An adaptive resource management scheme in cloud computing

There are various significant issues in resource allocation, such as maximum computing performance and green computing, which have attracted researchers’ attention recently. Therefore, how to accomplish tasks with the lowest cost has become an important issue, especially considering the rate at which the resources on the Earth are being used. The goal of this research is to design a sub-optimal resource allocation system in a cloud computing environment. A prediction mechanism is realized by using support vector regressions (SVRs) to estimate the number of resource utilization according to the SLA of each process, and the resources are redistributed based on the current status of all virtual machines installed in physical machines. Notably, a resource dispatch mechanism using genetic algorithms (GAs) is proposed in this study to determine the reallocation of resources. The experimental results show that the proposed scheme achieves an effective configuration via reaching an agreement between the utilization of resources within physical machines monitored by a physical machine monitor and service level agreements (SLA) between virtual machines operators and a cloud services provider. In addition, our proposed mechanism can fully utilize hardware resources and maintain desirable performance in the cloud environment.     

自适应实时资源管理技术研究

分析了自适应实时资源管理面临的问题,研究了自适应实时资源管理的构造和常用模式,并指出其不足,最后提出一种基于控制论的实时资源管理模式,对动态实时系统进行自适应实时资源管理.通过建立抽象的实时资源装置,给出反馈控制、自适应控制和智能控制这三种基本模型,将控制论映射到实时资源管理,更好地满足实时系统的需求.     

云计算环境下的自适应资源管理技术综述

云环境下的自适应资源管理是当前云计算研究领域的热点问题,是云计算具备弹性扩展、动态分配和资源共享等特点的关键技术支撑,具有重要的理论意义和实用价值.其主要研究点包括:虚拟机放置优化算法,虚拟资源动态伸缩模型、多IDC间的全局云计算资源调度、全局资源配置及能力规划模型等.对云环境下自适应资源管理研究现状进行分析研究,并指出当前研究中存在的一些主要问题,同时进一步展望本领域未来的研究方向.     

Camera handoff with adaptive resource management for multi-camera multi-object tracking

Camera handoff is a crucial step to obtain a continuously tracked and consistently labeled trajectory of the object of interest in multi-camera surveillance systems. Most existing camera handoff algorithms concentrate on data association, namely consistent labeling, where images of the same object are identified across different cameras. However, there exist many unsolved questions in developing an efficient camera handoff algorithm. In this paper, we first design a trackability measure to quantitatively evaluate the effectiveness of object tracking so that camera handoff can be triggered timely and the camera to which the object of interest is transferred can be selected optimally. Three components are considered: resolution, distance to the edge of the camera’s field of view (FOV), and occlusion. In addition, most existing real-time object tracking systems see a decrease in the frame rate as the number of tracked objects increases. To address this issue, our handoff algorithm employs an adaptive resource management mechanism to dynamically allocate cameras’ resources to multiple objects with different priorities so that the required minimum frame rate is maintained. Experimental results illustrate that the proposed camera handoff algorithm can achieve a substantially improved overall tracking rate by 20% in comparison with the algorithm presented by Khan and Shah.     

On runtime adaptive tile defragmentation for resource management in many-core systems

Before an application can be actually launched in a many-core system, the first thing that needs to be done is to get the application mapped to a number of tiles (cores). Such online application mapping process may unfortunately lead to a serious resource leak problem, referred as tile fragmentation, that free (uncommitted) tiles from any single contiguous region are just inadequate to accommodate the performance needs of an incoming application, although the total number of free tiles may still exceed what is required to service this application. When applications have to be mapped to noncontiguous tiles due to fragmentation, there will be obvious performance penalty due to increased communication distances. As a result, defragmentation that consolidates fragmented tiles needs to be routinely exercised, and this defragmentation process must not introduce high computation overhead that otherwise can adversely impact the system performance. In this paper, we propose a task migration-based adaptive tile defragmentation algorithm that helps consolidate running applications through online task migration. This algorithm relocates the applications’ tile regions so that a contiguous free tile region is formed and maintained. By doing so, future applications can be mapped to a region with low communication distance. Both the computation overhead and quality of defragmentation result of the proposed algorithm are adaptively set in response to the system workloads. Enabled by its low overhead, the proposed defragmentation algorithm is an effective resource management enhancement to the existing runtime task-to-tile mapping methods, with as much as 3× system throughput improvement observed in some experiments.     

