Adaptive and scalable load balancing for metadata server cluster in cloud-scale file systems

Big data is an emerging term in the storage industry, and it is data analytics on big storage, i.e., Cloud-scale storage. In Cloud-scale (or EB-scale) file systems, load balancing in request workloads across a metadata server cluster is critical for avoiding performance bottlenecks and improving quality of services.Many good approaches have been proposed for load balancing in distributed file systems. Some of them pay attention to global namespace balancing, making metadata distribution across metadata servers as uniform as possible. However, they do not work well in skew request distributions, which impair load balancing but simultaneously increase the effectiveness of caching and replication. In this paper, we propose Cloud Cache (C²), an adaptive and scalable load balancing scheme for metadata server cluster in EB-scale file systems. It combines adaptive cache diffusion and replication scheme to cope with the request load balancing problem, and it can be integrated into existing distributed metadata management approaches to efficiently improve their load balancing performance. C² runs as follows: 1) to run adaptive cache diffusion first, if a node is overloaded, loadshedding will be used; otherwise, load-stealing will be used; and 2) to run adaptive replication scheme second, if there is a very popular metadata item (or at least two items) causing a node be overloaded, adaptive replication scheme will be used, in which the very popular item is not split into several nodes using adaptive cache diffusion because of its knapsack property. By conducting performance evaluation in trace-driven simulations, experimental results demonstrate the efficiency and scalability of C².     

基于Kademlia的负载平衡云存储算法

针对采用主从式结构的主流云存储系统可能出现的性能瓶颈和可扩展问题,基于分布式哈希表(DHT)技术的完全分布式云存储系统成为一种新的选择。解决好节点的负载平衡问题,是此类技术获得推广的关键。研究了Kademlia算法应用于云存储系统的负载平衡性能。考虑到算法在异构环境下负载平衡性能有明显下降,改进算法在Kademlia找出的候选存储节点中根据节点的存储能力来分配负载。仿真结果表明,改进后算法的负载平衡性能有非常明显的提高,在系统模拟运行时间足够长(如1500 h以上)时,过载节点平均下降7.0%(轻载)和33.7%(重载);文件保存成功率平均提高27.2%(轻载)和35.1%(重载),而增加的通信开销可接受。     

A proactive low-overhead file replication scheme for structured P2P content delivery networks

File replication is a widely used technique for high performance in peer-to-peer content delivery networks. A file replication technique should be efficient and at the same time facilitates efficient file consistency maintenance. However, most traditional methods do not consider nodes’ available capacity and physical location in file replication, leading to high overhead for both file replication and consistency maintenance. This paper presents a proactive low-overhead file replication scheme, namely Plover. By making file replicas among physically close nodes based on nodes’ available capacities, Plover not only achieves high efficiency in file replication but also supports low-cost and timely consistency maintenance. It also includes an efficient file query redirection algorithm for load balancing between replica nodes. Theoretical analysis and simulation results demonstrate the effectiveness of Plover in comparison with other file replication schemes. It dramatically reduces the overhead of both file replication and consistency maintenance compared to other schemes. In addition, it yields significant improvements in reduction of overloaded nodes.     

A dynamic load balancing scheme with incentive mechanism in heterogeneous structured P2P networks

Dynamic load imbalance is a basic and inherent problem in structured P2P networks. Most existing research suffers from the problems of inefficiency in globally managing the nodes’ load information and consumption of network bandwidth. This paper describes the mechanisms for collecting and globally managing the dynamic load of each node, and based on which to present a load balancing strategy which transfers the load from overloaded to under loaded nodes so as to improve load balancing efficiency. In order to encourage the rational and selfish nodes to actively participate in the load balancing process, we also propose an incentive mechanism in dynamic load balancing, by which the differentiated services could be provided for the nodes according to their load balancing abilities. The simulation results indicate that our approach could tackle the load imbalance problem in structured P2P networks effectively and efficiently in terms of the load distribution and the transferred load volume.     

