The grid, the load and the gradient

An important concern for an efficient use of distributed computing is dealing with load balancing to ensure all available nodes and their shared resources are equally exploited. In large scale systems such as volunteer computing platforms and desktop grids, centralized solutions may introduce performance bottlenecks and single points of failure. Accordingly fully distributed alternatives have been considered, due to their inherent robustness and reliability. In extremely dynamic contexts, scheduling middlewares should adapt their job scheduling policies to the actual availability and overcome the volatility and heterogeneity typical of the underlying nodes. To deal with the dynamicity of a large pool of resources, self-organizing and adaptive solutions represent a promising research direction. Solutions based on bio-inspired methodologies are particularly suitable, as they inherently provide the desired features. In this paper we present a fully distributed load balancing mechanism, called ozmos, which aims at increasing the efficiency of distributed computing systems through peer-to-peer interaction between nodes. The proposed algorithm is based on a Chord overlay, and employs ant-like agents to spread information about the current load on each node, to reschedule tasks from overloaded systems to underloaded ones, and to relocate incompatible tasks on suitable resources in heterogeneous grids. By means of several evaluation scenarios we demonstrate the effectiveness of the proposed solution in achieving system-wide load balancing, both with homogeneous and heterogeneous resources. In particular we consider the load balancing performance of our approach, its scalability, as well as its communication efficiency.     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台,是当前并行和分布处理技术的一个发展方向,而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础,而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题,但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”,依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格,改进和优化计算网格资源的分配。     

基于计算经济的网格资源管理研究

网格是为解决大规模资源密集型问题而提出的新一代计算平台,资源管理是网格的关键技术之一。但是,资源的分布性、异构性、自治性、动态性等使得网格资源的管理变得异常复杂。目前,基于市场的经济资源管理和调度算法非常适合解决网格中的资源管理问题。本文提出了网格环境下基于经济模型的各种代理,给出了一种新的资源管理模型
型,并定义了效用函数,给出了基于效用最优的资源调度算法。为解决网格资源管理的问题提供了一个有效的途径。     

Scheduling in distributed systems: A cloud computing perspective

Scheduling is essentially a decision-making process that enables resource sharing among a number of activities by determining their execution order on the set of available resources. The emergence of distributed systems brought new challenges on scheduling in computer systems, including clusters, grids, and more recently clouds. On the other hand, the plethora of research makes it hard for both newcomers researchers to understand the relationship among different scheduling problems and strategies proposed in the literature, which hampers the identification of new and relevant research avenues. In this paper we introduce a classification of the scheduling problem in distributed systems by presenting a taxonomy that incorporates recent developments, especially those in cloud computing. We review the scheduling literature to corroborate the taxonomy and analyze the interest in different branches of the proposed taxonomy. Finally, we identify relevant future directions in scheduling for distributed systems.     

分布式系统下的DAG任务调度研究综述

近年来随着网格、云计算工作流等分布式计算技术的发展,关于DAG(有向无环图)模型任务在分布式系统环境下的调度问题逐渐成为备受关注的研究热点。根据最新研究进展,对分布式系统下的DAG任务调度问题和有关技术进行了研究与讨论,主要包括四个方面:系统地描述了分布式系统和异构分布式系统的有关概念,异构分布式系统下的DAG任务调度问题、调度模型及其典型应用;对现有分布式系统下DAG任务调度的研究按照不同的方式进行了分类;探讨了多DAG共享异构分布式资源调度的研究现状;讨论了目前多DAG共享异构分布式资源调度研究存在的问题和未来可能的研究方向。     

Communication cost effective scheduling policies of nonclairvoyant jobs with load balancing in a grid

Effective load distribution is of great importance at grids, which are complex heterogeneous distributed systems. In this paper we study site allocation scheduling of nonclairvoyant jobs in a 2-level heterogeneous grid architecture. Three scheduling policies at grid level which utilize site load information are examined. The aim is the reduction of site load information traffic, while at the same time mean response time of jobs and fairness in utilization between the heterogeneous sites are of great interest. A simulation model is used to evaluate performance under various conditions. Simulation results show that considerable decrement in site load information traffic and utilization fairness can be achieved at the expense of a slight increase in response time.     

An improved approach for load balancing among heterogeneous resources in computational grids

In recent days, due to the rapid technological advancements, the grid computing has become an important area of research in distributed systems. The load balancing is a very important and complex problem in grid computing. In this paper, we propose a dynamic-distributed load-balancing technique called the improved load balancing on enhanced GridSim with deadline control (IEGDC) for computational grids. Here, we provide a new mechanism of scheduling to enhance the utilization of the resources and to prevent the resource overloading. A selection method for scheduling by considering the state of resource bandwidth and capacity of various resources is presented. We simulate the proposed load-balancing strategy on the GridSim platform. The proposed mechanism on comparison is found to outperform the existing schemes in terms of response time, resubmitted time, finished and unfinished Gridlets. The simulation results are presented.     

