Enabling Interoperability among Grid Meta-Schedulers

The goal of Grid computing is to integrate the usage of computer resources from cooperating partners in the form of Virtual Organizations (VO). One of its key functions is to match jobs to execution resources efficiently. For interoperability between VOs, this matching operation occurs in resource brokering middleware, commonly referred to as the meta-scheduler or meta-broker. In this paper, we present an approach to a meta-scheduler architecture, combining hierarchical and peer-to-peer models for flexibility and extensibility. Interoperability is further promoted through the introduction of a set of protocols, allowing meta-schedulers to maintain sessions and exchange job and resource state using Web Services. Our architecture also incorporates a resource model that enables an efficient resource matching across multiple Virtual Organizations, especially where the compute resources and state are dynamic. Experiments demonstrate these new functional features across three distributed organizations (BSC, FIU, and IBM), that internally use different job scheduling technologies, computing infrastructure and security mechanisms. Performance evaluations through actual system measurements and simulations provide the insights on the architecture’s effectiveness and scalability.     

On development of an efficient parallel loop self-scheduling for grid computing environments

The approaches to deal with scheduling and load balancing on PC-based cluster systems are famous and well-known. Self-scheduling schemes, which are suitable for parallel loops with independent iterations on cluster computer system, they have been designed in the past. In this paper, we propose a new scheme that can adjust the scheduling parameter dynamically on an extremely heterogeneous PC-based cluster and Grid computing environments in order to improve system performance. A Grid computing environment consists of multiple PC-based clusters is constructed using Globus Toolkit and MPICH-G2 middleware. The experimental results show that our scheduling can result in higher performance than other similar schemes on Grid computing environments.     

A standards‐based Grid resource brokering service supporting advance reservations,coallocation, and cross‐Grid interoperability

The problem of Grid‐middleware interoperability is addressed by the design and analysis of a feature‐rich, standards‐based framework for all‐to‐all cross‐middleware job submission. The architecture is designed with focus on generality and flexibility and builds on extensive use, internally and externally, of (proposed) Web and Grid services standards such as WSRF, JSDL, GLUE, and WS‐Agreement. The external use provides the foundation for easy integration into specific middlewares, which is performed by the design of a small set of plugins for each middleware. Currently, plugins are provided for integration into Globus Toolkit 4 and NorduGrid/ARC. The internal use of standard formats facilitates customization of the job submission service by replacement of custom components for performing specific well‐defined tasks. Most importantly, this enables the easy replacement of resource selection algorithms by algorithms that address the specific needs of a particular Grid environment and job submission scenario. By default, the service implements a decentralized brokering policy, striving to optimize the performance for the individual user by minimizing the response time for each job submitted. The algorithms in our implementation perform resource selection based on performance predictions, and provide support for advance reservations as well as coallocation of multiple resources for coordinated use. The performance of the system is analyzed with focus on overall service throughput (up to over 250 jobs per min) and individual job submission response time (down to under 1 s). Copyright © 2009 John Wiley & Sons, Ltd.     

The application hosting environment: Lightweight middleware for grid-based computational science

Grid computing is distributed computing performed transparently across multiple administrative domains. Grid middleware, which is meant to enable access to grid resources, is currently widely seen as being too heavyweight and, in consequence, unwieldy for general scientific use. Its heavyweight nature, especially on the client-side, has severely restricted the uptake of grid technology by computational scientists. In this paper, we describe the Application Hosting Environment (AHE) which we have developed to address some of these problems. The AHE is a lightweight, easily deployable environment designed to allow the scientist to quickly and easily run legacy applications on distributed grid resources. It provides a higher level abstraction of a grid than is offered by existing grid middleware schemes such as the Globus Toolkit. As a result, the computational scientist does not need to know the details of any particular underlying grid middleware and is isolated from any changes to it on the distributed resources. The functionality provided by the AHE is ‘application-centric’: applications are exposed as web services with a well-defined standards-compliant interface. This allows the computational scientist to start and manage application instances on a grid in a transparent manner, thus greatly simplifying the user experience. We describe how a range of computational science codes have been hosted within the AHE and how the design of the AHE allows us to implement complex workflows for deployment on grid infrastructure.     

