An Adaptive Dynamic Grid-Based approach to DDM for large-scale distributed simulation systems

Data Distribution Management (DDM) plays a key role in traffic volume control of large-scale distributed simulations. In recent years, several solutions have been devised to make DDM more efficient and adaptive to different traffic conditions. Examples of such systems include the Region-Based, Fixed Grid-Based, Hybrid, and Dynamic Grid-Based (DGB) schemes. However, less effort has been directed toward improving the processing performance of DDM techniques. This paper presents a novel DDM scheme called the Adaptive Dynamic Grid-Based (ADGB) scheme that optimizes DDM time through analysis of matching performance. ADGB uses an advertising scheme in which information about the target cell involved in the process of matching subscribers to publishers is known in advance. An important concept known as the Distribution Rate (DR) is devised. The distribution rate represents the relative processing load and communication load generated at each federate. The matching performance and the distribution rate are used as part of the ADGB method to select, throughout the simulation, the devised advertisement scheme that achieves the maximum gain with acceptable network traffic overhead. If we assume the same worst case propagation delays, when the matching probability is high, the performance estimation of ADGB has shown that a maximum efficiency gain of 66% can be achieved over the Dynamic Grid-Based scheme. The novelty of the ADGB scheme is its focus on improving performance, an important (and often forgotten) goal of DDM strategies.     

Dynamic Grid-Based Approach to Data Distribution Management

Data distribution management (DDM) is one of the services defined by the DoD High Level Architecture and is necessary to provide efficient, scalable mechanisms for distributing state updates and interaction information in large scale distributed simulations. In this paper, we focus on data distribution management mechanisms (also known as filtering) used for real time training simulations. We propose a new method of DDM, which we refer to as the dynamic grid-based approach. Our scheme is based on a combination of a fixed grid-based method, known for its scalability, and a region-based strategy, which provides greater accuracy than the fixed grid-based method. We describe our DDM algorithm, its implementation, and report on the performance results that we have obtained using the RTI-Kit framework. Our results clearly indicate that our scheme is scalable and that it reduces the message overhead by 40%, and the number of multicast groups used by 98% when compared to the fixed grid-based allocation scheme using 10 nodes, 1000 objects, and 20,000 grid cells.     

A performance comparison of mobility anchor point selection schemes in Hierarchical Mobile IPv6 networks

Hierarchical Mobile IPv6 (HMIPv6) introduces a mobility anchor point (MAP) that localizes the signaling traffic and hence reduces the handoff latency. In addition to processing binding update messages from mobile nodes (MNs) on behalf of MNs’ home agents (HAs), the MAP performs data traffic tunneling destined to or originated from MNs, both of which will burden the MAP substantially as the network size grows. To provide scalable and robust mobile Internet services to a large number of visiting MNs, multiple MAPs will be deployed. In such an environment, how to select an appropriate MAP has a vital effect on the overall network performance. In this paper, we choose four MAP selection schemes: the furthest MAP selection scheme, the nearest MAP selection scheme, the mobility-based MAP selection scheme, and the adaptive MAP selection scheme. Then, we compare their performances quantitatively in terms of signaling overhead and load balancing. It can be shown that the dynamic schemes (i.e., the mobility-based and the adaptive MAP selection schemes) are better than the static schemes (i.e., the furthest and the nearest MAP selection schemes), since the dynamic schemes can select the serving MAP depending on the MN’s characteristics, e.g., mobility and session activity. In addition, the adaptive MAP selection scheme achieves low implementation overhead and better load balancing compared with the mobility-based MAP selection scheme.     

一种基于网格的DDM区域匹配算法

在大规模分布式交互仿真中,数据分发管理(DDM)的重要功能是减少联邦成员接收不相关数据,实现数据过滤。它允许联邦成员在路径空间中通过更新区域或订购区域表达它们要发送或接收数据的范围,通过区域匹配运算确定数据供求关系,实现数据过滤。其关键是如何减少需要匹配的区域,以减少区域匹配运算量,文章以此为目的,提出一种基于网格的区域匹配算法。     