A grid-based dynamic load balancing approach for data-centric storage in wireless sensor networks

In many data-centric storage techniques, each event corresponds to a hashing location by event type. However, most of them fail to deal with storage memory space due to high percentage of the load is assigned to a relatively small portion of the sensor nodes. Hence, these nodes may fail to deal with the storage of the sensor nodes effectively. To solve the problem, we propose a grid-based dynamic load balancing approach for data-centric storage in sensor networks that relies on two schemes: (1) a cover-up scheme to deal with a problem of a storage node whose memory space is depleted. This scheme can adjust the number of storage nodes dynamically; (2) the multi-threshold levels to achieve load balancing in each grid and all nodes get load balancing. Simulations have shown that our scheme can enhance the quality of data and avoid hotspot of the storage while there are a vast number of the events in a sensor network.     

Dynamic Load Sharing in Peer-to-Peer Systems: When Some Peers Are More Equal than Others

Object caching and replication are the primary mechanisms for addressing load balancing in peer-to-peer systems. In structured P2P networks, object popularity is an important challenge because it can adversely impact both the request and forwarding loads in the network. To balance loads across P2P networks, the authors propose a method for dynamically updating routing tables to balance the load induced by forwarding traffic in the neighborhood of nodes holding popular objects.     

Efficient, proximity-aware load balancing for DHT-based P2P systems

Many solutions have been proposed to tackle the load balancing issue in DHT-based P2P systems. However, all these solutions either ignore the heterogeneity nature of the system, or reassign loads among nodes without considering proximity relationships, or both. In this paper, we present an efficient, proximity-aware load balancing scheme by using the concept of virtual servers. To the best of our knowledge, this is the first work to use proximity information in load balancing. In particular, our main contributions are: 1) relying on a self-organized, fully distributed k-ary tree structure constructed on top of a DHT, load balance is achieved by aligning those two skews in load distribution and node capacity inherent in P2P systems - that is, have higher capacity nodes carry more loads; 2) proximity information is used to guide virtual server reassignments such that virtual servers are reassigned and transferred between physically close heavily loaded nodes and lightly loaded nodes, thereby minimizing the load movement cost and allowing load balancing to perform efficiently; and 3) our simulations show that our proximity-aware load balancing scheme reduces the load movement cost by 11-65 percent for all the combinations of two representative network topologies, two node capacity profiles, and two load distributions of virtual servers. Moreover, we achieve virtual server reassignments in O(log N) time.     

基于DHT的P2P系统负载均衡的有效算法

基于DHT的P2P系统中,各种因素例如结点异构性和不同的文件访问率等,都可能会影响DHT系统的效率。本文提出一个基于DHT的P2P系统中有效的负载均衡算法。该算法提出一个全分布机制来维护文件访问的历史信息,用来预测未来文件访问频率。设计了一个新的负载均衡算法,当新结点加入时,历史信息和结点异构性一起用来决定最佳负载分配。在系统运行期间如果有过载结点出现也可动态执行负载重分配。该算法不使用虚服务器,减少了维护路由元数据的处理开销。     

一种新的结构化对等网负载均衡算法

研究结构化对等网(P2P)中的负载均衡问题,P2P网络的节点、延迟和处理能力差异性很大,当前负载均衡算法忽略节点差异性,造成网络负载极不平衡,容易出现"热点"问题,使负载变化大。为了更好的均衡P2P负载,提出一种新的网络负载均衡算法。算法充分考虑节点之间的差异性,对物理节点的地址空间进行动态分配,对于热点资源下载采用局部搜索算法找到邻居节点,并自动把负载转移到轻载节点上,保证节点间负载均衡。仿真结果表明,新网络负载均衡算法加快了负载均衡速度,使P2P网络负载均衡更加均衡,能够很好保持系统稳定性。     

基于网络编码的对等网流媒体网络中优化的带宽分配策略

针对采用了网络编码技术的对等网(P2P)流媒体系统应用,提出一种基于负载转移的节点带宽资源均衡策略,尽可能避免节点选择邻居节点并请求带宽资源的随意性形成的节点过载。在策略中,当某些节点过载后将选取部分带宽资源负载较轻的节点作为负载转移节点,同时将请求节点所需数据通过阶梯型带宽分配方式推送给这些选择出的负载转移节点。数值仿真表明,这种负载转移的策略能够有效降低过载节点的带宽资源占用,避免网络热区的出现。     

一种实现高效副本发布与查询的DHT覆盖网

在对等计算应用中,副本复制技术是提升查询命中率、提升查询速度、维护负载均衡的一种有效方法,然而它也提升了在存储空间和流量上的代价.研究如何在结构化的P2P覆盖网中,通过拓扑优化手段减少复制中的冗余流量和冗余副本.首先在网络中选择支配集节点作为超级节点,设计一个层次化的、体现节点邻近度的P2P覆盖网,然后基于多hash函数,开发对应的复制技术以实现低代价的副本查询.该方法能够有效地在网络中分散副本,提升查询命中率,减少冗余消息和所需存储空间.给出了性能指标的理论分析,并通过仿真验证了该方法的优越性.     

