A Framework for Adaptive Cluster Computing Using JavaSpaces

Heterogeneous networked clusters are being increasingly used as platforms for resource-intensive parallel and distributed applications. The fundamental underlying idea is to provide large amounts of processing capacity over extended periods of time by harnessing the idle and available resources on the network in an opportunistic manner. In this paper we present the design, implementation and evaluation of a framework that uses JavaSpaces to support this type of opportunistic adaptive parallel/distributed computing over networked clusters in a non-intrusive manner. The framework targets applications exhibiting coarse grained parallelism and has three key features: (1) portability across heterogeneous platforms, (2) minimal configuration overheads for participating nodes, and (3) automated system state monitoring (using SNMP) to ensure non-intrusive behavior. Experimental results presented in this paper demonstrate that for applications that can be broken into coarse-grained, relatively independent tasks, the opportunistic adaptive parallel computing framework can provide performance gains. Furthermore, the results indicate that monitoring and reacting to the current system state minimizes the intrusiveness of the framework.     

Configurable Network Processing for Mobile Agents on the Internet

This paper presents a framework for building and deploying protocols for migrating mobile agents over the Internet. The framework enables network protocols for agent migration to be naturally implemented within mobile agents and then dynamically deployed at remote hosts by migrating the agents that perform the protocols. It is built on a hierarchical mobile agent system, called MobileSpaces, and several protocols for migrating agents for managing cluster computing systems have been designed and implemented based on the framework. This paper describes the framework and its prototype implementation, which uses Java as both the implementation language and the protocol development language.     

Autonomous learning of load and traffic patterns to improve cluster utilization

Adaptive clustering aims at improving cluster utilization for varying load and traffic patterns. Locality-based least-connection with replication (LBLCR) scheduling that comes with Linux is designed to help improve cluster utilization through adaptive clustering. A key issue with LBLCR, however, is that cluster performance depends much on a single threshold value that is used to determine adaptation. Once set, the threshold remains fixed, regardless of the load and traffic patterns. If a cluster of PCs is to adapt to different traffic patterns for high utilization, a good threshold has to be selected and used dynamically. We present in this paper an adaptive clustering framework that autonomously learns and adapts to different load and traffic patterns at runtime with no administrator intervention. The cluster is configured once and for all. As the patterns change, the cluster automatically expands/contracts to meet the changing demands. At the same time, the patterns are proactively learned so that when similar patterns emerge in the future, the cluster knows what to do to improve utilization. We have implemented this autonomous learning method and compared it with LBLCR using published Web traces. Experimental results indicate that our autonomous learning method produces high performance scalability and adaptability for different patterns. On the other hand LBLCR-based clustering suffers from performance scalability and adaptability for different traffic patterns since it is not designed to obtain good threshold values and use them at runtime.     

Load balancing on temporally heterogeneous cluster of workstations for parallel simulated annealing

Simulated annealing (SA) is a general-purpose optimization technique widely used in various combinatorial optimization problems. However, the main drawback of this technique is a long computation time required to obtain a good quality of solution. Clusters have emerged as a feasible and popular platform for parallel computing in many applications. Computing nodes on many of the clusters available today are temporally heterogeneous. In this study, multiple Markov chain (MMC) parallel simulated annealing (PSA) algorithms have been implemented on a temporally heterogeneous cluster of workstations to solve the graph partitioning problem and their performance has been analyzed in detail. Temporal heterogeneity of a cluster of workstations is harnessed by employing static and dynamic load balancing techniques to further improve efficiency and scalability of the MMC PSA algorithms.     

