Shoubin Dong

A Multi-Objective Optimization Scheduling Method Based on the Ant Colony Algorithm in Cloud Computing

For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the users resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the users budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this methods performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.

PGO: A parallel computing platform for global optimization based on genetic algorithm

This paper presents the design, architecture and implementation of a general parallel computing platform, termed PGO, based on the Genetic Algorithm (GA) for global optimization. PGO provides an efficient and easy-to-use framework for parallelizing the global optimization procedure for general scientific modeling and simulation processes. Along with a core optimization kernel built on a GA, PGO also includes a general input generator and an output extractor that can facilitate its easy integration with various scientific computing tasks. In this paper, we demonstrate the efficiency and versatility of PGO with two different applications: (1) the parallelization of a large scale parameter estimation problem associated with modeling water flow in a heterogeneous deep vadose zone; (2) the parallelization of a complex simulation-optimization procedure for searching for an optimal groundwater remediation design. PGO is developed as an open source code, and is independent of the computer operating system. It has been tested in a heterogeneous computing environment consisting of Solaris 9, Fedora Core 2 Linux, and Microsoft Windows machines, and is freely available for download from http://grid.scut.edu.cn/PGO/.

Dynamic VM Consolidation for Energy-Aware and SLA Violation Reduction in Cloud Computing

With the large-scale deployment of virtualized data centers, energy consumption and SLA (Service Level Agreement) violation have already become the urgent issue to be solved. And it is essential and important to design energy-aware allocation policy for energy-aware and SLA violation reduction. In this paper, we propose a novel allocation and selection policy for the dynamic virtual machine (VM) consolidation in virtualized data centers to reduce energy consumption and SLA violation. Firstly, we use the mean and standard deviation of CPU utilization for VM to determine the hosts overloaded or not, secondly we use the positive maximum correlation coefficient to select VMs from those overloading hosts for migration. Although the proposed allocation and selection policies performs a little worse than the previous ones in energy consumption, experiments show that it performs greatly better than the previous ones on the whole.

Evaluation of a Performance Model of Lustre File System

As a large-scale global parallel file system, Lustre file system plays a key role in High Performance Computing (HPC) system, and the potential performance of such systems can be difficult to predict because the potential impact to application performance is not clearly understood. It is important to gain insights into the deliverable Lustre file system IO efficiency. In order to gain a good understanding on what and how to impact the performance of Lustre file system. This paper presents a study on performance evaluation of Lustre file systems and we propose a novel relative performance model to predict overhead under different performance factors. In our previous experiments, we discover that different performance factors have a closed correlation. In order to mining the correlations, we introduce relative performance model to predict performance differences between a pair of Lustre file system equipped with different performance factors. On average, relative model can predict bandwidth within 17%-28%. The results show our relative prediction model can obtain better prediction accuracy.

A multi-strategy collaborative prediction model for the runtime of online tasks in computing cluster/grid

An efficient function of a complicated or dynamic high performance computing environment requires the scheduler to dispatch the submitted tasks according to the identification of the idling resources. A derivative problem is to provide accurate forecasts of the tasks runtimes. This is usually needed to assist scheduling policies and fine tune scheduling decisions, and also used for future planning of resource allocation when conducting advance reservation. However, the characteristics of the existing prediction strategies determine that the sole strategy is not appropriate for all kinds of heterogeneous tasks. Aiming at this problem, a multi-strategy collaborative prediction model (MSCPM) for the runtime of online tasks is proposed, and a novel concept named Prediction Accuracy Assurance (PAA) as a criterion is introduced to quantitatively evaluate the precision of the prediction runtime provided by a specific prediction strategy.MSCPM uses the existing strategies of prediction runtime to generate multiple collaborative prediction schemes and takes the prediction result of the scheme which provides the optimal PAA. We evaluate the performance of the proposed model which recently integrates four simple yet widely used time series prediction strategies based on the gathered traces of three different tasks. The analysis results show that MSCPM can aggregate the superiority of the various existing prediction strategies and the evaluation criterion can pick out the near-optimal one within the prediction results provided by the integrated strategies. MSCPM provides an enhanced accuracy assurance for the prediction runtime of the online tasks in the computing environments.

Initiative movement prediction assisted adaptive handover trigger scheme in fast MIPv6

Roaming across two adjacent access networks poses a challenging issue in providing continuity services for end-users. FMIPv6, a cross layer handover scheme proposed by the IETF, requires the timely link layer (L2) trigger to invoke the handover protocols of upper layer, the specified handover procedures hence can be completed before terminating the current wireless link. Generating the L2 trigger however is not always preferable by experimental analysis. The premature L2 trigger leads to a false alarm and unnecessary handover operations with serious performance loss and resources waste. By analyzing the movement behavior of the mobile node (MN), an initiative movement predictive algorithm is developed to predict the movement trend of the MN, and an adaptive handover trigger scheme (IMP-AHT) taken as the supplement for FMIPv6 is proposed accordingly. IMP-AHT addresses the investigation on rational decision of generating L2 trigger reliably. Owing to the inevitably introduced errors of the prediction process, some effective measures are also introduced to compensate the degraded performance caused by the false decisions. Simulations will compare as well as analyze IMP-AHT and FMIPv6 to evaluate the efficiency.