An agent-based approach for dynamic adjustment of scheduled jobs in computational grids

Grid computing is a newly developed technology for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Grid scheduling is an important infrastructure in the grid computing environment. Most of the existing grids scheduling methods focus on maximizing processor utilization without taking grid load into consideration. This may lead to significant inefficiencies in performance such as large job queues and processing delays. In this paper, we propose a multiagent-based scheduling system for computational grids with a new approach. Agent technology is suitable for a computational grid because of the dynamic, heterogeneous, and autonomous nature of the grid. The main idea of the proposed system is a combination of a static scheduling using a fixed scheduling algorithm and a dynamic adjustment through the autonomous behavior of agents. The superiority of the proposed system, in reducing the load of the grid and minimizing the response time for executing user applications, is demonstrated by simulation experiments.     

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.     

Agent scheduling model for adaptive dynamic load balancing in agent-based distributed simulations

Agent-based distributed simulations are confronted with load imbalance problem, which significantly affects simulation performance. Dynamic load balancing can be effective in decreasing simulation execution time and improving simulation performance. The characteristics of multi-agent systems and time synchronization mechanisms make the traditional dynamic load balancing approaches not suitable for dynamic load balancing in agent-based distributed simulations. In this paper, an adaptive dynamic load balancing model in agent-based distributed simulations is proposed. Due to the complexity and huge time consuming for solving the model, a distributed approximate optimized scheduling algorithm with partial information (DAOSAPI) is proposed. It integrates the distributed mode, approximate optimization and agent set scheduling approach. Finally, experiments are conducted to verify the efficiency of the proposed algorithm and the simulation performance under dynamic agent scheduling. The experiments indicate that DAOSPI has the advantage of short execution time in large-scale agent scheduling, and the distributed simulation performance under this dynamic agent scheduling outperforms that under static random agent distribution.     

Dynamic grid load sharing with adaptive dissemination protocols

Scheduling in large scale dynamic grids comprising eclectic collections of resources is increasingly difficult. Autonomous resource neighborhoods may wish to determine the level of grid offered load that they can or will accept; different sites may wish to attract different amounts of load, to satisfy some desired property within a grid economy. This changes the traditional notion of load sharing, which generally assumes that the desired equilibrium should be an equal distribution of load across all participating machines, because they are under the jurisdiction of a single site, and therefore more likely to implement one common policy. In large-scale grids, nodes and neighborhoods should instead get a portion of the load that best matches their local policies for supporting and admitting grid jobs. This article describes information dissemination protocols that can distribute load in this way, without using load rebalancing through job migration, which is more difficult and costly in large-scale heterogeneous grids. Essentially, nodes adjust their advertising rates and aggressiveness to influence where jobs get scheduled. We report experimental results with example resource configurations in which each resource neighborhood determines its ideal grid load and disseminates accordingly. In turn, each neighborhood attracts the requisite amount of resource requests from the grid. Moreover, performance does not degrade: overall query satisfaction rates are within 9% of both adaptive dissemination protocols that use static adaptation policies, and static dissemination protocols that may be custom-tailored to specific resource and load distributions.     

Cooperative scheduling mechanism for large-scale peer-to-peer computing systems

Over recent years, peer-to-peer (P2P) systems have become an important part of Internet. Millions of users have been attracted to their structures and services. P2P computing is a distributed computing paradigm that uses Internet to connect thousands, or even millions, of users into a single large virtual computer based on the sharing of computational resources. One of the most critical aspects to the design of P2P computing systems is the development of scheduling techniques to manage the computational resources efficiently and in a scalable way. This paper proposes a cooperative scheduling mechanism with a two-level topology designed to work on large-scale distributed computing P2P systems. Our main contribution is proposing three criteria that only use local information to schedule tasks thus providing scalability to the overall scheduling system. By setting up these three criteria, the system can be easily adapted to work efficiently with very different kinds of distributed applications. The extensive experimentation carried out justifies the importance of good scheduling in such heterogeneous systems, but also emphasizes the importance of having a scheduling algorithm capable of being adapted to the requirements of different kinds of application.     

Algorithmic mechanism design for load balancing in distributed systems

Computational grids are promising next-generation computing platforms for large-scale problems in science and engineering. Grids are large-scale computing systems composed of geographically distributed resources (computers, storage etc.) owned by self interested agents or organizations. These agents may manipulate the resource allocation algorithm in their own benefit, and their selfish behavior may lead to severe performance degradation and poor efficiency. In this paper, we investigate the problem of designing protocols for resource allocation involving selfish agents. Solving this kind of problems is the object of mechanism design theory. Using this theory, we design a truthful mechanism for solving the static load balancing problem in heterogeneous distributed systems. We prove that using the optimal allocation algorithm the output function admits a truthful payment scheme satisfying voluntary participation. We derive a protocol that implements our mechanism and present experiments to show its effectiveness.     