Real-time adaptive radiometric compensation

Recent radiometric compensation techniques make it possible to project images onto colored and textured surfaces. This is realized with projector-camera systems by scanning the projection surface on a per-pixel basis. Using the captured information, a compensation image is calculated that neutralizes geometric distortions and color blending caused by the underlying surface. As a result, the brightness and the contrast of the input image is reduced compared to a conventional projection onto a white canvas. If the input image is not manipulated in its intensities, the compensation image can contain values that are outside the dynamic range of the projector. These will lead to clipping errors and to visible artifacts on the surface. In this article, we present an innovative algorithm that dynamically adjusts the content of the input images before radiometric compensation is carried out. This reduces the perceived visual artifacts while simultaneously preserving a maximum of luminance and contrast. The algorithm is implemented entirely on the GPU and is the first of its kind to run in real-time.     

Real-time adaptive contrast enhancement

A recursive filter approach is introduced to simplify real-time implementation of an adaptive contrast enhancement scheme for imaging sensors. With this scheme, even scenes possessing large global dynamic ranges (>40 dB) can be accommodated by the limited dynamic range (20 dB) of a display without losing the local contrast essential for image interpretation. This paper describes the recursive filter implementation of the local area contrast enhancement scheme using charge-coupled devices and the resultant real-time hardware capable of processing standard 525 and 875 line TV compatible video (from vidicons, videotape recorders, etc). Several examples from video imagery are included to demonstrate its effectiveness.     

Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

The complexity, scale and dynamic of data source in the human-centric computing bring great challenges to maintainers. It is problem to be solved that how to reduce manual intervention in large scale human-centric computing, such as cloud computing resource management so that system can automatically manage according to configuration strategies. To address the problem, a resource management framework based on resource prediction and multi-objective optimization genetic algorithm resource allocation (RPMGA-RMF) was proposed. It searches for optimal load cluster as training sample based on load similarity. The neural network (NN) algorithm was used to predict resource load. Meanwhile, the model also built virtual machine migration request in accordance with obtained predicted load value. The multi-objective genetic algorithm (GA) based on hybrid group encoding algorithm was introduced for virtual machine (VM) resource management, so as to provide optimal VM migration strategy, thus achieving adaptive optimization configuration management of resource. Experimental resource based on CloudSim platform shows that the RPMGA-RMF can decrease VM migration times while reduce physical node simultaneously. The system energy consumption can be reduced and load balancing can be achieved either.     

An adaptive model-free resource and power management approach for multi-tier cloud environments

With the development of cloud environments serving as a unified infrastructure, the resource management and energy consumption issues become more important in the operations of such systems. In this paper, we investigate adaptive model-free approaches for resource allocation and energy management under time-varying workloads and heterogeneous multi-tier applications. Specifically, we make use of measurable metrics, including throughput, rejection amount, queuing state, and so on, to design resource adjustment schemes and to make control decisions adaptively. The ultimate objective is to guarantee the summarized revenue of the resource provider while saving energy and operational costs. To validate the effectiveness, performance evaluation experiments are performed in a simulated environment, with realistic workloads considered. Results show that with the combination of long-term adaptation and short-term adaptation, the fluctuation of unpredictable workloads can be captured, and thus the total revenue can be preserved while balancing the power consumption as needed. Furthermore, the proposed approach can achieve better effect and efficiency than the model-based approaches in dealing with real-world workloads.     

视频实时自适应去雾算法

在雾天环境下,户外视频的可视性将受到极大损害,需要通过视频实时去雾来恢复视频的可视性。视频实时去雾对于单帧图像处理的速度有很高的要求,现有的图像去雾算法或是速度上达不到要求,或是速度虽快但去雾效果不理想。另外,视频还会面临拍摄场景中雾气浓度不断变化的问题,现有图像去雾算法中需要手动设置参数且参数固定,无法在雾气浓度变化的条件下始终达到理想的去雾效果。提出了一种实时的视频自适应去雾算法,该算法对视频中单帧图像进行去雾时,会基于暗原色值来区分图像区域,并对不同区域进行不同程度的去雾,在满足实时性的同时得到了很好的去雾效果。此外,该算法还基于暗通道先验设计了评价去雾结果的方法,并使用迭代的方式根据雾气浓度自动调整去雾参数,从而在视频中雾气浓度变化的情况下,始终能达到理想的去雾效果。     

An optimization-based adaptive resource management framework for economic Grids: A switching mechanism

The application of Grid computing has been broadening day by day. An increasing number of users has led to the requirement of a job scheduling process, which can benefit them through optimizing their utility functions. On the other hand, resource providers are exploring strategies suitable for economically efficient resource allocation so that they can maximize their profit through satisfying more users. In such a scenario, economic-based resource management strategies (economic models) have been found to be compelling to satisfy both communities. However, existing research has identified that different economic models are suitable for different scenarios in Grid computing. The Grid application and resource models are typically very dynamic, making it challenging for a particular model for delivering stable performance all the time. In this work, our focus is to develop an adaptive resource management architecture capable of dealing with multiple models based on the models’ domains of strengths (DOS). Our preliminary results show promising outcomes if we consider multiple models rather than relying on a single model throughout the life cycle of a Grid.     