A Linear Domain System Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Genetic Algorithm

<正> This paper proposes a new adaptive linear domain system identification method for small unmanned aerial rotorcraft.Byusing the flash memory integrated into the micro guide navigation control module, system records the data sequences of flighttests as inputs (control signals for servos) and outputs (aircraft's attitude and velocity information).After data preprocessing, thesystem constructs the horizontal and vertical dynamic model for the small unmanned aerial rotorcraft using adaptive geneticalgorithm.The identified model is verified by a series of simulations and tests.Comparison between flight data and the one-stepprediction data obtained from the identification model shows that the dynamic model has a good estimation for real unmannedaerial rotorcraft system.Based on the proposed dynamic model, the small unmanned aerial rotorcraft can perform hovering,turning, and straight flight tasks in real flight tests.     

Vulnerability of Australian tropical savanna birds to climate change

Assessments of species vulnerability to climate change should increase the effectiveness of interventions in the current decline in biodiversity. Species vulnerability to climate change is a consequence of their sensitivity and adaptive capacity, in combination with their exposure to climate change. We apply a vulnerability assessment framework to 243 bird species inhabiting the tropical savannas of northern Australia. We build on previous vulnerability studies by including detailed data for variables relating to species sensitivity to change (relative abundance, clutch size, sensitivity to fire and distribution area), species adaptive capacity (movement behaviour and dietary breadth) and proportional changes predicted for their geographic range (i.e. exposure to climate change). These are integrated to provide a ranking of vulnerability. Our analysis found that birds of Australian tropical savannas cluster together with high sensitivity, with a few wide‐ranging increasing species with very low sensitivity. Australian tropical savanna birds have a range of adaptive capacities, and the impact of climate change on these species is predicted to be substantial. Two already endangered species are among the most vulnerable. Species largely restricted to Cape York Peninsula (a geographically distinct region) had the greatest overall vulnerability; these species were, in general, sensitive due to small distributions, sensitivity to fire frequency and had a lower capacity for dispersal. It will be important for the future of Australian tropical savanna birds to mitigate ecological threats and maintain extensive areas of suitable habitat to facilitate species dispersal.     

Load balancing and optimization of distributed proximity effect correction on a temporally heterogeneous cluster

Grayscale electron-beam lithography is a technique widely used in transferring three-dimensional structures onto the resist layer or substrate. The proximity effect caused by electron scattering in the resist imposes a severe limitation on the ultimate spatial resolution attainable by e-beam lithography. Therefore, correction of the proximity effect is essential particularly for the fine-feature, high-density circuit patterns. However, the proximity effect correction is very time-consuming due to the intensive computation required in the correction procedure and a large size of circuit data to be processed. Hence, it is an ideal candidate for distributed computing where the otherwise-unused CPU cycles of a number of computers on a network (cluster) can be efficiently utilized. One of the characteristics of such a cluster is its heterogeneity, i.e., the available computing power varies with computer and/or time. This variation may degrade the performance of distributed computing significantly. In this paper, efficient distributed implementations of grayscale proximity effect correction on a temporally heterogeneous cluster are described with the main emphasis on static and dynamic load balancing schemes and their optimization through effective task partitioning methods. The experimental results obtained on a cluster of Sun workstations shared by multiple users are presented with detailed discussion.     

Photoassimilate partitioning in nodulated soybean I. 11C methodology

An established method using ¹¹C for the in vivo measurement of photoassimilate partitioning within intact plants was applied to the characterization of partitioning of photoassimilate to soybean nodules. The method describes partitioning in terms of the magnitude and stability of partitioning flows, i.e. sink 'activity' and 'priority', and the transit time of tracer to a given sink. Leaflet labelling with ¹¹CO2 was recommended over whole shoot labelling to allow information on transport properties of the shoot to be acquired. The assumptions inherent in the method, that labelled and unlabelled photoassimilate in passage within the stem to the root system were well mixed and that tracer flow is unidirectional between source and sink (nodule), were validated. Tracer was re-exported from root to shoot, but this re-export process did not invalidate the assumption of unidirectional flow because the transit time of the re-export process was long relative to the half-life of the isotope. The transit time of tracer between entry to, and respiration from, the root system was also long (>60 min) relative to the half-life of the isotope. However, a significant fraction of tracer entering the root system was respired (c. 10% within 200 min), mainly by nodules (37% of tracer entering a nodule cluster was respired with 200 min). Therefore root-respired tracer was trapped and attributed to the nodule in partitioning calculations. A case study is presented using the method to assess changes in partitioning to nodules following treatment of the root system with nitrate, highlighting the limitation to this method of ontogenetic changes in the pattern of export from the load leaflet.     