Service level agreement based adaptive Grid superscheduling

Grid computing brings heterogeneity and decentralization to the world of science and technology. It leverages every bit of idle computing resources and provides a straightforward middleware for integrating cross-domain scientific devices and legacy systems. In a super big Grid, job scheduling is challenging specifically when it needs to have access to vast amount of resources. The process of mapping jobs onto Grid resources requires significant consideration in terms of Grid architecture design, consumer demands and provider revenues. In this paper, we simultaneously utilize the legacy architecture of superscheduling, forwarding strategy, service level, success rate, and service pricing strategies and finally propose a service level agreement based on adaptive superscheduling (SAS) algorithm. SAS algorithm presents unified connectivity via efficient diffusion of jobs through the Grid infrastructure that is fueled from the previous scheduling events across the Grid. Moreover, by enforcing the service level agreement terms from a rich set of ask and bid prices, system performance, and load statistics, SAS successfully boosts revenue and utilization statistics. We perform an extensive experimental analysis for different Grid scales. Based on our experimental result, the SAS algorithm maximizes revenue while guarantees quality of service. More specifically, the quality of service is achieved through a high ratio of completed jobs and remarkable utilization of resources.     

Globus Toolkit Version 4: Software for Service-Oriented Systems

The Globus Toolkit （GT） has been developed since the late 1990s to support the development of serviceoriented distributed computing applications and infrastructures. Core GT components address, within a common framework, fundamental issues relating to security, resource access, resource management, data movement, resource discovery, and so forth. These components enable a broader ＂Globus ecosystem＂ of tools and components that build on, or interoperate with, GT functionality to provide a wide range of useful application-level functions. These tools have in turn been used to develop a wide range of both ＂Grid＂ infrastructures and distributed applications. I summarize here the principal characteristics of the recent Web Services-based GT4 release, which provides significant improvements over previous releases in terms of robustness, performance,, usability, documentation, standards compliance, and functionality. I also introduce the new ＂dev.globus＂ community development process, which allows a larger community to contribute to the development of Globus software.     

Double auction-inspired meta-scheduling of parallel applications on global grids

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users’ individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users’ service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.     

A computational economy for grid computing and its implementation in the Nimrod-G resource broker

Computational grids that couple geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service (QoS). Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user-defined QoS requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength grids. We present the results of experiments using the Nimrod-G resource broker for scheduling parametric computations on the World Wide Grid (WWG) resources that span five continents.     

Middleware infrastructure for parallel and distributed programming models in heterogeneous systems

We introduce a middleware infrastructure that provides software services for developing and deploying high-performance parallel programming models and distributed applications on clusters and networked heterogeneous systems. This middleware infrastructure utilizes distributed agents residing on the participating machines and communicating with one another to perform the required functions. An intensive study of the parallel programming models in Java has helped identify the common requirements for a runtime support environment, which we used to define the middleware functionality. A Java-based prototype, based on this architecture, has been developed along with a Java object-passing interface (JOPI) class library. Since this system is written completely in Java, it is portable and allows executing programs in parallel across multiple heterogeneous platforms. With the middleware infrastructure, users need not deal with the mechanisms of deploying and loading user classes on the heterogeneous system. Moreover, details of scheduling, controlling, monitoring, and executing user jobs are hidden, while the management of system resources is made transparent to the user. Such uniform services are essential for facilitating the development and deployment of scalable high-performance Java applications on clusters and heterogeneous systems. An initial deployment of a parallel Java programming model over a heterogeneous, distributed system shows good performance results. In addition, a framework for the agents' startup mechanism and organization is introduced to provide scalable deployment and communication among the agents.     

An ant algorithm for balanced job scheduling in grids

Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids.In the natural environment, the ants have a tremendous ability to team up to find an optimal path to food resources. An ant algorithm simulates the behavior of ants. In this paper, we propose a Balanced Ant Colony Optimization (BACO) algorithm for job scheduling in the Grid environment. The main contributions of our work are to balance the entire system load while trying to minimize the makespan of a given set of jobs. Compared with the other job scheduling algorithms, BACO can outperform them according to the experimental results.     

REMUS: A Rerouting and Multiplexing System for Grid Connectivity Across Firewalls

The Grid provides unique opportunities for high-performance computing through distributed applications that execute over multiple remote resources. Participating institutions can form a virtual organization to maximize the utilization of collective resources as well as to facilitate collaborative projects. However, there are two design aspects in distributed environments like the Grid that can easily clash: security and resource sharing. It may be that resources are secure but are not entirely conducive to resource sharing, or networks are wide open for resource sharing but sacrifice security as a result. We developed REMUS, a rerouting and multiplexing system that provides a compromise through connection rerouting and wrappers. REMUS reroutes connections using proxies, ports and protocols that are already authorized across firewalls, avoiding the need to make new openings through the firewalls. We also encapsulate applications within wrappers, transparently rerouting the connections among Grid applications without modifying their programs. In this paper, we describe REMUS and the tests we conducted across firewalls using two Grid middleware case studies: Globus Toolkit 2.4 and Nimrod/G 3.0.     

