Junwei Cao

GridFlow: workflow management for grid computing

Grid computing is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Workflow management is emerging as one of the most important grid services. In this work, a workflow management system for grid computing, called GridFlow, is presented, including a user portal and services of both global grid workflow management and local grid sub-workflow scheduling. Simulation, execution and monitoring functionalities are provided at the global grid level, which work on top of an existing agent-based grid resource management system. At each local grid, sub-workflow scheduling and conflict management are processed on top of an existing performance prediction based task scheduling system. A fuzzy timing technique is applied to address new challenges of workflow management in a cross-domain and highly dynamic grid environment. A case study is given and corresponding results indicate that local and global grid workflow management can coordinate with each other to optimise workflow execution time and solve conflicts of interest.

Grid load balancing using intelligent agents

Scalable management and scheduling of dynamic grid resources requires new technologies to build the next generation intelligent grid environments. This work demonstrates that AI techniques can be utilised to achieve effective workload and resource management. A combination of intelligent agents and multi-agent approaches is applied to both local grid resource scheduling and global grid load balancing. Each agent is a representative of a local grid resource and utilises predictive application performance data with iterative heuristic algorithms to engineer local load balancing across multiple hosts. At a higher level, agents cooperate with each other to balance workload using a peer-to-peer service advertisement and discovery mechanism.

ARMS: An agent-based resource management system for grid computing

Resource management is an important component of a grid computing infrastructure. The scalability and adaptability of such systems are two key challenges that must be addressed. In this work an agent-based resource management system, ARMS, is implemented for grid computing. ARMS utilises the performance prediction techniques of the PACE toolkit to provide quantitative data regarding the performance of complex applications running on a local grid resource. At the meta-level, a hierarchy of homogeneous agents are used to provide a scalable and adaptable abstraction of the system architecture. Each agent is able to cooperate with other agents and thereby provide service advertisement and discovery for the scheduling of applications that need to utilise grid resources. A case study with corresponding experimental results is included to demonstrate the efficiency of the resource management and scheduling system.

Agent-based grid load balancing using performance-driven task scheduling

Load balancing is a key concern when developing parallel and distributed computing applications. The emergence of computational grids extends this problem, where issues of cross-domain and large-scale scheduling must also be considered. In this paper an agent-based grid management infrastructure is coupled with a performance-driven task scheduler that has been developed for local grid load balancing. Each grid scheduler utilises predictive application performance data and an iterative heuristic algorithm to engineer local load balancing across multiple processing nodes. At a higher level, a hierarchy of homogeneous agents are used to represent multiple grid resources. Agents cooperate with each other to balance workload in the global grid environment using service advertisement and discovery mechanisms. A case study is included with corresponding experimental results to demonstrate that both local schedulers and agents contribute to overall grid load balancing, which significantly improves grid application execution performance and resource utilisation.

Performance evaluation of an agent-based resource management infrastructure for grid computing

Resource management is an important infrastructure in the grid computing environment. Scalability and adaptability are two key challenges in the implementation of such complex software systems. We introduce a new model for resource management in a metacomputing environment using a hierarchy of homogeneous agents that has the capability of service discovery. The performance of the agent system can be improved using different combinations of optimisation strategies. A modelling and simulation environment has been developed in this work that enables the performance of the system to be investigated. A simplified model of the resource management infrastructure is given as a case study and simulation results are included that show the impact of the choice of performance optimisation strategies on the overall system performance.

Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

Along with the development of cloud computing, an increasing number of enterprises start to adopt cloud service, which promotes the emergence of many cloud service providers. For cloud service providers, how to configure their cloud service platforms to obtain the maximum profit becomes increasingly the focus that they pay attention to. In this paper, we take customer satisfaction into consideration to address this problem. Customer satisfaction affects the profit of cloud service providers in two ways. On one hand, the cloud configuration affects the quality of service which is an important factor affecting customer satisfaction. On the other hand, the customer satisfaction affects the request arrival rate of a cloud service provider. However, few existing works take customer satisfaction into consideration in solving profit maximization problem, or the existing works considering customer satisfaction do not give a proper formalized definition for it. Hence, we first refer to the definition of customer satisfaction in economics and develop a formula for measuring customer satisfaction in cloud computing. And then, an analysis is given in detail on how the customer satisfaction affects the profit. Lastly, taking into consideration customer satisfaction, service-level agreement, renting price, energy consumption, and so forth, a profit maximization problem is formulated and solved to get the optimal configuration such that the profit is maximized.