Hybrid Architecture for Autonomous Load Balancing in Distributed Systems Based on Smooth Fuzzy Function

Due to the rapid advancements and developments in wide area networks and powerful computational resources, the load balancing mechanisms in distributed systems have gained pervasive applications covering wired as well as mobile distributed systems. In large-scale distributed systems, sharing of distributed resources is required for enhancing overall resource utilization. This paper presents a comprehensive study and detailed comparative analysis of different load balancing algorithms employing fuzzy logic and mobile agents. We have proposed a hybrid architecture for integrated load balancing and monitoring in distributed computing systems employing fuzzy logic and autonomous mobile agents. Furthermore, we have proposed a smooth and composite fuzzy membership function in order to model fine grained load information in a system. The simulation study and a detailed qualitative as well as quantitative analysis of algorithmic performances are presented. Lastly, a deployment environment is described.     

Reputation-based dependable scheduling of workflow applications in Peer-to-Peer Grids

Grids facilitate creation of wide-area collaborative environment for sharing computing or storage resources and various applications. Inter-connecting distributed Grid sites through peer-to-peer routing and information dissemination structure (also known as Peer-to-Peer Grids) is essential to avoid the problems of scheduling efficiency bottleneck and single point of failure in the centralized or hierarchical scheduling approaches. On the other hand, uncertainty and unreliability are facts in distributed infrastructures such as Peer-to-Peer Grids, which are triggered by multiple factors including scale, dynamism, failures, and incomplete global knowledge.In this paper, a reputation-based Grid workflow scheduling technique is proposed to counter the effect of inherent unreliability and temporal characteristics of computing resources in large scale, decentralized Peer-to-Peer Grid environments. The proposed approach builds upon structured peer-to-peer indexing and networking techniques to create a scalable wide-area overlay of Grid sites for supporting dependable scheduling of applications. The scheduling algorithm considers reliability of a Grid resource as a statistical property, which is globally computed in the decentralized Grid overlay based on dynamic feedbacks or reputation scores assigned by individual service consumers mediated via Grid resource brokers. The proposed algorithm dynamically adapts to changing resource conditions and offers significant performance gains as compared to traditional approaches in the event of unsuccessful job execution or resource failure. The results evaluated through an extensive trace driven simulation show that our scheduling technique can reduce the makespan up to 50% and successfully isolate the failure-prone resources from the system.     

面向分布式环境的仿真部署工具设计

仿真环境的构建是大规模分布式仿真系统中最为繁琐的问题,易受到人的主观因素影响，存在着环境重构难、自动化程度低和数据资源难以管理等多个方面的问题。本文设计一种基于客户端/服务器架构的面向分布式环境的仿真部署工具，通过定义规范化的对象系统、标准化的交互接口和统一化的数据资源调度方式，实现分布式环境下仿真资源的集中管理，优化了部署环境，简化了部署流程。仿真实验表明使用本文提出的方法可快速实现部署环境的重构。     

Task Scheduling on the PASM Parallel Processing System

PASM is a proposed large-scale distributed/parallel processing system which can be partitioned into independent SIMD/MIMD machines of various sizes. One design problem for systems such as PASM is task scheduling. The use of multiple FIFO queues for nonpreemptive task scheduling is described. Four multiple-queue scheduling algorithms with different placement policies are presented and applied to the PASM parallel processing system. Simulation of a queueing network model is used to compare the performance of the algorithms. Their performance is also considered in the case where there are faulty control units and processors. The multiple-queue scheduling algorithms can be adapted for inclusion in other multiple-SIMD and partitionable SIMD/MIMD systems that use similar types of interconnection networks to those being considered for PASM.     

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.     

基于PI3000平台的分布式任务调度研究与实现

在PI3000平台中任务调度是应用系统中很重要的部分,应用环境的复杂程度导致各式各样调度的框架和算法.文中提出一个新的分布式负载均衡的任务调度框架,以解决在并行任务中特定的应用环境下的任务调度问题.这个框架是动态的、可重用的,通过提供给各种不同的资源环境对应的接口,来进行跨服务的调度.整个框架关注于实际应用环境下任务、资源的动态不稳定性和任务计算的快速响应.     

Adaptive Reputation-Based Scheduling on Unreliable Distributed Infrastructures

This paper addresses the inherent unreliability and instability of worker nodes in large-scale donation-based distributed infrastructures such as peer-to-peer and grid systems. We present adaptive scheduling techniques that can mitigate this uncertainty and significantly outperform current approaches. In this work, we consider nodes that execute tasks via donated computational resources and may behave erratically or maliciously. We present a model in which reliability is not a binary property, but a statistical one based on a node's prior performance and behavior. We use this model to construct several reputation-based scheduling algorithms that employ estimated reliability ratings of worker nodes for efficient task allocation. Our scheduling algorithms are designed to adapt to changing system conditions, as well as nonstationary node reliability. Through simulation, we demonstrate that our algorithms can significantly improve throughput while maintaining a very high success rate of task completion. Our results suggest that reputation-based scheduling can handle a wide variety of worker populations, including nonstationary behavior, with overhead that scales well with system size. We also show that our adaptation mechanism allows the application designer fine-grain control over the desired performance metrics.