A speculative load adaptive streaming scheme for efficient video delivery

Video on demand services require video broadcast schemes to provide efficient and reliable performance under various client request loads. In this paper, we have developed an efficient request load adaptive broadcast scheme, speculative load adaptive streaming scheme (SLAS), that requires lower service bandwidth than previous approaches, regardless of request rate. We have provided both analysis and simulation to show the performance gain over previous schemes. In this paper, we provide the theoretic upper bound of the continuous segment allocations on channels. We found that the number of allocated segments of the SLAS is close to the theoretic upper bound when compared with other schemes over various numbers of stream channels. Our analysis of client waiting time is almost identical to simulation results about all client requests. By simulation, we compared the required service bandwidth and storage requirements of the SLAS scheme and other schemes and found the SLAS scheme is an efficient broadcast scheme as compared to well known seamless channel transition schemes.     

Disseminating streaming data in a dynamic environment: an adaptive and cost-based approach

In a distributed stream processing system, streaming data are continuously disseminated from the sources to the distributed processing servers. To enhance the dissemination efficiency, these servers are typically organized into one or more dissemination trees. In this paper, we focus on the problem of constructing dissemination trees to minimize the average loss of fidelity of the system. We observe that existing heuristic-based approaches can only explore a limited solution space and hence may lead to sub-optimal solutions. On the contrary, we propose an adaptive and cost-based approach. Our cost model takes into account both the processing cost and the communication cost. Furthermore, as a distributed stream processing system is vulnerable to inaccurate statistics, runtime fluctuations of data characteristics, server workloads, and network conditions, we have designed our scheme to be adaptive to these situations: an operational dissemination tree may be incrementally transformed to a more cost-effective one. Our adaptive strategy employs distributed decisions made by the distributed servers independently based on localized statistics collected by each server at runtime. For a relatively static environment, we also propose two static tree construction algorithms relying on apriori system statistics. These static trees can also be used as initial trees in a dynamic environment. We apply our schemes to both single- and multi-object dissemination. Our extensive performance study shows that the adaptive mechanisms are effective in a dynamic context and the proposed static tree construction algorithms perform close to optimal in a static environment.     

Dynamic load balancing in IP-over-WDM optical burst switching networks

In this paper, we consider the problem of dynamic load balancing in wavelength division multiplexing (WDM)-based optical burst switching (OBS) networks. We propose a load balancing scheme based on adaptive alternate routing aimed at reducing burst loss. The key idea of adaptive alternate routing is to reduce network congestion by adaptively distributing the load between two pre-determined link-disjoint alternative paths based on the measurement of the impact of traffic load on each of them. We develop two alternative-path selection schemes to select link-disjoint alternative paths to be used by adaptive alternate routing. The path selection schemes differ in the way the cost of a path is defined and in the assumption made about the knowledge of the traffic demands. Through extensive simulation experiments for different traffic scenarios, we show that the proposed dynamic load balancing algorithm outperforms the shortest path routing and static alternate routing algorithms.     

Modeling of Adaptive Load Balancing with Hybrid F/CDMA and Sectorization Schemes in Mobile Communication Networks

In this paper, we investigate the load balancing problem in mobile communications by considering sectorization and a hybrid F/CDMA scheme (HFCS) jointly in the scenario of uneven traffic distributions. The problem is formulated as a combinatorial optimization model, subject to quality of service (QoS) requirements, and solved by the Lagrangean relaxation approach. In addition, Lagrangean multipliers are used to conduct sensitivity analysis. The model’s objective is to minimize the weighted call blocking rate in terms of the distribution diversity. The model’s performance is evaluated by the proposed HFCS, which is an adaptive scheme (AS). We compare the performance of AS with that of a non-adaptive (NA) scheme, which is a common power control method. Experiment results show that combining sectorization with the bandwidth segmentation scheme provides effective adaptive load balancing (ALB). The performance improvement achieved by the proposed adaptive scheme over the common power control scheme is as high as 68%. Moreover, under the scheme, the performance improves as the traffic load increases. Load balancing improves even further when AS is combined with the sectorization.     

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses. There must be a need of better server load balancing mechanism for improving the performance of web services in business. Though many load balancing methods exist, there is still a need for sophisticated load balancing mechanism for not letting the clients to get frustrated. In this work, the server with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests. The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low, medium and high load by the load balancing application. Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system. Many Load Balancing schemes are based on the graded thresholds, because the exact information about the network flux is difficult to obtain. Using two thresholds L and U, it is possible to indicate the load on particular server as low, medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L, between L and U or above U respectively. However, the existing works of load balancing in the server farm incorporate fixed time to measure real time response time, which in general are not optimal for all traffic conditions. Therefore, an algorithm based on Proportional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal performance. The emulation results has shown a significant gain in the performance by tuning the threshold time. In addition to that, tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune the fixed time slots.     