Real-time management of complex resource allocation systems: Necessity,achievements and further challenges

Many contemporary applications, ranging from flexibly automated production systems, to automated material handling and intelligent transportation systems, to internet-based workflow management systems, and more recently, to the massively parallelized software systems that emerge in the context of the novel multi-core computing architectures, can be perceived as a set of finite resources that support a number of concurrently running processes. These processes execute in a staged manner and, at each stage, they vie for the allocation of various subsets of the system resources. To effectively support and manage the extensive levels of concurrency and operational flexibility that are contemplated for these environments, and the ensuing complexity, there is a substantial need for formal models and tools that will enable the modeling, analysis and eventually the control of the aforementioned resource allocation function so that the resulting dynamics are, both, behaviorally correct and operationally efficient. This article overviews a research program that seeks to address the aforementioned need by using the unifying abstraction of the resource allocation system (RAS) and supporting modeling frameworks, like automata, Petri nets, and Markov reward and decision processes, borrowed from the area of Discrete Event Systems (DES) theory. The presented results take advantage of the special structure that exists in the considered RAS classes, and they are characterized by their analytical rigor and computational tractability. The article also highlights the further challenges that must be addressed for the successful completion and promotion of the pursued framework.     

ARMCO: Advanced topics in resource management for ubiquitous cloud computing: An adaptive approach

Cloud Computing can be seen as one of the latest major evolution in computing offering unlimited possibility to use ICT in various domains: business, smart cities, medicine, environmental computing, mobile systems, design and implementation of cyber-infrastructures. The recent expansion of Cloud Systems has led to adapting resource management solutions for large number of wide distributed and heterogeneous datacenters. The adaptive methods used in this context are oriented on: self-stabilizing, self-organizing and autonomic systems; dynamic, adaptive and machine learning based distributed algorithms; fault tolerance, reliability, availability of distributed systems. The pay-per-use economic model of Cloud Computing comes with a new challenge: maximizing the profit for service providers, minimizing the total cost for customers and being friendly with the environment.This special issue presents advances in virtual machine assignment and placement, multi-objective and multi-constraints job scheduling, resource management in federated Clouds and in heterogeneous environments, dynamic topology for data distribution, workflow performance improvement, energy efficiency techniques and assurance of Service Level Agreements.     

传感器网络中一种实时的自适应时隙调度算法

无线传感器网络节点数目众多,MAC协议为节点分配工作时隙面临能量利用不高、节点延时较长等方面的难题。目前基于时隙调度的MAC协议一般采用等长的时隙大小,不能适应数据流量变化大的网络且忽略与网络层的融合,没有利用路由层信息来减低时隙分配算法性能代价。提出一种基于路由转发树的时隙调度算法（ATSA）,网络采用簇结构,在簇内构造一棵路由转发树,根据路由转发树形成的路径信息对节点实时获取节点每轮需要发送的数据量大小,根据节点的数据量大小来分配节点每轮需要的时隙,然后由簇头据此动态地为成员节点分配时隙,降低时隙划分的能量和时间代价,减少空闲侦听时间,避免串音。仿真表明,该算法有效地提高了网络能量利用效率,延长了网络生存周期,降低数据包的延时。     

Real-time adaptive fluid simulation with complex boundaries

In this paper, we present a new adaptive model for real-time fluid simulation with complex boundaries based on Smoothed Particle Hydrodynamics (SPH) framework. Firstly, we introduce an adaptive SPH framework that is based on our character field function composed of four factors: geometrical complexity, boundary condition, physical complexity, and complementary condition in terms of the neighboring particle number. Meanwhile, the rule for particle adaptation is presented. We also present a two-step method to fast detect collision with complex boundary. The first step is voxelization on the complex scene. In the second step, based on the result of voxelization, we propose a three-phase method to fast detect collisions between complex boundaries and particles. By using this method, we avoid most of the useless intersection detection computation and greatly enhance the computation efficiency. In addition, a subdivision of boundary is precomputed before the collision interaction method so that fluid in a scene with complex boundary can still be simulated at relatively high speed and system stability risk is reduced greatly. To further accelerate the simulation, a highly parallel fluid algorithm is presented and implemented using GPU so that we can simulate dynamic fluid with mutual interaction between fluid and complex boundary at a considerably fast speed without compromising realism.     