A Case Study on the Use of Workflow Technologies for Scientific Analysis: Gravitational Wave Data Analysis

Modern scientific experiments acquire large amounts of data that must be analyzed in subtle and complicated ways to extract the best results. The Laser Interferometer Gravitational Wave Observatory (LIGO) is an ambitious effort to detect gravitational waves produced by violent events in the universe, such as the collision of two black holes or the explosion of supernovae [37,258]. The experiment records approximately 1 TB of data per day, which is analyzed by scientists in a collaboration that spans four continents. LIGO and distributed computing have grown up side by side over the past decade, and the analysis strategies adopted by LIGO scientists have been strongly influenced by the increasing power of tools to manage distributed computing resources and the workflows to run on them. In this chapter, we use LIGO as an application case study in workflow design and implementation. The software architecture outlined here has been used with great efficacy to analyze LIGO data [2–5] using dedicated computing facilities operated by the LIGO Scientific Collaboration, the LIGO Data Grid. It is just the first step, however. Workflow design and implementation lies at the interface between computing and traditional scientific activities. In the conclusion, we outline a few directions for future development and provide some long-term vision for applications related to gravitational wave data analysis.

Multi-objective scheduling of many tasks in cloud platforms

a b s t r a c t The scheduling of a many-task workflow in a distributed computing platform is a well known NP-hard problem. The problem is even more complex and challenging when the virtualized clusters are used to execute a large number of tasks in a cloud computing platform. The difficulty lies in satisfying multiple objectives that may be of conflicting nature. For instance, it is difficult to minimize the makespan of many tasks, while reducing the resource cost and preserving the fault tolerance and/or the quality of service (QoS) at the same time. These conflicting requirements and goals are difficult to optimize due to the unknown runtime conditions, such as the availability of the resources and random workload distributions. Instead of taking a very long time to generate an optimal schedule, we propose a new method to generate suboptimal or sufficiently good schedules for smooth multitask workflows on cloud platforms. Our new multi-objective scheduling (MOS) scheme is specially tailored for clouds and based on the ordinal optimization (OO) method that was originally developed by the automation community for the design optimization of very complex dynamic systems. We extend the OO scheme to meet the special demands from cloud platforms that apply to virtual clusters of servers from multiple data centers. We prove the suboptimality through mathematical analysis. The major advantage of our MOS method lies in the significantly reduced scheduling overhead time and yet a close to optimal performance. Extensive experiments were carried out on virtual clusters with 16 to 128 virtual machines. The multitasking workflow is obtained from a real scientific LIGO workload for earth gravitational wave analysis. The experimental results show that our proposed algorithm rapidly and effectively generates a small set of semi-optimal scheduling solutions. On a 128-node virtual cluster, the method results in a thousand times of reduction in the search time for semi-optimal workflow schedules compared with the use of the Monte Carlo and the Blind Pick methods for the same purpose.

Self-organizing agents for grid load balancing

A computational grid is a wide-area computing environment for cross-domain resource sharing and service integration. Resource management and load balancing are key concerns when implementing grid middleware and improving resource utilization. Grid resource management can be implemented as a multiagent system with resource advertisement and discovery capabilities if job requests from users are associated with explicit QoS requirements. In this work agent-based self-organization is proposed to perform complementary load balancing for batch jobs with no explicit execution deadlines. In particular, an ant-like self-organizing mechanism is introduced and proved to be powerful to achieve overall grid load balancing through a collection of very simple local interactions. A modeling and simulation environment is developed to enable performance of the ant algorithm to be investigated quantitatively. Simulation results included in this work illustrate the impact of different performance optimization strategies on the overall system load balancing level, speed and efficiency.

Optimal Power Allocation and Load Distribution for Multiple Heterogeneous Multicore Server Processors across Clouds and Data Centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.