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.     

A model of pilot-job resource provisioning on production grids

Pilot-job systems emerged as a computation paradigm to cope with the heterogeneity of large-scale production grids, greatly reducing fault ratios and middleware overheads. They are now widely adopted to sustain the computation of scientific applications on such platforms. However, a model of pilot-job systems is still lacking, making it difficult to build realistic experimental setups for their study (e.g. simulators or controlled platforms). The variability of production conditions, background loads and resource characteristics further complicate this issue. This paper presents a model of pilot-job resource provisioning. Based on a probabilistic modeling of pilot submission and registration, the number of pilots registered to the application host and the makespan of a divisible-load application are derived. The model takes into account job failures and it does not make any assumption on the characteristics of the computing resources, on the scheduling algorithm or on the background load. Only a minimally invasive monitoring of the grid is required. The model is evaluated in production conditions, using logs acquired on a pilot-job server deployed in the biomed virtual organization of the European Grid Infrastructure. Experimental results show that the model is able to accurately describe the number of registered pilots along time periods ranging from a few hours to a few days and in different pilot submission conditions.     

QoS based resource scheduling by computational economy in computational grid

In this paper, we consider multiple QoS based grid resource scheduling. Each of grid task agent's diverse requirements is modeled as a quality of service (QoS) dimension, associated with each QoS dimension is a utility function that defines the benefit that is perceived by a user with respect to QoS choices in that dimension. The objective of multiple QoS based grid resource scheduling is to maximize the global utility of the scheduling system.     

GAME based dynamic resource scheduling in QoS aware radio access networks

Radio resource management and QoS are finally inseparable in wideband CDMA networks. In this paper, we propose a novel wireless resource scheduler, called GAME-C, that integrates our genetic algorithm for mobiles equilibrium (GAME) with the standard CDMA transmitter closed loop power control (CLPC). GAME assigns optimally both transmitting power and bit rate to every mobile station. Optimal allocation is in the sense that every user gets only enough resources necessary for meeting or exceeding its QoS requirements while minimizing interference to other users. Having done that, we gain further benefits as well. In addition to QoS provisioning, lower transmitting power extends a mobile station battery life. Moreover, the base station coverage efficiency is improved by decreasing the probability of blocking new connections or dropping current ones. In short, GAME-C expands the number of QoS-satisfied mobile stations in a cell. Various simulations show improvements achieved over the established (CLPC) basic scheme.     

Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm

We investigate that resource provisioning and scheduling is a prominent problem due to heterogeneity as well as dispersion of cloud resources. Cloud service providers are building more and more datacenters due to demand of high computational power which is a serious threat to environment in terms of energy requirement. To overcome these issues, we need an efficient meta-heuristic technique that allocates applications among the virtual machines fairly and optimizes the quality of services (QoS) parameters to meet the end user objectives. Binary particle swarm optimization (BPSO) is used to solve real-world discrete optimization problems but simple BPSO does not provide optimal solution due to improper behavior of transfer function. To overcome this problem, we have modified transfer function of binary PSO that provides exploration and exploitation capability in better way and optimize various QoS parameters such as makespan time, energy consumption, and execution cost. The computational results demonstrate that modified transfer function-based BPSO algorithm is more efficient and outperform in comparison with other baseline algorithm over various synthetic datasets.

     

一种网格资源调度中QoS的最大化匹配算法

针对网格资源选择中复杂的QoS参数处理和精确匹配导致的资源调度率低下问题,将QoS参数按性质分类,定义了QoS参数距离,实现QoS参数相似性判断,由此提出了一种软化的参数处理模型,给出了一种最大化匹配调度算法。实验表明,该算法提高了系统吞吐量、任务满足率、资源调度率和整个系统资源利用率。     

Multi-workflow scheduling and resource provisioning in Mobile Edge Computing using opposition-based Marine-Predator Algorithm

Workflow Scheduling in Mobile Edge Computing (MEC) tries to allocate the best possible set of resources for the workflows, considering objectives such as deadline, cost, energy, Quality of Service (QoS), and so on. However, MEC may be under different workloads from the IoT and this may not have the required amount of resources to efficiently handle the workflows. To mitigate this problem, in this paper, we use proactive resource provisioning and workload prediction methods. For this purpose, we present a workload prediction method using a multilayer feed-forward Artificial Neural Network (ANN) model and apply its results for resource provisioning. Afterward, we present an opposition-based version of the Marine-Predator Algorithm (MPA) algorithm, denoted as OMPA. In this algorithm, we present a probabilistic opposition-based learning (OBL) method, which benefits from the OBL, Quasi OBL, and dynamic OBL methods. Afterward, the OMPA algorithm is used for training the multi-layer feed-forward ANN model and multi-workflow scheduling by taking into account factors such as the makespan and number of Virtual Machines (VMs). Extensive experiments conducted in the iFogSim simulator and on the NASA and Saskatchewan datasets indicate that the proposed scheme can achieve better results compared to other metaheuristic algorithms and scheduling schemes.     

云计算环境中基于混合蛙跳算法的资源调度

提出云计算环境中基于改进混合蛙跳算法（Shuffled Frog Leaping Algorithm,SFLA）的保证QoS（Quality of Service）资源调度方案。根据任务和资源的特点提出SFLA两种编码结构及其对应更新方程;对调度方案的QoS给出定义;提出根据QoS值进行个体优劣选择的改进SFLA;在CloudSim平台对算法进行了仿真实验。实验结果证明所提出的计算方案有效。     

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.     