GMarte: Grid middleware to abstract remote task execution

Grid computing technologies are now being largely deployed with the widespread adoption of the Globus Toolkit as the industrial standard Grid middleware. However, its inherent steep learning curve discourages the use of these technologies for non‐experts. Therefore, to increase the use of Grid computing, it is important to have high‐level tools that simplify the process of remote task execution. In this paper we introduce a middleware, developed on top of the Java Commodity Grid, which offers an object‐oriented, user‐friendly application programming interface, from the Java language, which eases remote task execution for computationally intensive applications. Copyright © 2006 John Wiley & Sons, Ltd.     

Scalable Grid‐wide capacity allocation with the SweGrid Accounting System (SGAS)

The SweGrid Accounting System (SGAS) allocates capacity in collaborative Grid environments by coordinating enforcement of Grid‐wide usage limits as a means to offer usage guarantees and prevent overuse. SGAS employs a credit‐based allocation model where Grid capacity is granted to projects via Grid‐wide quota allowances that can be spent across the Grid resources. The resources collectively enforce these allowances in a soft, real‐time manner. SGAS is built on service‐oriented principles with a strong focus on interoperability and Web services standards. This article covers the SGAS design and implementation, which, besides addressing inherent Grid challenges (scale, security, heterogeneity, decentralization), emphasizes generality and flexibility to produce a customizable system with lightweight integration into different middleware and scheduling system combinations. We focus the discussion around the system design, a flexible allocation model, middleware integration experiences and scalability improvements via a distributed virtual banking system, and finally, an extensive set of testbed experiments. The experiments evaluate the performance of SGAS in terms of response times, request throughput, overall system scalability, and its performance impact on the Globus Toolkit 4 job submission software. We conclude that, for all practical purposes, the quota enforcement overhead incurred by SGAS on job submissions is not a limiting factor for the job‐handling capacity of the job submission software. Copyright © 2008 John Wiley & Sons, Ltd.     

Implementing and evaluating scheduling policies in gLite middleware

Grid scheduling algorithms are usually implemented in a simulation environment using tools that hide the complexity of the Grid and assumptions that are not always realistic. In our work, we describe the steps followed, the difficulties encountered and the solutions provided to develop and evaluate a scheduling policy, initially implemented in a simulation environment, in the gLite Grid middleware. Our focus is on a scheduling algorithm that allocates in a fair way the available resources among the requested users or jobs. During the actual implementation of this algorithm in gLite, we observed that the validity of the information used by the scheduler for its decisions affects greatly its performance. To improve the accuracy of this information, we developed an internal feedback mechanism that operates along with the scheduling algorithm. Also, a Grid computation resource cannot be shared concurrently between different users or jobs, making it difficult to provide actual fairness. For this reason we investigated the use of virtualization technology in the gLite middleware. We did a proof‐of‐concept implementation and performed an experimental evaluation of our scheduling algorithm in a small gLite testbed that proves the validity and applicability of our solutions. Copyright © 2012 John Wiley & Sons, Ltd.     

Using a Simple Prioritisation Mechanism to Effectively Interoperate Service and Opportunistic Grids in the EELA-2 e-Infrastructure

Grids currently in production can be broadly classified as either service Grids, composed of dedicated resources, or opportunistic Grids that harvest the computing power of non-dedicated resources when they are idle. While a service Grid provides high and well defined levels of quality of service, an opportunistic Grid provides only a best-effort service. Nevertheless, since opportunistic Grids do not require resources to be fully dedicated to the Grid, they have the potential to assemble a much larger number of resources. Moreover, these Grids cater very well to the execution of the so-called embarrassingly parallel applications, a type of application that is frequently found in practice, and that comprises the largest portion of the typical workload processed in production Grid systems. The EELA-2 e-infrastructure is comprised of a service Grid and an opportunistic Grid that federates computing resources from scientific institutions in both Europe and Latin America. Due to the complementary characteristics of these two types of Grids, a lot of attention has recently been placed in how to interoperate them. In this paper we focus on the less studied problem of assessing the feasibility of such interoperation. We analyse different prioritisation policies that define when the resources of one Grid can be used to run jobs originating from the other. Our results show that in the absence of a suitable prioritisation policy, the benefits that the users of one Grid may have, frequently come with an important negative impact on the users of the other Grid. We also show that a simple reciprocation mechanism is capable of arbitrating the interoperation in such a way that, whenever possible, users profit from the interoperation and, in no case, this benefit leads to a noticeable reduction on the quality of service that the users would experience were the Grids not to interoperate. We conclude discussing how we have implemented, in the context of the EELA-2 project, this prioritisation mechanism, allowing the effective interoperation of a service Grid based on the gLite middleware with an opportunistic Grid that uses the OurGrid middleware.     

