Optimizing bag-of-tasks scheduling on cloud data centers using hybrid swarm-intelligence meta-heuristic

The Journal of Supercomputing - Usually, a large number of concurrent bag-of-tasks (BoTs) application execution requests are submitted to cloud data centers (CDCs), which needs to be optimally...     

Autonomic resource provisioning for software business processes

Software development nowadays involves several levels of abstraction: starting from the programming of single objects, to their combination into components, to their publication as services and the overall architecture linking elements at each level. As a result, software engineering is dealing with a wider range of artifacts and concepts (i.e., in the context of this paper: services and business processes) than ever before. In this paper we explore the importance of having an adequate engine for executing business processes written as compositions of Web services. The paper shows that, independently of the composition language used, the overall scalability of the system is determined by how the run-time engine treats the process execution. This is particularly relevant at the service level because publishing a process through a Web service interface makes it accessible to an unpredictable and potentially very large number of clients. As a consequence, the process developer is confronted with the difficult question of resource provisioning. Determining the optimal configuration of the distributed engine that runs the process becomes sensitive both to the actual number of clients and to the kinds of processes to be executed. The main contribution of the paper is to show how resource provisioning for software business processes can be solved using autonomic computing techniques. The engine separates execution in two stages (navigation and dispatching) and uses a controller to allocate the node of a cluster of computers to each one of those stages as the workload changes. The controller can be configured with different policies that define how to reconfigure the system. To prove the feasibility of the concept, we have implemented the autonomic controller and evaluated its performance with an extensive set of experiments.     

Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm

Purpose

The objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment.

Methods

The proposed IDEA combines the Taguchi method and a differential evolution algorithm (DEA). The DEA has a powerful global exploration capability on macro-space and uses fewer control parameters. The systematic reasoning ability of the Taguchi method is used to exploit the better individuals on micro-space to be potential offspring. Therefore, the proposed IDEA is well enhanced and balanced on exploration and exploitation. The proposed cost model includes the processing and receiving cost. In addition, the time model incorporates receiving, processing, and waiting time. The multi-objective optimization approach, which is the non-dominated sorting technique, not with normalized single-objective method, is applied to find the Pareto front of total cost and makespan.

Results

In the five-task five-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.368 and C(IDEA, NSGA-II) of 0.3 are superior to the ratios C(DEA, IDEA) of 0.249 and C(NSGA-II, IDEA) of 0.288, respectively. In the ten-task ten-resource problem, the mean coverage ratios C(IDEA, DEA) of 0.506 and C(IDEA, NSGA-II) of 0.701 are superior to the ratios C(DEA, IDEA) of 0.286 and C(NSGA-II, IDEA) of 0.052, respectively. Wilcoxon matched-pairs signed-rank test confirms there is a significant difference between IDEA and the other methods. In summary, the above experimental results confirm that the IDEA outperforms both the DEA and NSGA-II in finding the better Pareto-optimal solutions.

Conclusions

In the study, the IDEA shows its effectiveness to optimize task scheduling and resource allocation compared with both the DEA and the NSGA-II. Moreover, for decision makers, the Gantt charts of task scheduling in terms of having smaller makespan, cost, and both can be selected to make their decision when conflicting objectives are present.     

Cloud workflow scheduling with hybrid resource provisioning

Resource provisioning strategies are crucial for workflow scheduling problems which are widespread in cloud computing. The main challenge lies in determining the amounts of reserved and on-demand resources to meet users’ requirements. In this paper, we consider the cloud workflow scheduling problem with hybrid resource provisioning to minimize the total renting cost, which is NP-hard and has not been studied yet. An iterative population-based meta-heuristic is developed. According to the shift vectors obtained during the search procedure, timetables are computed quickly. The appropriate amounts of reserved and on-demand resources are determined by an incremental optimization method. The utilization of each resource is balanced in a swaying way, in terms of which the probabilistic matrix is updated for the next iteration. The proposed algorithm is compared with modified existing algorithms for similar problems. Experimental results demonstrate effectiveness and efficiency of the proposed algorithm.     

QoS based resource provisioning and scheduling in grids

As Grid computing has emerged as a technology for providing the computational resources to industries and scientific projects, new requirements arise. Nowadays, resource management has become an important research area in the Grid computing environment. To provision the appropriate resource to a corresponding application is a tedious task. So, it is important to check and verify the provisioning of the resource before the application’s execution. In this paper, a resource provisioning framework has been presented that offers a resource provisioning policy, which caters to provisioned resource allocation and resource scheduling. The framework has been formally specified and verified. Formal specification and verification of the framework helps in predicting possible errors before the scheduling process itself, and thus results in efficient resource provisioning and scheduling of Grid resources.     

Admission control and resource provisioning in fog-integrated cloud using modified fuzzy inference system

The Journal of Supercomputing - Fog-integrated cloud (FiC) contains a fair amount of heterogeneity, leading to uncertainty in the resource provisioning. An admission control manager (ACM) is...     