基于索引排序的快速动态区域匹配算法

数据分发管理实现基于值的过滤,可进一步减少大规模仿真中盟员接收冗余数据的可能性和网络中的数据流量.数据分发管理实现的关键是区域匹配算法的实现,高效、精确的区域匹配一直是数据分发管理追求的目标.现有的区域匹配算法如直接匹配法、网格法、基于分类的算法等都不很理想,或者过滤效果不佳,或者耗时较大,难以适应大规模分布式仿真的需要.论文针对在区域比较多的大规模分布式仿真系统中,区域大都需要随着仿真的推进而频繁地被动态修改的实际特点,提出了一个基于索引排序的快速动态区域匹配算法--IOBM算法,该算法将范围的上下界分别各用一个节点表示,使用指针数组来索引每一维上的范围节点,数组元素的下标值表示对应的节点值,利用区域范围更新前后的信息,将匹配限定在移动区间之内,通过对移动区间之内的链表进行直接操作来完成匹配工作,因而大大减少了匹配计算的时间,实现了高效、精确的动态匹配.该算法尤其适合区域比较多的大规模分布式仿真的需要.     

Usefulness of adaptive load sharing for parallel processing on networks of workstations

Networks of workstations (NOWs) can be used for parallel processing by using public domain software like PVM. However, NOW-based parallel processing suffers from node heterogeneity, background load variations, and high-latency, low-bandwidth communication network. Previous studies on load sharing in NOW-based systems have indicated that, for applications using the work-pile model, a simple load sharing scheme in which the master process gives a fixed amount of work to the slave processes performs as well as any other, more complex schemes. In this paper, we propose a new adaptive load sharing scheme and evaluate its performance using a Pentium-based NOW machine. The communication network used in the system consists of the standard 10 Mbps Ethernet and the 100 Mbps fast Ethernet. We use both these networks to study their impact on the performance of our new policy. The results presented here indicate that the new policy is useful for computation-intensive applications. Copyright © 1999 John Wiley & Sons, Ltd.     

基于位移残差的数据分发管理区域匹配及传输算法

高层体系结构(high level architecture,HLA)是建模与仿真的国际标准,它规定了数据分发管理服务(data distribution management,DDM)来减少不相关数据的发送与接收,从而减少网络带宽占用.发布区域与订购区域之间匹配计算的效率是影响数据分发管理服务质量的重要因素.区域信息的传输同样非常重要,盟员间通过区域信息的传输来保持区域的正确性和一致性,但区域信息的传输同样会给网络带来负载.现有算法通常是在区域保真度和网络负载两者之间做折中处理,难以兼顾。因此,本文充分考虑了变化区域的匹配效率以及区域传输的带宽占用问题,做出以下贡献:1)定义了区域变化与区域匹配变化之间的映射关系,采用该映射可以明显减少区域改变后所需要的匹配计算次数,从而提高了匹配效率;2)将区域更新信息划分为两种数据表达,区域快照与区域残差.通过尽量传输区域残差来代替传输完整的区域信息可以有效减少区域信息传输所消耗的网络带宽.因此,本文提出一种基于位移残差的DDM匹配与传输算法,给出了算法的理论分析与测试结果,并且对算法在实际应用中的表现进行了测试对比,给出了数据对比与结果分析.实验结果表明,基于位移残差的DDM匹配与传输算法具有良好的匹配效率,并且能够大大减少网络带宽占用,尤其是在具有大量不断变化区域的情况下.     