Principles of bonding and reactivity in transition metal cluster compounds

The bonding in transition metal cluster compounds is examined by partitioning the system into the component parts suggested by expressions for the total energy. The nature of MM (metal-metal) interactions, ML (metal-ligand) interactions and LL (ligand-ligand) interactions are examined, and their effect on the stability and hence structure of the system considered. The processes by which one structure can rearrange into another are discussed. Some consideration is given to the partitioning of a cluster into ML_j fragments, and the interactions between these fragments. Isolobal analogies are discussed in this context. The emphasis of this work is on the general principles behind the structure and reactivity of transition metal cluster compounds, rather than focusing on specific systems.     

Power and environment aware control of Beowulf clusters

Beowulf clusters are now deployed worldwide, chiefly in support of scientific computing. Beowulf clusters yield high computing performance, yet they also pose several challenges: (1) heat-induced hardware failure makes large scale commodity clusters fail quite frequently and (2) cost effectiveness of the Beowulf cluster is challenged by the fact that it lacks means of adapting its power state according to varying work load. This paper addresses these issues by developing a Power and Environment Awareness Module (PEAM) for a Beowulf cluster. The busty nature of computation load in an academic environment inspired the implementation and analysis of a fixed timeout Dynamic Power Management (DPM) policy. Today it is common that many Beowulf clusters in academic environment are composed of older, recycled nodes that may lack of out-of-band management technologies, thus Advanced Configuration and Power Interface (ACPI) and Wake-on-LAN (WOL) technology is exploited to control the power state of cluster nodes. A data center environment monitoring system that uses Wireless Sensor Networks (WSN) technology is developed and deployed to realize environment awareness of the cluster. Our PEAM module has been implemented on our cluster at Purdue University, reducing the operational cost and increasing the reliability of the cluster by reducing heat generation and optimizing workload distribution in an environment aware manner.     

基于适应性循环理论的区域生态风险时空演变评估——以长江三角洲城市群为例

在城市扩张和气候变化背景下,三角洲社会与生态系统正遭受多方面风险威胁,适应性视角下的区域生态风险评估有助于理解复杂系统与风险影响之间的相互作用,为长江三角洲城市群适应性规划策略提供空间定量参考。以长江三角洲城市群社会-生态系统为研究对象,耦合适应性循环与区域生态风险评估理论,构建"潜力-连通度-韧性"适应性生态风险评估框架,从整体和动态的角度评估区域在当前与未来适应性生态风险的时空分布与各城市所处的适应性循环阶段。结果表明,适应性生态风险由沿海区-城市群-生态区域呈现由较高到高再到低的趋势,大城市外围现已出现较高风险。至2030年,风险整体呈上升趋势,高风险向中小城市和生态区域蔓延。从适应性循环阶段来看,杭州、宁波等14个城市处于生态风险较低的重组阶段。常州、南通等8个城市处于风险升高的开发阶段。上海、南京、无锡和苏州处于城市发展成熟风险开始降低的保护阶段。本研究通过评估长三角城市群适应性生态风险,得到高风险区分布与各个城市的适应性风险趋势,为长三角城市群动态变化下的适应性规划策略制定提供了科学的指导,以实现区域的可持续发展。     

Fine‐tuning the nested structure of pollination networks by adaptive interaction switching,biogeography and sampling effect in the Galápagos Islands

