasonable data placement can reduce data scheduling between the data centers effectively, and improve the data acquisition efficiency of users. In this paper, the mathematical model of data scheduling between data centers is built. By means of the global optimization ability of the genetic algorithm, generational evolution produces better approximate solution, and gets the best approximation of the data placement at last. The experimental results show that genetic algorithm can effectively work out the approximate optimal data placement, and minimize data scheduling between data centers.

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.

移动分布式存储系统中自适应数据布局策略

分布式存储系统在移动网络场景中正受到越来越多的关注,作为其关键技术,数据布局对于提高数据分布式存储的成功率至关重要.然而,移动环境下无线信号不稳定,网络带宽波动大,传统的数据布局策略,如随机策略和存储容量感知策略,在数据布局时并未考虑节点的网络带宽,导致数据传输成功率低.面向高动态移动网络环境,针对移动分布式存储系统面临的数据布局问题,提出一种带宽感知的自适应数据布局策略.其基本思想是将网络带宽和节点上的其他信息结合,从而选择性能良好的节点,实现自适应数据布局,提高数据传输成功率.所提策略包含3个设计要点:(1)采用群组移动模型感知节点的网络带宽;(2)分组管理节点信息,减少通信开销,并利用小根堆的特性构建节点选择树;(3)自适应数据布局根据节点可用性动态选择性能良好的节点,提高数据传输成功率.实验结果表明:当网络动态变化时,所提策略的数据传输成功率相较于随机策略和存储容量感知策略分别提升30.6%,34.6%,并始终将通信开销维持在较低的水平.

混合内存架构下数据放置研究综述

当前基于DRAM和NVM的混合内存系统在系统结构领域的研究前景广阔,特别是对混合内存系统进行数据放置的研究已经成为国内外研究的热点。对混合内存架构下数据放置策略进行了研究,在介绍当前常见混合内存架构的基础上,对现有数据放置策略的设计思路进行了全面分析,主要涉及硬件/软件机制、内存访问特征、静态/动态分析、机器智能、触发方式和粒度选择等方面,并针对混合内存性能、功耗和耐久性的数据放置优化进行总结。综合分析发现,现有的混合内存数据放置策略在内存架构、数据迁移、计算成本和全局优化等方面还存在局限性,未来在架构设计以及内存管理方面的改进还有很大的研究探索空间和发展前景。

混合云环境面向安全科学工作流数据布局策略

为解决混合云环境下科学工作流数据布局问题,在考虑数据的安全需求的前提下,以优化跨数据中心传输时延为目标,提出了一种混合云环境下面向安全的科学工作流布局策略。分析数据集的安全需求以及数据中心所能提供的安全服务,提出安全等级分级规则;设计并提出基于遗传算法和模拟退火算法的自适应粒子群优化算法(adaptive particle swarm optimization algorithm based on SA and GA,SAGA-PSO),避免算法陷入局部极值,有效提高种群多样性;与其它经典布局算法对比,基于SAGA-PSO的数据布局策略在满足数据安全需求的同时能够大大降低传输时延。

基于分组遗传算法的数据中心虚拟机节能映射

近年来,随着人们对云计算业务需求持续增长,数据中心能耗日益增加,由此不仅增加了运营成本,巨大的碳排放对生态环境也产生严重的影响,数据中心节能已成为当前亟须解决的重要难题。对云数据中心的虚拟机放置(Virtual Machine Placement,VMP)进行优化能有效地提高资源利用率,同时,VMP也是减少数据中心能耗的重要技术之一;针对数据中心的能耗感知VMP问题,提出一种基于分组遗传算法的节能算法EEGGA(Energy-Efficient Grouping Genetic Algorithm),算法将节能VMP问题视为装箱问题(Bin Packing Problem,BPP),并应用基于分组编码的遗传算法对其进行求解,通过减少活动物理主机的数量(装箱数量)以实现降低数据中心能耗的目标;在算法迭代过程的交叉和变异等阶段,设计了多种启发优化策略提升子代染色体的适应度,从而提高算法的节能性能和加快迭代收敛的速度;通过仿真实验,在收敛速度和求解性能等方面将提出的算法与传统的节能遗传算法进行对比,实验结果表明:提出的算法能够有效地减少数据中心的能耗,在节能性能和求解收敛速度方面均优于其他算法。