Real-time structured light coding for adaptive patterns

Coded structured light is a technique that allows the 3D reconstruction of poorly or non-textured scene areas. With the codes uniquely associated with visual primitives of the projected pattern, the correspondence problem is quickly solved by means of local information only, with robustness against disturbances like high surface curvatures, partial occlusions, out-of-field of view or out-of-focus. Real-time 3D reconstruction with one shot is possible with pseudo-random arrays, where the encoding is done in a single pattern using spatial neighbourhood. To correct more mismatched visual primitives and to get patterns globally more robust, a higher Hamming distance between all the used codewords should be suited. Recent works in the structured light field have shown a growing interest for adaptive patterns. These can account for geometrical or spectral specificities of the scene to provide better features matching and reconstructions. Up till today, such patterns cannot benefit from the robustness offered by spatial neighbourhood coding with a minimal Hamming distance constraint, because the existing algorithms for such a class of coding are designed with an offline coding only. In this article, we show that due to two new contributions, a mixed exploration/exploitation search behaviour and a O(n ²) to ～O(n) complexity reduction using the epipolar constraint, the real-time coding of patterns having similar properties than those coded offline can be achieved. This allows to design a complete closed-loop processing pipeline for adaptive patterns.     

低资源成本的云计算自适应实时资源监控研究

分布式云计算的云端数据量巨大,而云端资源监控则需对大数据采样,因而产生大量的计算开销与资源消耗,对此,提出了一种两阶段的自适应监控方案。首先,在训练阶段,采集适量的资源样本,计算其资源变化相关的阈值参数,并计算资源变化的质量值;然后,在自适应监控阶段,使用训练阶段的算法,根据不同的资源变化剧烈程度更新采样周期,从而实现动态的采样周期调节。试验结果证明,与静态监控算法相比,该算法具有较好的监控性能,同时具有较低的资源开销。     

Real-time range acquisition by adaptive structured light

The goal of this paper is to provide a "self-adaptive" system for real-time range acquisition. Reconstructions are based on a single frame structured light illumination. Instead of using generic, static coding that is supposed to work under all circumstances, system adaptation is proposed. This occurs on-the-fly and renders the system more robust against instant scene variability and creates suitable patterns at startup. A continuous trade-off between speed and quality is made. A weighted combination of different coding cues - based upon pattern color, geometry, and tracking - yields a robust way to solve the correspondence problem. The individual coding cues are automatically adapted within a considered family of patterns. The weights to combine them are based on the average consistency with the result within a small time-window. The integration itself is done by reformulating the problem as a graph cut. Also, the camera-projector configuration is taken into account for generating the projection patterns. The correctness of the range maps is not guaranteed, but an estimation of the uncertainty is provided for each part of the reconstruction. Our prototype is implemented using unmodified consumer hardware only and, therefore, is cheap. Frame rates vary between 10 and 25 fps, dependent on scene complexity.     

基于自适应背景的实时运动物体检测

为了能够在监控系统中实时的检测出运动物体,提出了一种能够自适应获取并更新背景的运动检测方法。它是利用连续帧差分,当前帧与背景图像差分来获取运动信息,同时选择性的更新背景。实验证明该算法运算量小,鲁棒性好,算法中通过直方图分析来获取合适的阈值,提高了系统的自动化程度。     

基于自适应精确罚函数的分布式资源分配算法

在多智能体系统中, 分布式资源分配问题是近年来研究热点之一. 分布式资源分配问题旨在通过智能体间信息交互实现资源最优配置. 其中智能体局部约束给算法设计带来巨大挑战. 首先, 针对一阶多智能体系统, 提出基于自适应精确罚函数的分布式资源分配算法, 其中各智能体利用距离函数实现局部约束求解. 此外, 自适应设计思想旨在避免算法对全局先验知识获取. 其次, 利用跟踪技术实现二阶多智能体系统算法设计. 并利用凸函数和非光滑分析法给出严谨的收敛性分析. 最后, 仿真结果验证了本文所设计优化算法对强凸分布式资源分配问题的有效性.