Simple Efficient Load-Balancing Algorithms for Peer-to-Peer Systems

Load balancing is a critical issue for the efficient operation of peer-to-peer (P2P) networks. We give two new load-balancing protocols whose provable performance guarantees are within a constant factor of optimal. Our protocols refine the consistent hashing data structure that underlies the Chord (and Koorde) P2P network. Both preserve Chord's logarithmic query time and near-optimal data migration cost. Consistent hashing is an instance of the distributed hash table (DHT) paradigm for assigning items to nodes in a P2P system: items and nodes are mapped to a common address space, and nodes have to store all items residing closeby in the address space. Our first protocol balances the distribution of the key address space to nodes, which yields a load-balanced system when the DHT maps items "randomly" into the address space. To our knowledge, this yields the first P2P scheme simultaneously achieving O(log n) degree, O(log n) look-up cost, and constant-factor load balance (previous schemes settled for any two of the three). Our second protocol aims to balance directly the distribution of items among the nodes. This is useful when the distribution of items in the address space cannot be randomized. We give a simple protocol that balances load by moving nodes to arbitrary locations "where they are needed." As an application, we use the last protocol to give an optimal implementation of a distributed data structure for range searches on ordered data.     

The grid, the load and the gradient

An important concern for an efficient use of distributed computing is dealing with load balancing to ensure all available nodes and their shared resources are equally exploited. In large scale systems such as volunteer computing platforms and desktop grids, centralized solutions may introduce performance bottlenecks and single points of failure. Accordingly fully distributed alternatives have been considered, due to their inherent robustness and reliability. In extremely dynamic contexts, scheduling middlewares should adapt their job scheduling policies to the actual availability and overcome the volatility and heterogeneity typical of the underlying nodes. To deal with the dynamicity of a large pool of resources, self-organizing and adaptive solutions represent a promising research direction. Solutions based on bio-inspired methodologies are particularly suitable, as they inherently provide the desired features. In this paper we present a fully distributed load balancing mechanism, called ozmos, which aims at increasing the efficiency of distributed computing systems through peer-to-peer interaction between nodes. The proposed algorithm is based on a Chord overlay, and employs ant-like agents to spread information about the current load on each node, to reschedule tasks from overloaded systems to underloaded ones, and to relocate incompatible tasks on suitable resources in heterogeneous grids. By means of several evaluation scenarios we demonstrate the effectiveness of the proposed solution in achieving system-wide load balancing, both with homogeneous and heterogeneous resources. In particular we consider the load balancing performance of our approach, its scalability, as well as its communication efficiency.     

A novel dynamic load balancing scheme for parallel systems

Adaptive mesh refinement (AMR) is a type of multiscale algorithm that achieves high resolution in localized regions of dynamic, multidimensional numerical simulations. One of the key issues related to AMR is dynamic load balancing (DLB), which allows large-scale adaptive applications to run efficiently on parallel systems. In this paper, we present an efficient DLB scheme for structured AMR (SAMR) applications. This scheme interleaves a grid-splitting technique with direct grid movements (e.g., direct movement from an overloaded processor to an underloaded processor), for which the objective is to efficiently redistribute workload among all the processors so as to reduce the parallel execution time. The potential benefits of our DLB scheme are examined by incorporating our techniques into a SAMR cosmology application, the ENZO code. Experiments show that by using our scheme, the parallel execution time can be reduced by up to 57% and the quality of load balancing can be improved by a factor of six, as compared to the original DLB scheme used in ENZO.     

