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摘要:
随着云计算技术的不断发展，越来越多的企业和组织开始采用跨云的方式进行IT交付.跨云环境可以更有效地应对传统单云环境资源利用率低、资源受限以及供应商锁定等问题，并对云资源进行统一管理.由于跨云环境中资源具有异构性，导致跨云任务调度变得更为复杂.基于此，如何合理地调度用户任务并将其分配到最佳的跨云资源上执行，成为了跨云环境中需要解决的重要问题.拟从跨云环境的角度出发，探讨该环境下任务调度算法研究的进展及挑战.首先，结合跨云环境特征将云计算分为联盟云、多云环境并进行详细介绍，同时回顾已有的任务调度类型并分析其优缺点；其次，根据研究现状选取代表性文献对跨云环境下任务调度算法进行整理、分析；最后探讨了跨云环境下任务调度算法研究中的不足和未来的研究趋势，为跨云环境下任务调度算法的进一步研究提供了参考.
Abstract:As cloud computing technology advances continuously, there are a growing number of enterprises and organizations choosing the inter-cloud approach to apply on IT delivery. Inter-cloud environments can efficiently solve problems such as low resource utilization, resource limitation, and vendor lock-in in traditional single-cloud environments, and manage cloud resources in an integrated model. Due to the heterogeneity of resources in the inter-cloud environment, which will complicate the scheduling of inter-cloud tasks. Based on the current status, how to logically schedule user tasks and allocate them to the most suitable inter-cloud resources for execution has developed to be an important issue to be solved in the inter-cloud environment. From the perspective of the inter-cloud environment, we discuss the progress and future challenges of research on the task of scheduling algorithms under this environment. Firstly, combined with the characteristics of an inter-cloud environment, cloud computing is divided into federated cloud and multi-cloud environments and introduced in detail. Meanwhile, the existing task scheduling types are reviewed and their advantages and disadvantages are analyzed. Secondly, based on the classification and current research procedure, representative documents are selected to analyze the algorithms for task scheduling on inter-cloud. Finally, shortcomings in research on algorithms for task scheduling in inter-cloud and future research trends are discussed, which provide a reference for further research on inter-cloud task scheduling.
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Keywords:
- cloud computing /
- inter-cloud /
- task scheduling /
- federated cloud /
- multi-cloud
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图 2 跨云环境下任务调度示意图
Figure 2. Schematic diagram of task scheduling in inter-cloud environment
表 1 单云环境下常见的任务调度算法
Table 1 Common Task Scheduling Algorithms in Single Cloud Environment
算法 调度机制 优点 缺点 FCFS 将任务按照到达的先后顺序调度资源. 易于实现 性能有限 Min_Min 将最小完成时间的任务分配到处理时间最短的资源上. 易于实现 资源利用率不高 Max_Min 将最大完成时间的任务分配到处理时间最短的资源上. 易于实现 资源利用率不高 MCT 将任务以任意顺序分配到具有最早完成时间的资源上. 任务完成时间短 任务总体执行时间长 MET 将任务分配到具有最小时间花费的资源上. 任务处理速度快 资源分配不均 RR 将任务分配到相同时间量的资源上. 易于实现、调度机制公平 性能有限 ACO 模拟蚂蚁的觅食方式，采用正反馈机制，依据信息素寻找最短路径. 收敛效果较好 初期速度较慢，等待时间长 PSO 模拟鸟群的觅食方式，粒子追随当前的最优粒子在解空间中搜索，通过迭代找到近似最优解. 易于实现、收敛速度快、效率高 易陷入局部最优 GA 模拟自然生物进化过程，通过选择、交叉、变异等方式不断迭代寻找最优解. 可扩展性强、全局优化 易过早收敛、较为依赖参数 SA 模拟固体退火过程，设定初始值不断迭代衰减得到近似最优解. 鲁棒性强、全局优化 收敛速度慢 TS 模拟人类的记忆机制，引入禁忌准则来避免迂回搜索最终得到最优解. 能够跳出局部最优 较为依赖初始解、搜索时间长 
