Ligang He

A Hybrid Chemical Reaction Optimization Scheme for Task Scheduling on Heterogeneous Computing Systems

Scheduling for directed acyclic graph (DAG) tasks with the objective of minimizing makespan has become an important problem in a variety of applications on heterogeneous computing platforms, which involves making decisions about the execution order of tasks and task-to-processor mapping. Recently, the chemical reaction optimization (CRO) method has proved to be very effective in many fields. In this paper, an improved hybrid version of the CRO method called HCRO (hybrid CRO) is developed for solving the DAG-based task scheduling problem. In HCRO, the CRO method is integrated with the novel heuristic approaches, and a new selection strategy is proposed. More specifically, the following contributions are made in this paper. (1) A Gaussian random walk approach is proposed to search for optimal local candidate solutions. (2) A left or right rotating shift method based on the theory of maximum Hamming distance is used to guarantee that our HCRO algorithm can escape from local optima. (3) A novel selection strategy based on the normal distribution and a pseudo-random shuffle approach are developed to keep the molecular diversity. Moreover, an exclusive-OR (XOR) operator between two strings is introduced to reduce the chance of cloning before new molecules are generated. Both simulation and real-life experiments have been conducted in this paper to verify the effectiveness of HCRO. The results show that the HCRO algorithm schedules the DAG tasks much better than the existing algorithms in terms of makespan and speed of convergence.

A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization

A new meta-heuristic method, called Chemical Reaction Optimization (CRO), has been proposed very recently. The method encodes solutions as molecules and mimics the interactions of molecules in chemical reactions to search the optimal solutions. The CRO method has demonstrated its capability in solving NP-hard optimization problems. In this paper, the CRO scheme is used to formulate the scheduling of Directed Acyclic Graph (DAG) jobs in heterogeneous computing systems, and a Double Molecular Structure-based Chemical Reaction Optimization (DMSCRO) method is developed. There are two molecular structures in DMSCRO: one is used to encode the execution order of the tasks in a DAG job, and the other to encode the task-to-computing-node mapping. The DMSCRO method also designs four elementary chemical reaction operations and the fitness function suitable for the scenario of DAG scheduling. In this paper, we have also conducted the simulation experiments to verify the effectiveness and efficiency of DMSCRO over a large set of randomly generated graphs and the graphs for real-world problems.

Performance-Aware Workflow Management for Grid Computing

Grid middleware development has advanced rapidly over the past few years to support component-based programming models and service-oriented architectures. This is most evident with the forthcoming release of the Globus toolkit (GT4), which represents a convergence of concepts (and standards) from both the grid and web-services communities. Grid applications are increasingly modular, composed of workflow descriptions that feature both resource and application dynamism. Understanding the performance implications of scheduling grid workflows is critical in providing effective resource management and reliable service quality to users. This paper describes a series of extensions to an existing performance-aware grid management system (TITAN). These extensions provide additional support for workflow prediction and scheduling using a multi-domain performance management infrastructure.

Allocating non-real-time and soft real-time jobs in multiclusters

This paper addresses workload allocation techniques for two types of sequential jobs that might be found in multicluster systems, namely, non-real-time jobs and soft real-time jobs. Two workload allocation strategies, the optimized mean response time (ORT) and the optimized mean miss rate (OMR), are developed by establishing and numerically solving two optimization equation sets. The ORT strategy achieves an optimized mean response time for non-real-time jobs, while the OMR strategy obtains an optimized mean miss rate for soft real-time jobs over multiple clusters. Both strategies take into account average system behaviors (such as the mean arrival rate of jobs) in calculating the workload proportions for individual clusters and the workload allocation is updated dynamically when the change in the mean arrival rate reaches a certain threshold. The effectiveness of both strategies is demonstrated through theoretical analysis. These strategies are also evaluated through extensive experimental studies and the results show that when compared with traditional strategies, the proposed workload allocation schemes significantly improve the performance of job scheduling in multiclusters, both in terms of the mean response time (for non-real-time jobs) and the mean miss rate (for soft real-time jobs).

From Mobiles to Clouds: Developing Energy-Aware Offloading Strategies for Workflows

Cloud computing and mobile computing are two of the most influential technologies that look set to change the face of computing in the coming years. Combination of the two provides us with an unprecedented opportunity to provide highly portable and yet content-rich and computation-intensive services to the end user. In this paper we investigate the possibility of using code/task offload techniques between mobile and cloud in order to reduce the energy cost of workflows deployed on mobile devices. We first present a vision in which mobile devices are coordinated over a network, which is equipped with a layer of cloud-like infrastructures which we term cloudlets, whose computational resources can be leveraged by the mobile devices to host the execution of mission-critical mobile workflows in an energy-aware manner. We then build a model that encompasses various characteristics of the workflows software and the networks hardware devices. With this model, we construct the objective functions that guide the offload decisions. We then present a heuristic algorithm that produces statistical and dynamic offload plans according to these objective functions and their variations both statically and dynamically. We conclude the paper with a series of simulation studies, the results of which give insight into the offload-ability of workflows of different characteristics. The results also illustrate how different hardware specifications can affect offload efficiency. These studies indicate that our offload algorithm can significantly improve the energy efficiency and execution speed of mobile workflows.