Price aggregation in an end-to-end QoS provisioning

In this paper we are focusing on the possibility that information about end users' willingness to pay for data transfer in the end-to-end manner is propagated through the communication network to achieve certain level of QoS. We propose a scheme where price information is propagated along the network path, where each network resource subtracts its share of value from packet's price. The proposed scheme establishes a direct relationship between expressed willingness to pay and gained QoS level. The scheme could be further developed and implemented with compliancy to QoS standards, like Differentiated and Integrated Services.     

A PTS-PGATS based approach for data-intensive scheduling in data grids

Grid computing is the combination of computer resources in a loosely coupled, heterogeneous, and geographically dispersed environment. Grid data are the data used in grid computing, which consists of large-scale data-intensive applications, producing and consuming huge amounts of data, distributed across a large number of machines. Data grid computing composes sets of independent tasks each of which require massive distributed data sets that may each be replicated on different resources. To reduce the completion time of the application and improve the performance of the grid, appropriate computing resources should be selected to execute the tasks and appropriate storage resources selected to serve the files required by the tasks. So the problem can be broken into two sub-problems: selection of storage resources and assignment of tasks to computing resources. This paper proposes a scheduler, which is broken into three parts that can run in parallel and uses both parallel tabu search and a parallel genetic algorithm. Finally, the proposed algorithm is evaluated by comparing it with other related algorithms, which target minimizing makespan. Simulation results show that the proposed approach can be a good choice for scheduling large data grid applications.     

A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid

This paper is to solve efficient QoS based resource scheduling in computational grid. It defines a set of QoS dimensions with utility function for each dimensions, uses a market model for distributed optimization to maximize the global utility. The user specifies its requirement by a utility function. A utility function can be specified for each QoS dimension. In the grid, grid task agent acted as consumer pay for the grid resource and resource providers get profits from task agents. The task agent' utility can then be defined as a weighted sum of single-dimensional QoS utility function. QoS based grid resource scheduling optimization is decomposed to two subproblems: joint optimization of resource user and resource provider in grid market. An iterative multiple QoS scheduling algorithm that is used to perform optimal multiple QoS based resource scheduling. The grid users propose payment for the resource providers, while the resource providers set a price for each resource. The experiments show that optimal QoS based resource scheduling involves less overhead and leads to more efficient resource allocation than no optimal resource allocation.     

基于服务质量的云工作流调度优化算法

为满足云工作流实例的多样化需求,根据工作流的特点和云环境中资源部署结构,建立多服务质量指标的云工作流调度模型。对蚁群算法进行改进,解决其收敛速度慢、易陷入局部最优等缺点。利用用户对服务质量不同程度的偏好,引入云任务优先次序启发式规则,提出一种基于服务质量的云工作流调度算法(SPACO)。在Cloud Sim平台上,对云工作流调度模型和算法进行仿真分析,将仿真结果与基本蚁群算法(ACO)、改进的蚁群算法(PACO)进行比较,其结果表明该算法能缩短执行时间、降低能耗成本,验证了该模型的可行性和算法的有效性。     

A novel approach to resource scheduling for parallel query processing on computational grids

Advances in network technologies and the emergence of Grid computing have both increased the need and provided the infrastructure for computation and data intensive applications to run over collections of heterogeneous and autonomous nodes. In the context of database query processing, existing parallelisation techniques cannot operate well in Grid environments because the way they select machines and allocate tasks compromises partitioned parallelism. The main contribution of this paper is the proposal of a low-complexity, practical resource selection and scheduling algorithm that enables queries to employ partitioned parallelism, in order to achieve better performance in a Grid setting. The evaluation results show that the scheduler proposed outperforms current techniques without sacrificing the efficiency of resource utilisation. Recommended by: Ioannis Vlahavas     

WiMAX Mesh网络中一种集中式QoS调度算法

针对WiMAX Mesh网络集中式下资源的调度和分配,分析了现有调度算法的研究进展与优缺点,结合标准对Mesh模式的QoS定义,提出了一种WiMAX Mesh网络集中式下基于分组的QoS调度算法。通过区分数据流的优先级方式对不同业务数据流进行分组,并计算组内流节点的权值,保证同种业务流间高传输要求和低传输节点的公平性。仿真结果表明,与HRF和LRC算法相比,该算法对于分类业务的时延和网络吞吐量具有明显的优势,实现了区分业务的QoS保障。     

网格环境下基于QoS需求的关联任务调度算法*

首先描述QoS调度问题,建立QoS需求模型;然后通过分析任务的依赖性,提出时间花费、资源价格和可靠性三种QoS参数的映射机制;最后针对网格环境的新特征,提出一种以优化用户效用为目标,基于QoS的关联任务调度算法(QBDTS_UO).仿真实验结果表明,该算法能以较小的时间花费为代价,有效满足用户的QoS需求,并能大大提高网格资源的使用率.     

基于SAC的多服务移动边缘计算中任务卸载和资源配置算法

多服务移动边缘计算网络环境中的不同服务的缓存要求、受欢迎程度、计算要求以及从用户传输到边缘服务器的数据量是随时间变化的。如何在资源有限的边缘服务器中调整总服务类型的缓存子集,并确定任务卸载目的地和资源分配决策,以获得最佳的系统整体性能是一个具有挑战性的问题。为了解决这一难题,首先将优化问题转换为马尔可夫决策过程,然后提出了一种基于软演员—评论家（soft actor-critic,SAC）的深度强化学习算法来同时确定服务缓存和任务卸载的离散决策以及上下带宽和计算资源的连续分配决策。算法采用了将多个连续动作输出转换为离散的动作选择的有效技巧,以应对连续—离散混合行动空间所带来的关键设计挑战,提高算法决策的准确性。此外,算法集成了一个高效的奖励函数,增加辅助奖励项来提高资源利用率。广泛的数值结果表明,与其他基线算法相比,提出的算法在有地减少任务的长期平均完成延迟的同时也具有良好的稳定性。