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表 2 联盟云环境下任务调度算法对比
Table 2 Comparison of Task Scheduling Algorithms in Federated Cloud Environment
算法来源 算法描述 优化目标 工具 特点 文献[40] 通过成本决策函数的计算结果分配资源. 最大完成时间、成本 KVM （kernel-based virtual machine），OpenStack 优点：成本降低
缺点：限制成本会影响调度策略文献[41] 利用代理将博弈论引入其中，迫使资源实体之间进行隐性协调，最终向纳什均衡解靠拢. 成本 ProtoPeer platform 优点：成本降低，可伸缩性强
缺点：代理设计复杂文献[43] 将最大完成时间的任务分配到处理时间最短的资源上. 最大完成时间、负载均衡 BioNimbuZ，Amazon EC2 优点：任务完成时间减小
缺点：未考虑环境中其他因素影响
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表 3 异构公有云环境下任务调度算法对比
Table 3 Comparison of Task Scheduling Algorithms in Heterogeneous Public Cloud Environment
算法来源 算法描述 优化目标 工具 特点 文献[46] 通过二进制整数规划选取成本最优的资源分配. 成本 AMPL ，CPLEX 优点：成本降低
缺点：求解时间长，存在偏差文献[47] 利用适应度函数求导和变异的思想改进遗传算法. 最大完成时间 MATLAB 优点：任务完成时间减小
缺点：未考虑环境中其他因素影响文献[48] 为工作流任务中部分关键路径分配子截止时间. 成本 Workflow Generator 优点：成本降低，有截止时间约束
缺点：未考虑任务执行时间文献[49] 以多个随机键组成的向量作为输入并不断迭代，改进遗传算法减少调度中不必要的虚拟机数量. 最大完成时间、成本 Amazon EC2，Google
Cloud Platform，Microsoft Azure，Java优点：成本降低，响应迅速，可拓展性强
缺点：不支持工作流调度文献[50] 改进了遗传算法中的交叉及变异算子，允许云服务使用者根据偏好选择最优方案. 响应时间、成本、可靠性 QWS Dataset （2.0） 优点：响应迅速，可拓展性强，收敛速度快
缺点：参数设置复杂文献[51] 基于云服务提供商的历史价格计算其平均值及趋势，动态预测未来价格. 成本 Amazon EC2 优点：成本降低，负载均衡
缺点：需要大量时间收集云服务提供商的历时价格信息，未考虑环境中其他因素影响文献[52] 不断更新实际任务执行的信息，依据这些信息来动态调整资源
分配.最大完成时间、能耗 自建模拟测试平台 优点：能耗降低
缺点：未考虑环境中其他因素影响文献[53] 以最大完工时间和最小成本分别作为2个调度阶段的目标，将任务映射到合适的云资源上. 最大完成时间、成本、资源利用率 MATLAB 优点：实现了最大完成时间和成本之间的平衡
缺点：实际调度性能未知文献[54] 以最大完成时间、成本和可靠性作为多目标调度策略的3个QoS指标，后根据数据传输顺序将工作流任务分配到适当的云资源上. 最大完成时间、成本、可靠性 Amazon EC2， Microsoft Azure，Google Compute Engine，Python 优点：性能得到提升
缺点：未考虑虚拟机性能波动、能耗等问题文献[55] 使用预定义的优先级构建任务调度列表，后根据可靠性、成本、将任务分配给最优的资源. 最大完成时间、成本、可靠性 CloudSim 优点：架构新颖，性能较好，可靠性强
缺点：公有云计费机制考虑简单文献[56] 基于正弦函数的变化趋势，调度算法早期倾向于收敛，后期倾向于多样性. 最大完成时间、成本、吞吐量、能耗、资源利用率、负载均衡 CloudSim，MATLAB 优点：优化目标全面，可靠性强
缺点：实际调度性能未知文献[57] 基于成本模型、能量模型和性能模型改进遗传算法. 成本 CloudSim 优点：可降低大型和超大规模工作流的能耗
缺点：未考虑环境中其他因素影响文献[58] 考虑预算约束的前提下，使最大完成时间最小化. 最大完成时间 优点：考虑了最大完成时间和成本之间的平衡
缺点：实际调度性能未知
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表 4 公有云、私有云混合环境下任务调度算法对比
Table 4 Comparison of Task Scheduling Algorithms in Public Cloud and Private Cloud Hybrid Environment
算法来源 算法描述 优化目标 工具 特点 文献[63] 以成本为导向受截止时间约束. 成本 Amazon EC2, GoGrid, Java 优点：成本降低
缺点：未考虑调度时间文献[64] 将工作流调度到私有云上，估算工作流完成时间，之后从公有云租用合适的资源并将其聚合到私有云. 成本 自建模拟测试平台 优点：成本降低
缺点：未考虑环境中其他因素影响，实际调度性能未知文献[65] 在预计完成时间超过截止时间时，将开销最小、完成时间最短的任务调度到公有云上. 截止时间、成本 CloudSim 优点：成本降低
缺点：任务级别需要考虑的因素过多文献[66] 基于染色体编码、评估、选择、交叉和突变的改进遗传算法. 最大完成时间、成本 Amazon EC2 优点：可减少成本，完成协调
缺点：没有考虑任务执行时间变化的影响文献[67] 将最大成本的任务分配给私有云，当私有云资源不足时租用性价比最高的公有云资源. 任务完成数量、成本 Gaia Cluster 优点：安全性高
缺点：仅考虑了独立任务文献[68] 结合绿色数据中心的收益、绿色能源的价格以及公共云中资源的价格执行最优任务调度. 成本 MATLAB 优点：成本降低
缺点：未考虑环境中其他因素影响文献[69] 改进了NSGA-Ⅱ算法中基于帕累托最优的快速排序. 最大完成时间、成本 CloudSim 优点：成本降低
缺点：未考虑环境中其他因素影响
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