基于CK-means与CatBoost算法的电力系统暂态稳定评估

考虑到电力系统中,失稳数据样本远少于稳定数据样本,针对数据不平衡问题,论文首先提出了基于Canopy和K-means算法的数据分解方法,对稳定和失稳样本进行分解,缓解数据不平问题对评估效果的影响;其次利用CatBoost算法对分解后的数据进行暂态稳定评估;通过新英格兰10机39节点系统仿真算例,在准确率和召回率方面与其他发现进行了比较,论文方法都具有一定的优势;同时,考虑到同步相量测量装置(Phasor Measurement Unit,PMU)配置的实际情况,检验了算法在不同PMU安装数量情况下的评估效果,论文方法较传统方法,受PMU数量的减少影响小。

云数据中心时延优化的数据放置方法

针对云数据中心数据获取效率低和服务器资源浪费问题,为优化云平台的数据访问和资源利用,文中提出了一种时延优化的云数据中心数据放置(LOP)方法。文中首先分析了云平台的性能,建立了云平台的资源利用和数据获取时间模型。然后基于非支配排序算法NSGA-Ⅲ实现了全局最优的数据放置策略,对数据资源进行合理部署,有效利用服务器的资源,提高了数据获取的效率。最后通过CloudSim仿真平台,对提出的数据放置方法进行了仿真和对比实验。实验结果表明,LOP方法能明显提高云服务器的资源利用率,缩短任务的数据获取时间。

结构监测无线传感网络数据传输优化方法

针对结构监测无线传感网络数据传输策略优化问题,本文提出一种综合考虑持久、稳定和及时的数据传输优化方法。结合结构监测无线传感网络需求和无线数据传输原理,建立了结构监测无线传感网络数据传输性能评价模型,提出了求解最优数据传输策略的协同飞行萤火虫算法,采用数值算例进行了验证。研究结果表明:本文建立的数据传输性能评价模型能够全面描述结构监测无线传感网络的延续性、可靠性和实时性;本文提出的优化求解方法的计算效率和解的质量提升均超过50%;获得的数据传输策略既能最大化网络寿命,又可最小化数据丢失和传输延迟。可为结构监测无线传感网络配置提供依据。

基于数据驱动的高校学生毕业去向画像构建与分析

新时代背景下高校学生对择业提出更高的要求,推进高校学生个性化发展具有十分重要的意义,因此,帮助学生更好地规划适合自己的职业发展方向是必要的。文章采用智能算法对某高校的学情数据进行分析,以学生的毕业去向为导向构建画像并进行可视化,同时对各类画像进行比较,为高校学生发展提出合理建议。

基于数据中心的虚拟机放置优化节能策略研究

近年来,随着5G、人工智能等新兴技术的发展,算力为千行百业的数字化转型注入强心剂。社会各行各业对算力需求的增长日益明显,运营商数据中心的服务器为保证高可用性,数据中心的高能耗俨然成为制约数据中心发展的一大阻碍,“节能增效”是数据中心的刚需。由于大量资源碎片造成的资源利用率低下和能源浪费问题。为了解决这个问题,提出了一种基于线性整数规划结合装箱的多目标优化算法。旨在通过优化虚拟机放置,进行虚拟机的二次调度,提高资源利用率,腾挪出更多的空闲主机,执行物理机下电等绿色节能操作,以达到“节能增效”目的。

Adaptive Controller Placement in Software Defined Wireless Networks

Controller placement problem(CPP)is a critical issue in software defined wireless networks(SDWN).Due to the limited power of wireless devices,CPP is facing the challenge of energy efficiency in SDWN.Nevertheless,the related research on CPP in SDWN hasn’t modeled the energy consumption of controllers so far.To prolong the lifetime of SDWN and improve the practicability of research,we rebuilt a CPP model considering the minimal transmitted power of controllers.An adaptive controller placement algorithm(ACPA)is proposed with the following two stages.First,data field method is adopted to determine sub-networks for different network topologies.Second,for each sub-network we adopt an exhaustive method to find the optimal location which meets the minimal average transmitted power to place controller.Compared with the other algorithms,the effectiveness and efficiency of the proposed scheme are validated through simulation.

Optimal Territorial Resources Placement for Multipurpose Wireless Services Using Genetic Algorithms

This paper presents a study for finding a solution to the placement of territorial resources for multipurpose wireless services considering also the restrictions imposed by the orography of the territory itself. To solve this problem genetic algorithms are used to identify sites where to place the resources for the optimal coverage of a given area. The used algorithm has demonstrated to be able to find optimal solutions in a variety of considered situations.