论文标题
多层云计算中的服务水平驱动的工作调度：一种受生物启发的方法
Service Level Driven Job Scheduling in Multi-Tier Cloud Computing: A Biologically Inspired Approach
论文作者
论文摘要
云计算环境通常必须处理资源需求和需求高质量服务（QoS）义务的随机 - 到达计算工作负载。典型的是，使用服务级别的验证（SLA）来管理云计算服务提供商的QoS义务。服务支持者每天面临的典型挑战是在可用于计算的有限资源与不同随机需求的高QoS要求之间保持平衡。管理这些矛盾的目标的任何失衡都可能导致客户不满意和潜在的商业罚款，或者是一个过度资源的云计算环境，获取和运营可能会高昂。因此，安排客户到达环境时的工作负载，以确保及时执行是云计算中的核心问题。文献中已经报道了解决此问题的各种方法：最短的标题，Join-Idle-Quesue，Round Robin，Minmin，Maxmin和最少的连接，仅举几例。但是，这种方法的优化策略无法捕获QoS义务及其相关的商业惩罚。本文介绍了在多层环境中服务级驱动的负载调度和平衡的方法。联合计划和平衡操作被用来在资源之间分配和安排工作，从而最大程度地减少了客户职位的总等待时间，因此，服务提供商会产生罚款的潜力。罚款模型用于量化服务提供商的惩罚，这是工作总等待时间的函数。提出了虚拟标题抽象，以促进级别级别的最佳作业计划。这个问题是NP完整的，提出了用于计算工作时间表的遗传算法。
Cloud computing environments often have to deal with random-arrival computational workloads that vary in resource requirements and demand high Quality of Service (QoS) obligations. It is typical that a Service-Level-Agreement (SLA) is employed to govern the QoS obligations of the cloud computing service provider to the client. A typical challenge service-providers face every day is maintaining a balance between the limited resources available for computing and the high QoS requirements of varying random demands. Any imbalance in managing these conflicting objectives may result in either dissatisfied clients and potentially significant commercial penalties, or an over-resourced cloud computing environment that can be significantly costly to acquire and operate. Thus, scheduling the clients' workloads as they arrive at the environment to ensure their timely execution has been a central issue in cloud computing. Various approaches have been reported in the literature to address this problem: Shortest-Queue, Join-Idle-Queue, Round Robin, MinMin, MaxMin, and Least Connection, to name a few. However, optimization strategies of such approaches fail to capture QoS obligations and their associated commercial penalties. This paper presents an approach for service-level driven load scheduling and balancing in multi-tier environments. Joint scheduling and balancing operations are employed to distribute and schedule jobs among the resources, such that the total waiting time of client jobs is minimized, and thus the potential of a penalty to be incurred by the service provider is mitigated. A penalty model is used to quantify the penalty the service provider incurs as a function of the jobs' total waiting time. A Virtual-Queue abstraction is proposed to facilitate optimal job scheduling at the tier level. This problem is NP-complete, a genetic algorithm is proposed for computing job schedules.
