Combined performance and availability analysis of distributed resources in grid computing

Recently, distributed computing infrastructures such as grid computing, have witnessed huge developments which have a very important impact on computationally intensive scientific, industrial and engineering problems. Collaborative computing, resources access and sharing facilitated by grid computing systems amplifies concerns about cyber attacks and misbehaviors of grid resources (resources provider). In many cases, an organization may send out jobs for remote execution on untrustworthy machines. Since resource sharing and cooperation among different administrative domains are among the central goals of grid computing, it is necessary to guarantee that, among the shared resources, there are no malicious resources interested in invalidating or corrupting the job results. In order to guarantee reliable jobs execution in grid systems, we have developed a new approach for malicious grid resources tolerance using reputation to improve the efficiency of majority voting mechanisms. The grid broker service investigates the trustworthiness of the grid resources used in the voting procedure; this investigation is based on distributed and hierarchical reputation management system which maintains a reputation value for each grid resource based on its historical behavior. The validation of the result is then decided based on the grid resources reputation level.

The computational grid provides a promising platform for the efficient execution of parallel coarse grain tasks computing over very large sample space. Scheduling such applications is challenging for the heterogeneity, autonomy, and dynamic adaptability of grid resources. Assuming resource owners have a preemptive priority, we propose an adaptive algorithm of jobs scheduling based on time balancing strategy, which solves the parallel computing tasks by using the idle resources in computational grid. A mathematical model is developed to predict performance, which also considers systems with heterogeneous machine utilization and heterogeneous service distribution. The model separates the influence of machine utilization, job service rate and parallel task allocation on the completion time. According to the time balancing policy, a task is partitioned into several subtasks and scheduled, and the costs of communication are reduced. The expected value of parallel task completion time is predicted with performance model. To get better parallel computing performance, an optimal subset of heterogeneous resources with the shortest parallel executing time of tasks can be selected with the efficient algorithm. Remapping strategy is applied during scheduling, which is more suitable for the dynamic adaptability and domain autonomy in the grid.

Grid Computing is a newly emerging technology that enables users to share a large number of computing resources distributed over a network. Due to that the grid computational resources are not storable, the advance reservation of these resources are necessary for users to request resources from multiple scheduling systems at a specific time. In this paper, we use auction-based scheduling method for resource reservation. We propose a variant version of traditional ascending auction for automatic resource reservation and introduce two novel heuristic strategies to guide agents on participating actions. We also compare our bidding strategies with several other different bidding strategies for the computational resource reservation in different scenarios. The results of experiments show that our heuristic bidding strategies outperforms those methods in those cases.

The predominant technique for computing the transient distribution of a Continuous Time Markov Chain (CTMC) exploits uniformization, which is known to be stable and efficient for non-stiff to mildly-stiff CTMCs. On stiff CTMCs however, uniformization suffers from severe performance degradation. In this paper, we report on our observations and analysis of an alternative technique using Krylov subspaces. We implemented a Krylov-based extension to MRMC (Markov Reward Model Checker) and conducted extensive experiments on five case studies from different application domains. The results reveal that the Krylov-based technique is an order of magnitude faster on stiff CTMCs.

Grids are emerging as a promising solution for resource and computation demanding applications. However, the heterogeneity of resources in Grid computing, complicates resource management and scheduling of applications. In addition, the commercialization of the Grid requires policies that can take into account user requirements, and budget considerations in particular. This paper considers a basic model for workflow applications modelled as Directed Acyclic Graphs (DAGs) and investigates heuristics that allow to schedule the nodes of the DAG (or tasks of a workflow) onto resources in a way that satisfies a budget constraint and is still optimized for overall time. Two different approaches are implemented, evaluated and presented using four different types of basic DAGs.

Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems. However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time. This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to solve the job scheduling problem. The high level hybridization algorithm will keep the identity of each algorithm in performing the scheduling task. The study focuses on static grid computing environment and the metrics for optimization are the makespan and flowtime. Experiment results show that the proposed algorithm outperforms other stand-alone algorithms such as ant system, genetic algorithms, and ant colony system for makespan. However, for flowtime, ant system and genetic algorithm perform better.

During the past decade, the scientific community has witnessed the rapid accumulation of gene sequence data and data related to physiology and biochemistry of organisms. Bioinformatics tools used for efficient and computationally intensive analysis of genetic sequences require large-scale computational resources to accommodate the growing data. Grid computational resources such as the Open Science Grid and TeraGrid have proved useful for scientific discovery. GADU is a high-throughput computational system developed to automate the steps involved in accessing the Grid resources for running bioinformatics applications. This paper describes the requirements for building an automated scalable system such as GADU that can run a job simultaneously on different grids. The paper describes the resource-independent configuration of GADU using the Pegasus-based virtual data system that helps in using heterogeneous grid resources. The paper also highlights the features implemented to make GADU a gateway to computationally intensive bioinformatics applications on the Grid

Jump statistics, sojourn times, fluctuation dynamics and ergodic behavior for Markov processes in continuous time with a finite number of states

Continuous Time Markov chains (CTMCs) are widely used to model and analyze networked systems. A common analysis approach is to solve the system of balance equations governing the state transitions of a CTMC to obtain its steadystate probability distribution, and use the state probabilities to derive or compute various performance measures. In many systems, the state space of the underlying CTMC is infinite and multi-dimensional with state-dependent transitions; exact analysis of such models is challenging. For example, the exact probability distribution of the number of jobs in the Discriminatory Processor Sharing (DPS) system, first proposed by Kleinrock in 1967 [4], is still an open challenge. Likewise, obtaining the exact state probabilities of quasi-birth-and-death (QBD) processes with leveldependent transitions is known to be challenging [1]; QBDs are infinite state space multi-dimensional Markov chains in which states are organized into levels and transitions are skip-free between the levels.