Optimising static workload allocation in multiclusters

Summary form only given. Workload allocation and job dispatching are two fundamental components in static job scheduling for distributed systems. We address the static workload allocation techniques for two types of job stream in multicluster systems, namely, nonreal-time job streams and soft-real-time job streams, which request different qualities of service. Two workload allocation strategies (called ORT and OMR) are developed by establishing and numerically solving two optimisation equation sets. The ORT strategy achieves the optimised mean response time for the nonreal-time job stream; while the OMR strategy can gain the optimised mean miss rate for the soft-real-time job stream over multiple clusters (these strategies can also be applied in a single cluster system). The effectiveness of both strategies is demonstrated through theoretical analysis. The proposed workload allocation schemes are combined with two job dispatching strategies (weighted random and weighted round-robin) to generate new static job scheduling algorithms for multicluster environments. These algorithms are evaluated through extensive experimental studies and the results show that compared with static approaches without the optimisation techniques, the proposed workload allocation schemes can significantly improve the performance of static job scheduling in multiclusters, in terms of both the mean response time (for the nonreal-time jobs) and the mean miss rate (for soft-real-time jobs).

Dynamic scheduling of parallel real-time jobs by modelling spare capabilities in heterogeneous clusters

In this research, a scenario is assumed where periodic real-time jobs are being run on a heterogeneous cluster of computers, and new aperiodic parallel real-time jobs, modelled by directed acyclic graphs (DAG), arrive at the system dynamically. In the scheduling scheme presented in this paper, a global scheduler situated within the cluster schedules new jobs onto the computers by modelling their spare capabilities left by existing periodic jobs. Admission control is introduced so that new jobs are rejected if their deadlines cannot be met under the precondition of still guaranteeing the real-time requirements of existing jobs. Each computer within the cluster houses a local scheduler, which uniformly schedules both periodic job instances and the subtasks in the parallel realtime jobs using an early deadline first policy. The modelling of the spare capabilities is optimal in the sense that once a new task starts running on a computer, it will utilize all the spare capability left by the periodic real-time jobs and its finish time is the earliest possible. The performance of the proposed modelling approach and scheduling scheme is evaluated by extensive simulation; results show that the system utilization is significantly enhanced, while the real-time requirements of the existing jobs remain guaranteed.

Energy-Aware Data Allocation and Task Scheduling on Heterogeneous Multiprocessor Systems With Time Constraints

In this paper, we address the problem of energy-aware heterogeneous data allocation and task scheduling on heterogeneous multiprocessor systems for real-time applications. In a heterogeneous distributed shared-memory multiprocessor system, an important problem is how to assign processors to real-time application tasks, allocate data to local memories, and generate an efficient schedule in such a way that a time constraint can be met and the total system energy consumption can be minimized. We propose an optimal approach, i.e., an integer linear programming method, to solve this problem. As the problem has been conclusively shown to be computationally very complicated, we also present two heuristic algorithms, i.e., task assignment considering data allocation (TAC-DA) and task ratio greedy scheduling (TRGS), to generate near-optimal solutions for real-time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm that is commonly used to solve heterogeneous task scheduling problems. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance. We conducted experimental performance evaluation on two heterogeneous multiprocessor systems. The average reduction rates of the total energy consumption of the TAC-DA and TRGS algorithms to that of the greedy algorithm are 13.72% and 15.76%, respectively, on the first system, and 19.76% and 24.67%, respectively, on the second system. To the best of our knowledge, this is the first study to solve the problem of task scheduling incorporated with data allocation and energy consumption on heterogeneous distributed shared-memory multiprocessor systems.

An Investigation into the Application of Different Performance Prediction Methods to Distributed Enterprise Applications

Response time predictions for workload on new server architectures can enhance Service Level Agreement–based resource management. This paper evaluates two performance prediction methods using a distributed enterprise application benchmark. The historical method makes predictions by extrapolating from previously gathered performance data, while the layered queuing method makes predictions by solving layered queuing networks. The methods are evaluated in terms of: the systems that can be modelled; the metrics that can be predicted; the ease with which the models can be created and the level of expertise required; the overheads of recalibrating a model; and the delay when evaluating a prediction. The paper also investigates how a prediction-enhanced resource management algorithm can be tuned so as to compensate for predictive inaccuracy and balance the costs of SLA violations and server usage.

Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models

The automatic allocation of enterprise workload to resources can be enhanced by being able to make what–if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: (i) comparatively evaluate the layered queuing and historical techniques; (ii) evaluate the effectiveness of the management algorithm in different operating scenarios; and (iii) provide guidance on using prediction-based workload and resource management.