Simulating peer-to-peer cloud resource scheduling

Resource scheduling in large-scale distributed systems, such as grids and clouds, is difficult due to the size, dynamism, and volatility of resources. These resources are eclectic and autonomous, and may exhibit different usage policies, levels of participation, capabilities, local load, and reliability. Moreover, applications are likely to exhibit various patterns and levels, and distributed resources may organize into various different overlay topologies for information and query dissemination. Researchers have proposed a wide variety of approaches and policies for mapping offered load onto resources and for solving the various component parts of the scheduling problem. However, production clouds and grids may be underutilized, and may not exhibit the load to effectively characterize all of the scheduling system inputs. The composition of large-scale systems is also changing, potentially to include more individual and peer-to-peer resources. These factors will influence the effectiveness of proposed scheduling solutions. Therefore, a simulation environment is necessary to study different approaches under different scenarios, especially those that are expected, but that are not currently characteristic of existing systems. This article describes a general-purpose peer-to-peer simulation environment that allows a wide variety of parameters, protocols, strategies and policies to be varied and studied. To provide a proof of concept, utilization of the simulation environment is presented in a large-scale distributed system problem that includes a core model and related mechanisms. In particular, this article presents a definition and possible peer-to-peer solutions for the large-scale scheduling problem. Moreover, this article describes a general simulation model, some policies that can be varied, an implementation, and some sample results.     

Multiple cross-docks scheduling using two meta-heuristic algorithms

This paper considers a truck scheduling problem in a multiple cross docks while there is temporary storage in front of the shipping docks. Receiving and shipping trucks can intermittently move in and out of the docks during the time intervals between their task execution, in which trucks can enter to any of the cross docks. Thus, a mixed-integer programming (MIP) model for multiple cross docks scheduling is developed inspired by models in the body of the respective literature. Its objective is to minimize the total operation time or maximize the throughput of the cross-docking system. Moreover, additional concepts considered in the new method is multiple cross docks with a limited capacity. In this study, there are two types of delay times. The first type occurs when there is a shipping truck change and the second one occurs when the current shipping truck does not load any product from a certain receiving truck or temporary storage and waits until its needed products arrive at the shipping docks. To solve the developed model, two meta-heuristics, namely simulated annealing (SA) and firefly algorithms (FA), are proposed. In addition, a procedure for trucks scheduling in a state of a constant discrete firefly algorithm for the discrete adaptation has been proposed. The experimental design is carried out to tune the parameters of algorithms. Finally, the solutions obtained by the proposed SA and FA are compared.     

Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment

The Journal of Supercomputing - Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage...     

基于膜计算的蝙蝠算法在云计算资源调度的研究

如何进一步实现云计算环境下的资源利用最大化是目前研究的热点.建立云计算环境下的资源分配模型,云计算资源调度使用蝙蝠算法,同时引入膜计算概念,提出一种基于膜计算的蝙蝠算法,将膜系统内部分解为主膜和辅助膜,在辅助膜内进行蝙蝠的个体局部寻优,将优化后的个体传送到主膜间进行全局优化,从而达到了云计算资源优化分配要求.通过CloudSim平台与其他算法进行仿真对比表明算法提高了云计算环境下的系统处理时间和效率,使得云计算环境下的资源分配更加合理.     

A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.     

Task scheduling,resource provisioning,and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm

The Journal of Supercomputing - Cloud computing is one of the most popular distributed environments, in which, multiple powerful and heterogeneous resources are used by different user applications....     

信任驱动的资源调度算法

针对当前网格资源管理中任务与资源匹配的缺陷,基于信任效益函数和最小完成时间,提出了基于信任的Trust Mintime Min-Min算法.分析了传统的Min-Min算法,考虑Min-Min算法负载不平衡,对其在调度策略方面进行了改进.仿真实验表明,该算法不但可以有效地平衡负载,而且可以提高任务的完成率,兼顾计算的有效性和可靠性.     

Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

The Journal of Supercomputing - Scheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues...     

Empirical prediction models for adaptive resource provisioning in the cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates the cloud from the traditional computing paradigm. However, initializing a new virtual instance in a cloud is not instantaneous; cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands.Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in the cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud.     

A flexible deadline-driven resource provisioning and scheduling algorithm for multiple workflows with VM sharing protocol on WaaS-cloud

The Journal of Supercomputing - Maximized deployment of workflows in the research organizations has motivated the emergence of multi-tenant environment that offer these workflows deployment as a...     

云数据中心资源调度机制研究

云数据中心包含大量计算机,运作成本很高。有效整合资源、提高资源利用率、节约能源、降低运行成本是云数据中心关注的热点。云数据中心通过虚拟化技术将计算资源、存储资源和网络资源构建成动态的虚拟资源池;使用虚拟资源管理技术实现云计算资源自动部署、动态扩展、按需分配;用户采用按需和即付即用的方式获取资源。因此,数据中心对提高资源利用率的迫切需求,促使人们寻求新的方式以建设下一代数据中心。     

Granularity-based workflow scheduling algorithm for cloud computing

The workflow scheduling problem has drawn a lot of attention in the research community. This paper presents a workflow scheduling algorithm, called granularity score scheduling (GSS), which is based on the granularity of the tasks in a given workflow. The main objectives of GSS are to minimize the makespan and maximize the average virtual machine utilization. The algorithm consists of three phases, namely B-level calculation, score adjustment and task ranking and scheduling. We simulate the proposed algorithm using various benchmark scientific workflow applications, i.e., Cybershake, Epigenomic, Inspiral and Montage. The simulation results are compared with two well-known existing workflow scheduling algorithms, namely heterogeneous earliest finish time and performance effective task scheduling, which are also applied in cloud computing environment. Based on the simulation results, the proposed algorithm remarkably demonstrates its performance in terms of makespan and average virtual machine utilization.