G‐BLAST: a Grid‐based solution for mpiBLAST on computational Grids

Over the past few years, research and development in bioinformatics (e.g. genomic sequence alignment) has grown with each passing day fueling continuing demands for vast computing power to support better performance. This trend usually requires solutions involving parallel computing techniques because cluster computing technology reduces execution times and increases genomic sequence alignment efficiency. One example, mpiBLAST is a parallel version of NCBI BLAST that combines NCBI BLAST with message passing interface (MPI) standards. However, as most laboratories cannot build up powerful cluster computing environments, Grid computing framework concepts have been designed to meet the need. Grid computing environments coordinate the resources of distributed virtual organizations and satisfy the various computational demands of bioinformatics applications. In this paper, we report on designing and implementing a BioGrid framework, called G‐BLAST, that performs genomic sequence alignments using Grid computing environments and accessible mpiBLAST applications. G‐BLAST is also suitable for cluster computing environments with a server node and several client nodes. G‐BLAST is able to select the most appropriate work nodes, dynamically fragment genomic databases, and self‐adjust according to performance data. To enhance G‐BLAST capability and usability, we also employ a WSRF Grid Service Portal and a Grid Service GUI desk application for general users to submit jobs and host administrators to maintain work nodes. Copyright © 2008 John Wiley & Sons, Ltd.     

Network Bandwidth-aware job scheduling with dynamic information model for Grid resource brokers

A resource broker with a user-friendly interface for job submission developed on a platform constructed using the Globus toolkit is proposed. The broker employs a domain-based network information model and dynamic version to measure network statuses, and also monitors and collects resource statuses and network-related information as the basis of its brokerage. A network bandwidth-aware job scheduling algorithm for brokering suitable Grid resources to communication-intensive jobs based on improving and preserving the advantages of our previously developed network information model is also proposed. Using timely information, the resource broker effectively matches Grid resources and user requests, thus improving job execution efficiency.     

Scalable dimensioning of resilient Lambda Grids

Grids consist of the aggregation of numerous dispersed computational, storage and network resources, able to satisfy even the most demanding computing jobs. Due to the data-intensive nature of Grid jobs, there is an increasing interest in Grids using optical transport networks as this technology allows for the timely delivery of large amounts of data. Such Grids are commonly referred to as Lambda Grids.

An important aspect of Grid deployment is the allocation and activation of installed network capacity, needed to transfer data and jobs to and from remote resources. However, the exact nature of a Grid’s network traffic depends on the way arriving workload is scheduled over the various Grid sites. As Grids possibly feature high numbers of resources, jobs and users, solving the combined Grid network dimensioning and workload scheduling problem requires the use of scalable mathematical methods such as Divisible Load Theory (DLT). Lambda Grids feature additional complexity such as wavelength granularity and continuity or conversion constraints must be enforced. Additionally, Grid resources cannot be expected to be available at all times. Therefore, the extra complexity of resilience against possible resource failures must be taken into account when modelling the combined Grid network dimensioning and workload scheduling problem, enforcing the need for scalable solution methods. In this work, we tackle the Lambda Grid combined dimensioning and workload scheduling problem and incorporate single-resource failure or unavailability scenarios. We use Divisible Load Theory to tackle the scalability problem and compare non-resilient lambda Grid dimensioning to the dimensions needed to survive single-resource failures. We distinguish three failure scenarios relevant to lambda Grid deployment: computational element, network link and optical cross-connect failure. Using regular network topologies, we derive analytical bounds on the dimensioning cost. To validate these bounds, we present comparisons for the resulting Grid dimensions assuming a 2-tier Grid operation as a function of varying wavelength granularity, fiber/wavelength cost models, traffic demand asymmetry and Grid scheduling strategy for a specific set of optical transport networks.     

Data Intensive and Network Aware (DIANA) Grid Scheduling

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size. The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost. The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computations and data. The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles.