A New Algorithm by Index Parameters at Grid Environment with Static Scheduling

International audience

The utilization of the computational Grid processor network has become a common method for researchers and scientists without access to local processor clusters to avail of the benefits of parallel processing for compute-intensive applications. As a result, this demand requires effective and efficient dynamic allocation of available resources. Although static scheduling and allocation techniques have proved effective, the dynamic nature of the Grid requires innovative techniques for reacting to change and maintaining stability for users. The dynamic scheduling process requires quite powerful optimization techniques, which can themselves lack the performance required in reaction time for achieving an effective schedule solution. Often there is a trade-off between solution quality and speed in achieving a solution. This paper presents an extension of a technique used in optimization and scheduling which can provide the means of achieving this balance and improves on similar approaches currently published.KeywordsGridJob SchedulingGreat DelugeSimulated AnnealingNetworkParallel Processing

Most of the existing static scheduling schemes either ignore the communication delay or use a very simple model to estimate it. The result is that when the program is actually executed, its execution timing is unpredictable due to network behavior and load. However, if one knows the timing information and communication requirements for the program tasks as well as the network topology and behavior at compile time, one can compute (not estimate) the communication delays and network routes before execution. This information can then be used at execution-time for predictable network timing requirements. The authors show that optimal network routing is NP-complete. They then propose two heuristic algorithms, with different characteristics and complexity, to compute sub-optimal routing information for static scheduling at compile time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Total variation minimization (TVM) is a popular and useful method for accurate CT reconstruction from few-views and limited-angle data. However, the optimization procedure of previous TVM-based algorithms is very time-consuming. The purpose of this paper was to accelerate the high image quality CT reconstruction from few-views and limited-angle data. A new optimization algorithm based on the optimization transfer principle is proposed. The proposed algorithm uses TVM as the regularization term of the cost function that ensures the quality of the CT image reconstructed from few-views and limited-angle data. Additionally, half of the square of the difference between the original projection and the forward projection is used as the fidelity term. We then proved that the regularization term, i.e., 2-norm TV, is a convex function. Based on the convexity of the regularization and fidelity terms, a separable quadratic surrogate (SQS) function was proposed to substitute the regularization and fidelity terms. The solution of the cost function can be obtained by minimizing the SQS function and building the next SQS function at the minimum point. Both numerical simulations and simulations using real experimental data showed that the proposed algorithm reconstruct high-quality CT image from few-views and limited-angle data. The differences between the image reconstructed by the proposed algorithm and the images reconstructed by the previous algorithms are very small. However, the proposed algorithm required less than 1/10 time of the computational time of the previous algorithms. The image quality is not assessed in a rigorous way. The proposed algorithm can greatly accelerate the accurate CT reconstruction from few-views and limited-angle data relative to previous algorithms.

Different node types in smart grid (SG) system have different communication necessities. Different type of information such as power grid status and demand requests and responses are established between control station (CS) and different SG node types. Assigning the certain percentage of the nodes traffic to the radio access technologies (RATs) with respect to their cost functions (CFs) value can help to achieve this goal by defining the highest fitness degree between SG node type communication requirements and RATs communication characteristics to have an efficient load balance. In this study, the key performance indicators (KPIs) for CFs are the SG node communication requirements which are defined as data rate, delay, reliability, and security. The value of the work can be seen in node assigning method based on the CFs as a function of different RAT communication characteristics and node type communication requirements. The highest fitness degree between node communication needs and RAT characteristics are achieved in an efficient way which causes to make an efficient heterogeneous network. The work impact of using a certain RAT which has the best efficiency in sense of communication aspects by using a well-defined CF is the innovation of this study as well.

Satellite' sensors as well as ground-based sun photometers instruments surface are utilized gather data about the quantitative composition of components in the Earth's atmosphere. The relationships among these components produce effects in different phenomena, like regional climate change. Aerosols, consisting of particles from 0.01 to 10 µm, are atmosphere components, whose effects are still poorly understood. Through Remote Sensing, it is possible to classify them and gain information about their role in different atmospheric processes: low visibility, solar energy balance, cloud formation and increases or decreases of the quantity of precipitation. This paper describes how the study and analysis of satellite-based and ground-based measurements can be used to develop and validate a novel procedure to calculate the optical properties of temporal aerosols found in and around Guadalajara, based on previous algorithms:, to determine the aerosol size distribution function, scattering phase function, single scattering albedo, complex refractive index and asymmetry parameter. The quantifiable knowledge about the temporal and regional aerosols' optical properties will contribute to future investigations related to their quantitative effects on atmospheric processes in this region. These effects include: alteration of weather and climate, change of the tropospheric temperature, contribution to environmental ills, the formation and properties of the clouds, effects on the ecosystems, local solar energy balance, and the impacts on human health.

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application's workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG's specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.

Grid environments provide the mechanism to share heterogeneous resources among nodes. Because of the similarity between grid environments and P2P networks, the structures of P2P networks can be adapted to enhance scalability and efficiency in deployment and to search for services. In this paper, we present a membership management based on a hierarchical ring which constructs P2P-like Grid environments. The proposed approach uses only a limited number of connections, reducing communication cost. Also, it only keeps local information for membership, which leads to a further reduction in management cost. This paper analyzes the performance of the approach by simulation and compares it with other approaches.

Grid computing comprises of distributed computer systems which are geographically dispersed to share the combination of resources in a heterogeneous environment. Grid computing is one of the latest technologies to solve very complex problems in scientific applications, using the resource sharing concept in the dynamic virtual organizations. The main problem occurs in most of the organization is that the resources are become idle and underutilized. In some organizations, even the server machines can often be relatively idle. Grid computing provides an approach for exploiting these underutilized resources and thus has the possibility of substantially increasing the efficiency of resource usage. Effective and Efficient Task scheduling is an important aspect of Grid computing. The objective of this paper is that a grid scheduler first allocates the important(high priority) jobs to the resources and then it allocates the low prioritize job so as to achieve the maximum resource utilization rate, minimize the makespan and avoid the load balancing level problem. In this paper, we propose a new framework and QoS(Quality of Service) Priority Based Scheduling Algorithm for effective task scheduling to the resources in the grid environment. The algorithm is simulated using Java. The results show that our proposed QoS priority based scheduling algorithm gives better results in makespan and resource utilization rate than other algorithms such as QoS guided weighted mean time min (QWMTM), Min-Min and Max-Min heuristic algorithms.

Assigning jobs to optimum resources in a grid environment is the main aim of a grid scheduler. Communication cost has always been an important issue in grid environments. Proposing new scheduling algorithms to consider this cost accurately and allocate jobs to an optimum resource efficiently has always been of great importance. In this paper we have proposed a grid scheduling algorithm that is aware of the costs for different network paths. We have also considered soft real-time characteristics of jobs in our proposed algorithm. We have simulated and compared our algorithm with some key scheduling algorithms such as Least Load First (LLF), Random, and First Come First Served (FCFS) in Gridsim by considering soft real-time jobs. Results show the superiority of our scheduling algorithm due to its ability to predict network cost and to satisfy the demands of soft real-time tasks.

In this paper, we have described a non-intrusive, scalable administration and monitoring system for high performance grid environment. It is a fundamental building block for achieving and analyzing the performance of the storage virtualization system in a huge and heterogeneous grid storage environment. It offers a very flexible and simple model that collects nodes state information and requirements needed by the other services of our virtualization storage system improving storage distributed data performance. It is based on a multi-tiered hierarchical architecture the start-up of the monitoring and administration system.

This work is part of the ViSaGe project. This project concerns the storage virtualization applied in the grid environment. Its goal is to create and manage virtual storage resources by aggregating geographically distributed physical storage resources shared on a Grid. To these shared resources, a quality of service will be associated and related to the data storage performance. In a Grid environment, sharing resources may improve performances if these resources are well managed and if the management software obtains sufficient knowledge about the grid resources workload (computing nodes, storage nodes and links). The grid resources workload is mainly perceived by a monitoring system. Several existing monitoring systems are available for monitoring the grid resources and applications. Each one provides informations according to its aim. For example, Network Weather Service [6] is designed to monitor grid computing nodes and links. On the other hand, Netlogger [8] monitors grid resources and applications in order to detect application's bottleneck. These systems are useful for a post mortem analysis. However, in ViSaGe, we need a system that analyzes the necessary nodes state during execution time. This system is represented by the ViSaGe Administration and monitoring service "Admon". In this paper, we present our scalable distributed system: Admon. Admon traces applications, and calculates the system resources consumption (CPU, Disks, Links). Its originality consists in providing a new opportunity to improve virtual data storage performance by using workload's constraints (e.g., maximum CPU usage percentage). Many constraints will be discussed (such as time's constraint). These constraints allow performance improvement by assigning nodes to the ViSaGe's jobs in an effective manner.

Genetic programming (GP) is an artificial intelligence technique that benefits from evolutionary computations allowing computers to solve problems automatically. In this paper, we present an optimized genetic-programming-based classifier that directly solves the multi-class classification problems in data mining and image analysis. A new fitness function is proposed for multiclass classification and brain tumor detection, which is validated by 10-fold cross validation. Instead of defining static thresholds as boundaries to differentiate between multiple labels, our work presents a method of classification where a GP system learns the relationships among experiential data and models them mathematically during the evolutionary process. We propose an optimized GP classifier based on a combination of pruning subtrees and a new fitness function. An orthogonal least squares algorithm is also applied in the training phase to create a robust GP classifier. Our approach has been assessed on six multiclass datasets and on a magnetic resonance imaging (MRI) brain image for tumor detection. The results of data classification for Iris, Wine, Glass, Pima, BUPA Liver and Balance Scale datasets are compared with existing algorithms. The high accuracy of brain tumor classification provided by our GP classifier confirms the strong ability of the developed technique for complicated classification problems. We compared our approach in terms of speed with previous GP algorithms as well. The analyzed results illustrate that the developed classifier produces a productive and rapid method for classification tasks that outperforms the previous methods for more challenging multiclass classification problems.

In grid environment, resources and users from different administrative domains are integrated and coordinated each other. Grid uses open standard, general-purpose protocols and interfaces to provide nontrivial quality of services. Videoconferencing is communication between two or more people from geographically different locations by simultaneous two-way video and audio transmissions. This is a type of ’visual collaboration’ with audio-video communication. It differs from telephone calls through video display. Audio and Video communications is achieved in form of conferencing through Internet. This paper proposes design of a Video Conferencing Solution (VCS), which enables the users to explore the power of collaborative conferencing in grid environment. This solution is capable of setting up live meetings between a host and a number of participants.

This study presents a novel approach to optimizing the configuration of piezoelectric actuators for vibration control of a flexible aircraft fin. The fitness (cost) function for optimization using a genetic algorithm is derived directly from the frequency response function (FRF) obtained from a finite element model of the fin. In comparison to existing approaches, this method allows optimization on much more complex geometries where the derivation of an analytical fitness function is prohibitive or impossible. This technique is applied to two optimization problems for vibration control of the fin. First, the position of a single actuator is optimized anywhere within a judiciously pre-determined area of the fin using a genetic algorithm for polynomial surface fitting of the FRF in order to obtain a continuous fitness function. Next, the configuration of a pre-determined number of up to 48 separate actuators is optimized within the same area. The optimization approach is verified against experimental results obtained from a set of 12 actuators fixed to an experimental model of the fin.