基于双层ID空间的Peer-to-Peer文件系统设计与实现

提出并实现了一种建立在Peer-to-Peer搜索策略上的自组织,自适应,高效和可靠的文件系统DISPES（Double ID Space Based Peer-to-peer File System)。它在双层ID空间中构造虚拟存储节点,不仅有效地取得了文件系统的负载均衡,提高系统利用率,而且保证了动态环境中文件的可靠,快速获取,试验数据表明,DISPFS在系统接近满负荷运行和文件插入/删除操作频繁的双重压力下依然保持优良的性能。     

基于纠删码技术的云存储数据块部署方案

针对云存储系统中因忽视集群中存储节点之间的差异而引起的存储代价过高、可靠性较低、节点负载能力不足等问题,提出了段排序交换算法（FSSA）.首先对数据块部署问题进行数学建模;然后根据各个节点的负载情况进行分段,并在各个分段中依据数据可靠性的需求对节点进行初步选择;最后根据数学模型中对目标函数的分析在分段选择的结果中选取适当的节点进行数据部署.仿真结果表明,采用FSSA算法可以在保证数据存储可靠性的基础之上,降低数据存储代价、增强系统负载均衡能力.     

存储中的副本分级存储调度策略

当集群中的部分节点是廉价主机时,采用HDFS的随机存储策略可能使访问频率高的数据存储在廉价节点上,受到廉价节点的性能影响,访问时间过长,降低了集群效率。为改善以上问题,提出一种改进的副本分级存储调度策略。为减少副本调度的次数,先根据节点的CPU、内存、网络、存储负载以及网络距离来评价节点的性能,再从中选取高性能节点进行存储。副本调度以节点中副本的访问频率为依据,结合硬件配置,把访问频率高的副本尽可能存储在高性能、高配置的节点中,以加快集群响应速度。实验结果表明,改进后的策略可以在异构集群中提高副本的访问效率,优化负载均衡。     

针对访问成功率的P2P动态网络对象定位模型

针对网络海量存储系统的应用需求,提出了一个基于Peer-to-Peer思想的对象分布和定位模型,能够支持众多节点自发组成的动态网络结构.对该模型进行了比较完整的论述,依次建立了全局映射关系、路由表、对象定位和路由算法、对象索引分布方案和节点加入、退出时的维护算法,特别是提出了新的对象索引分布方案,提高了对象的平均访问成功率,围绕此方案,对模型的各组成部分进行了改进,实现了提出的5个性质.最后,通过建立模拟程序,验证了模型的分析预测结果,能够提供均衡的负载分布和较好的对象访问效率.     

Kosha: A Peer-to-Peer Enhancement for the Network File System

The storage needs of modern scientific applications are growing exponentially, and designing economical storage solutions for such applications – especially in Grid environments – is an important research topic. This work presents Kosha, a system that aims to harvest redundant storage space on cluster nodes and user desktops to provide a reliable, shared file system that acts as a large distributed storage. Kosha utilizes peer-to-peer (p2p) mechanisms to enhance the widely-used Network File System (NFS). P2P storage systems provide location transparency, mobility transparency, load balancing, and file replication – features that are not available in NFS. On the other hand, NFS provides hierarchical file organization, directory listings, and file permissions, which are missing from p2p storage systems. By blending the strengths of NFS and p2p storage systems, Kosha provides a low overhead storage solution. Our experiments show that compared to unmodified NFS, Kosha introduces a 3.3% fixed overhead and 4.5% additional overhead as nodes are increased from two to sixteen. For larger number of nodes, the additional overhead increases slowly. Kosha achieves load balancing in distributed directories, and guarantees or better file availability.*This work was supported in part by an NSF CAREER award (ACI-0238379).Troy A. Johnson was supported by a U.S. Department of Education GAANN doctoral fellowship.     

云存储中动态副本放置机制研究

数据副本管理是云计算系统管理的重要组成部分,在云计算系统的海量数据处理过程中,针对目前已知的数据存放与资源调度算法存在考虑副本动态性和可靠性的不足,提出了一种动态的副本放置机制。该机制基于区域结构,考虑数据处理时其副本的数量和放置位置,以及副本的产生对于内存和带宽等系统资源的开销:首先根据云存储中的副本信息,对被访问频率高且访问平均响应时间长的数据信息进行复制,并给出副本数量的计算方法;考虑缩小副本分布的节点选择范围,提出动态的副本放置算法——DRA,将一定范围内的节点根据提出的域的划分,进行放置筛选,以存放数据副本。实验结果表明,提出的动态放置机制不仅减少了低访问率副本对系统存储空间的浪费;同时也减少了高访问率副本所需跨节点的传输延迟,有效提高了云存储系统中的数据文件的访问效率、负载的均衡水平,以及云存储系统的可靠性和可用性。