Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach

Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid

The concept of Grid computing is becoming the most important research area in the high performance computing. Under this concept, the jobs scheduling in Grid computing has more complicated problems to discover a diversity of available resources, select the appropriate applications and map to suitable resources. However, the major problem is the optimal job scheduling, which Grid nodes need to allocate the appropriate resources for each job. In this paper, we combine Fuzzy C-Mean and Genetic Algorithms which are popular algorithms, the Grid can be used for scheduling. Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. In the experiments, we used the workload historical information and put it into our simulator. We get the better result when compared to the traditional algorithms for scheduling policies. Finally, the paper also discusses approach of the jobs classifications and the optimization engine in Grid scheduling

Predicting the distributions of under-recorded Odonata using species distribution models

1. Absences in distributional data may result either from the true absence of a species or from a false absence due to lack of recording effort. I use general linear models (GLMs) and species distribution models (SDMs) to investigate this problem in North American Odonata and present a potential solution. 2. I use multi-model selection methods based on Akaike's information criterion to evaluate the ability of water-energy variables, human population density, and recording effort to explain patterns of odonate diversity in the USA and Canada using GLMs. Water-energy variables explain a large proportion of the variance in odonate diversity, but the residuals of these models are significantly related to recorder effort. 3. I then create SDMs for 176species that are found solely in the USA and Canada using model averaging of eight different methods. These give predictions of hypothetical true distributions of each of the 176species based on climate variables, which I compare with observed distributions to identify areas where potential under-recording may occur. 4. Under-recording appears to be highest in northern Canada, Alaska, and Quebec, as well as the interior of the USA. The proportion of predicted species that have been observed is related to recorder effort and population density. Maps for individual species have been made available online () to facilitate recording in the future. 5. This analysis has illustrated a problem with current odonate recording in the form of unbalanced recorder effort. However, the SDM approach also provides the solution, targeting recorder effort in such a way as to maximise returns from limited resources

Correlated Resource Models of Internet End Hosts

Understanding and modelling resources of Internet end hosts is essential for the design of desktop software and Internet-distributed applications. In this paper we develop a correlated resource model of Internet end hosts based on real trace data taken from the SETI@home project. This data covers a 5-year period with statistics for 2.7 million hosts. The resource model is based on statistical analysis of host computational power, memory, and storage as well as how these resources change over time and the correlations between them. We find that resources with few discrete values (core count, memory) are well modeled by exponential laws governing the change of relative resource quantities over time. Resources with a continuous range of values are well modeled with either correlated normal distributions (processor speed for integer operations and floating point operations) or log-normal distributions (available disk space). We validate and show the utility of the models by applying them to a resource allocation problem for Internet-distributed applications, and demonstrate their value over other models. We also make our trace data and tool for automatically generating realistic Internet end hosts publicly available