Dynamically Weighted Load Evaluation Method Based on Self-adaptive Threshold in Cloud Computing

Cloud resources and their loads possess dynamic characteristics. Current research methods have utilized certain physical indicators and fixed thresholds to evaluate cloud resources, which cannot meet the dynamic needs of cloud resources or accurately reflect their resource states. To address this challenge, this paper proposes a Self-adaptive threshold based Dynamically Weighted load evaluation Method (termed SDWM). It evaluates the load state of the resource through a dynamically weighted evaluation method. First, the work proposes some dynamic evaluation indicators in order to evaluate the resource state more accurately. Second, SDWM divided the resource load into three states, including Overload, Normal and Idle using the self-adaptive threshold. It then migrated those overload resources to a balance load, and releases the idle resources whose idle times exceeded a threshold to save energy, which could effectively improve system utilization. Finally, SDWM leveraged an energy evaluation model to describe energy quantitatively using the migration amount of the resource request. The parameters of the energy model were obtained from a linear regression model according to the actual experimental environment. Experimental results showed that SDWM is superior to other methods in energy conservation, task response time, and resource utilization, and the improvements are 31.5 %, 50 %, 50.8 %, respectively. These results demonstrate the positive effect of the dynamic self-adaptive threshold. More specially, SDWM shows great adaptability when resources dynamically join or exit.

Increasing recommended effectiveness with markov chains and purchase intervals

Recommendation system is an important component of many websites and has brought huge economic benefits and challenges for online shoppers and e-commerce companies. Existing recommendation systems focus on producing a list of products which users may be interested to purchase, while overlooking the purchase chain and temporal diversity which may increase the likelihood of a purchase decision. In this paper, we propose to utilize the Markov chain to track the chain of users’ purchase behaviors and utilize the purchase intervals to improve the temporal diversity for e-commerce recommender. We design and implement several algorithms and integrate these into our recommendation model. We evaluate our system on a real-world e-commerce dataset. Experimental results demonstrate that our approach significantly improves the accuracy, conversion rate and temporal diversity compared to the state-of-the-art algorithms.

A lvy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems

Display OmittedAn expanded framework of shuffled frog-leaping algorithm for continuous optimization problem is performed according to the mechanism of exploration and exploitation, in which a lvy flight-based attractor was proposed. Experimental results show that our proposed algorithm has better performance than many state-of-the-art algorithms. A new framework of SFLA based on the exploration and exploitation mechanism was proposed to solve continuous optimization problems.Attractors based on the lvy flight was proposed to enhance the local search ability of the proposed algorithm.An interaction learning rule was proposed to improve the global search ability of the proposed algorithm.Proposed algorithm was compared with many well-known algorithms to demonstrate that it is successful to solve the real-world unconstrained and constrained continuous optimization problems. Shuffled frog-leaping algorithm (SFLA), a novel meta-heuristic optimization algorithm inspired by the foraging behavior of frogs, has been widely applied to many areas for combination problems. But it is easy to fall into the local optimum especially for the continuous optimization problems. This paper proposed a novel variant of SFLA for the continuous optimization problems based on the expanded framework (called the lvy flight-based shuffled frog-leaping algorithm, LSFLA). In this new framework, the shuffling process, local search step and global search step are combined according to the exploration and exploitation mechanism. An lvy flight based attractor was adopted for the local search step, which enhance the local search ability of algorithm due to the search of short walking distance and occasionally longer walking distance. An interaction learning rule was used for the global search step, which enhances the exploration ability. In order to test the effectiveness of LSFLA, thirty benchmark functions, six real-world constrained continuous optimization problems and a real-world support vector machine (SVM) parameter optimization problem were compared to the many well-known heuristic methods. The experimental results demonstrate that the performance of our proposed algorithm is better than other algorithms for the continuous optimization problems.

A Multi-Objective Hybrid Cloud Resource Scheduling Method Based on Deadline and Cost Constraints

We propose a task-oriented multi-objective scheduling method based on ant colony optimization (MOSACO) to optimize the finite pool of public and private computing resources in a hybrid cloud computing environment according to deadline and cost constraints. MOSACO is employed to minimize task completion times and costs using time-first and cost-first single-objective optimization strategies, respectively, and to maximize user quality of service and the profit of resource providers using an entropy optimization model. The effectiveness of the MOSACO algorithm based on multiple considerations of task completion time, cost, number of deadline violations, and degree of private resource utilization is verified using simulation and three application examples. Comparisons with similar scheduling methods demonstrate that MOSACO provides the highest optimality, and that the time-first and cost-first strategies provide definite advantages for minimizing completion time and cost, respectively.