The structure of pollination networks, particularly its nestedness, contain important information on network assemblages. However, there is still limited understanding of the mechanisms underlying nested pollination network structures. Here, we investigate the role of adaptive interaction switching (AIS), island area, isolation, age and sampling effort in explaining the nestedness of pollination networks across ten Galápagos Islands. The AIS algorithm is inspired by Wallace's elimination of the unfit, where a species constantly replaces its least profitable mutualistic partner with a new partner selected at random. To explain network structures, we first use a dynamic model that includes functional response of pollination and AIS, with only species richness and binary connectance as input (hereafter the AIS model). Thereafter, other explanatory variables (isolation, area, age and sampling effort) were added to the model. In four out of ten islands, the pollination network was significantly nested, and predictions from the AIS model correlated with observed structures, explaining 69% variation in nestedness. Overall, in terms of independent contribution from hierarchical partitioning of variation in observed nestedness, the AIS model predictions contributed the most (37%), followed by sampling effort (28%) and island area (22%), with only trivial contributions from island isolation and age. Therefore, adaptive switching of biotic interactions seems to be key to ensure network function, with island biogeographic factors being only secondary. Although large islands could harbour more diverse assemblages and thus foster more nested structures, sufficient sampling proves to be essential for detecting non‐random network structures.     

Navigating challenges and opportunities of land degradation and sustainable livelihood development in dryland social–ecological systems: a case study from Mexico

Drylands are one of the most diverse yet highly vulnerable social–ecological systems on Earth. Water scarcity has contributed to high levels of heterogeneity, variability and unpredictability, which together have shaped the long coadaptative process of coupling humans and nature. Land degradation and desertification in drylands are some of the largest and most far-reaching global environmental and social change problems, and thus are a daunting challenge for science and society. In this study, we merged the Drylands Development Paradigm, Holling''s adaptive cycle metaphor and resilience theory to assess the challenges and opportunities for livelihood development in the Amapola dryland social–ecological system (DSES), a small isolated village in the semi-arid region of Mexico. After 450 years of local social–ecological evolution, external drivers (neoliberal policies, change in land reform legislation) have become the most dominant force in livelihood development, at the cost of loss of natural and cultural capital and an increasingly dysfunctional landscape. Local DSESs have become increasingly coupled to dynamic larger-scale drivers. Hence, cross-scale connectedness feeds back on and transforms local self-sustaining subsistence farming conditions, causing loss of livelihood resilience and diversification in a globally changing world. Effective efforts to combat desertification and improve livelihood security in DSESs need to consider their cyclical rhythms. Hence, we advocate novel dryland stewardship strategies, which foster adaptive capacity, and continuous evaluation and social learning at all levels. Finally, we call for an effective, flexible and viable policy framework that enhances local biotic and cultural diversity of drylands to transform global drylands into a resilient biome in the context of global environmental and social change.     

Habitat use and its implications to functional morphology: niche partitioning and the evolution of locomotory morphology in Lake Tanganyikan cichlids (Perciformes: Cichlidae)

Animal locomotory morphology, i.e. morphological features involved in locomotion, is under the influence of a diverse set of ecological and behavioral factors. In teleost fish, habitat choice and foraging strategy are major determinants of locomotory morphology. In this study, we assess the influence of habitat use and foraging strategy on important locomotory traits, namely the size of the pectoral and caudal fins and the weight of the pectoral fin muscles, as applied to one of the most astonishing cases of adaptive radiation: the species flock of cichlid fishes in East African Lake Tanganyika. We also examine the course of niche partitioning along two main habitat axes, the benthic vs. limnetic and the sandy vs. rocky substrate axis. The results are then compared with available data on the cichlid adaptive radiation of neighbouring Lake Malawi. We find that pectoral fin size and muscle weight correlate with habitat use within the water column, as well as with substrate composition and foraging strategies. Niche partitioning along the benthic–limnetic axis in Lake Tanganyikan cichlids seems to follow a similar course as in Lake Malawi, while the course of habitat use with respect to substrate composition appears to differ between the cichlid assemblages of these two lakes.     

Food‐web structure and ecosystem function in the Laurentian Great Lakes—Toward a conceptual model

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