Web 2.0 has been adopted by many as the best way for forming a collaborative framework e.g., sharing resources, experiences, information, knowledge and feedback. A collaborative framework for application to e-health is necessary to provide patients with the awareness that assists in improving their health. Moreover, collaborative framework can be used by physician to exchange experiences and discuss challenge cases. However, the use of Web 2.0 with healthcare applications is not simple as the use of Web 20 with other enterprise applications according to the privacy of healthcare applications, which requires high quality and security of data, availability of resources, maintainability of services, system security, and Quality of Services (QoS). To offer the required requirements, grid computing is proposed here. Grid computing supporting enterprise applications through offering massive resources through resources collaborative framework that is offering power computing, storage devices, and services. The use of grid computing by Web 2.0 requires robust model that is able to deploy, discover, invoke, and integrate resources in open standard format. Therefore, Service Oriented Architecture (SOA) is adopted as a model for managing the mixing between Web 2.0 and grid computing technologies. SOA for Web 2.0 and Grid Computing (SOAW2G) are used throughout this work to offer a fabric for e-health applications.

The monitoring of grid environments helps administrators and users keep track of the availability and loading of resources, and the management of resources is dependent on the monitoring of information data. There is not an efficient and consistent monitoring mechanism to the manipulation of devices, resources and services in Grid computing. We propose a novel monitoring framework used to gather and retrieve monitoring information of Grid environments. The monitoring system RMCS integrates and extends the existing monitoring system using service-oriented mechanism and the common information model CIM. The RMCS defines a hierarchical structure of monitoring resources, and customizes the monitoring parameters and the display way. The adoption of CIM-based monitoring service enables compatible with other grid services such as grid portal, transaction or resource management and charging. The investigation shows that this monitoring approach provides the scalable monitoring capabilities, enables to exchange information in an unrestricted and flexible way, and improves grid performance and utilization.

Current use of high-speed networks has brought a change in computing methods. Technologies enabling cooperative use of varied resources as a single powerful computer for solving large scale problems have evolved into grid computing. It is involving available computer based resources. Complexity of computation in the modern era has culminated in crossing organizational boundaries to get the desired manipulation of data through grid computing. Security in this new availability of resources on demand with an easy access in grid computing is a definite need. Researchers have developed various solutions and there are several examples of Grid computing, since it a promising trend. This paper proposes a different dimension to security in the grid with freedom in one-one access

Grid Resource Broker allocates the user job/application requests to Grid resources based upon the job/application requirements. In some cases, the broker could not be able to run the user application requests due to the non-availability of application execution environment and the required amount of nodes in the single Grid resource. To handle this situation resource broker should have the mechanism to coordinate and allocate the multiple Grid resources called co-allocation. However, the main challenge in the co-allocation mechanism is there is no guarantee in the availability of resources during the application execution that leads to the non-assimilability of the user required Quality of Service (QoS) parameters. In this research work, we have employed the Bipartite-based Heuristics Aware Advanced Reservation and Scheduling (HAARS) mechanism that select and reserve the resources from Grid/Cloud environment in an advance and near optimal manner. The proposed mechanism made use of the open-source software's such as PluS and Haizea for performing advance reservation in the Grid and Cloud environment. The proposed approach guarantees the availability of resources during the application execution, and also it achieves the user required Quality of Service (QoS) requirements.

Ad hoc grids are highly heterogeneous and dynamic, in which the availability of resources and tasks may change at any time. The paper proposes a utility based resource selection scheme for QoS satisfaction and load balancing in ad hoc grid environments. The proposed scheme intends to maximize the QoS satisfaction of ad hoc grid users and support load balancing of grid resources. For each candidate ad hoc grid resource, the scheme obtains values from the computations of utility function for QoS satisfaction and benefit maximization game for ad hoc grid resource preference. The utility function for QoS satisfaction computes the utility value based on the satisfaction of QoS requirements of the grid user request. The benefit maximization game for grid resource node preference computes the preference value from the resource point of view. Its main goal is to achieve load balancing and decrease the number of resource selection failure. The utility value and the preference value of each candidate ad hoc grid resource are combined to select the most suitable grid resource for ad hoc grid user request. In the simulation, the performance evaluation of proposed algorithm for ad hoc grid is conducted.

Interdisciplinary challenges in research today require increasingly cooperation among researchers. The demand for building up virtual research environments (VRE) becomes more and more urgent than ever before. Built on top of Web, VRE systems aim at supporting research activities with much more efficient methods for sharing data and knowledge bases. In this paper, we present our recent work on development of a general-purpose VRE system by extending Sakai, an advanced collaboration and learning platform, with tools for accessing Microsoft exchange server through WebDAV, integrating existing grid tools to access remote computing and data grid resources via Web services for remote portlets (WSRP), and managing documents to help organising workshops and conferences