An Efficient Adaptive Transmission Control Scheme for Large-Scale Distributed Simulation Systems

Data distribution management (DDM) is one of the most critical component of any large-scale interactive distributed simulation systems. The aim of DDM is to reduce and control the volume of information exchanged among the simulated entities (federates) in a large-scale distributed simulation system. In order to fulfill its goal, a considerable amount of DDM messages needs to be exchanged within the simulation (federation). The question of whether each message should be sent immediately after it is generated or held until it can be grouped with other DDM messages needs to be investigated further. Our experimental results have shown that the total DDM time of a simulation varies considerably depending on which transmission strategy is used. Moreover, in the case of grouping, the DDM time depends on the size of the group. In this paper, we propose a novel DDM approach, which we refer to as Adaptive Grid-based (AGB) DDM. The AGB protocol is distinct from all existing DDM implementations, because it is able to predict the average amount of data generated in each time step of a simulation. Therefore, the AGB DDM approach controls a simulation running in the most appropriate mode to achieve a desired performance. This new DDM approach consists of two adaptive control parts: 1) the Adaptive Resource Allocation Control (ARAC) scheme and 2) the Adaptive Transmission Control (ATC) scheme. The focus of this paper is on the ATC scheme. We describe how to build a switching model to predict the average amount of DDM messages generated and how the ATC scheme uses this estimation result to optimize the overall DDM time. Our experimental results provide a clear evidence that the ATC scheme is able to achieve the best performance in DDM time when compared to all existing DDM protocols using an extensive set of experimental case studies.     

基于Hash函数的分布式路由算法

随着Internet的发展,路由器成为了网络性能的瓶颈。路由算法的效率和鲁棒性成为网络研究领域的热点之一。分布式系统采用并行运行,避免了单点故障。本文提出的分布式路由器使用IP做为任务分配粒度,利用Hash算法实现负载均衡。改进了基于心跳和检测点的故障检测机制,在较低的系统开销下缩短了系统检测的响应时间。仿真试验的结果表明,该机制可行且高效。     

On the Effectiveness of Flow Aggregation in Improving Instruction Reuse in Network Processing Applications

Instruction Reuse is a microarchitectural technique that exploits dynamic instruction repetition to remove redundant computations at run-time. In this paper we examine instruction reuse of integer ALU and load instructions in network processing applications and attempt to answer the following questions: (1) How much of instruction repetition can be reused in packet processing applications?, (2) Can the temporal locality of network traffic be exploited to reduce interference in the Reuse Buffer and improve reuse? and (3) What is the effect of reuse on microarchitectural features such as resource contention and memory accesses? We use an execution driven simulation methodology to evaluate instruction reuse and find that for the benchmarks considered, 1 to 50% of the dynamic instructions are reused yielding performance improvement between 1 and 20%. To further improve reuse, a flow aggregation scheme as well as an architecture for exploiting the same is proposed. This scheme is mostly applicable to header processing applications and exploits temporal locality in packet data to uncover higher reuse. As a side effect, instruction reuse reduces memory traffic and improves performance.

     

Resource-aware event triggered distributed estimation over adaptive networks

We propose a novel algorithm for distributed processing applications constrained by the available communication resources using diffusion strategies that achieves up to a 10³ fold reduction in the communication load over the network, while delivering a comparable performance with respect to the state of the art. After computation of local estimates, the information is diffused among the processing elements (or nodes) non-uniformly in time by conditioning the information transfer on level-crossings of the diffused parameter, resulting in a greatly reduced communication requirement. We provide the mean and mean-square stability analyses of our algorithms, and illustrate the gain in communication efficiency compared to other reduced-communication distributed estimation schemes.     

Dynamic storage and access load balancing for answering range queries in peer-to-peer networks

Recently, many applications have used Peer-to-Peer (P2P) systems to overcome the current problems with client/server systems such as non-scalability, high bandwidth requirement and single point of failure. In this paper, we propose an efficient scheme to support efficient range query processing over structured P2P systems, while balancing both the storage load and access load. The paper proposes a rotating token scheme to balance the storage load by placing joining nodes in appropriate locations in the identifier space to share loads with already overloaded nodes. Then, to support range queries, we utilize an order-preserving mapping function to map keys to nodes in order preserving way and without hashing. This may result in an access load imbalance due to non-uniform distribution of keys in the identifier space. Thus, we propose an adaptive replication scheme to relieve overloaded nodes by shedding some load on other nodes to balance the access load. We derive a formula for estimating the overhead of the proposed adaptive replication scheme. In this study, we carry simulation experiments with synthetic data to measure the performance of the proposed schemes. Our simulation experiments show significant gains in both storage load balancing and access load balancing.     

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.