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    • 3. 发明授权
    • Dynamic application placement based on cost and availability of energy in datacenters
    • 基于数据中心能源成本和可用性的动态应用放置
    • US09207993B2
    • 2015-12-08
    • US12779059
    • 2010-05-13
    • Navendu Jain
    • Navendu Jain
    • G06F15/173G06F9/50
    • G06F9/5094Y02D10/22
    • An optimization framework for hosting sites that dynamically places application instances across multiple hosting sites based on the energy cost and availability of energy at these sites, application SLAs (service level agreements), and cost of network bandwidth between sites, just to name a few. The framework leverages a global network of hosting sites, possibly co-located with renewable and non-renewable energy sources, to dynamically determine the best datacenter (site) suited to place application instances to handle incoming workload at a given point in time. Application instances can be moved between datacenters subject to energy availability and dynamic power pricing, for example, which can vary hourly in day-ahead markets and in a time span of minutes in realtime markets.
    • 基于能源成本和这些站点的能源可用性,应用程序SLA(服务水平协议)以及站点之间的网络带宽成本,动态地将应用程序实例跨多个托管站点的托管站点的优化框架,仅举几个例子。 该框架利用可能与可再生能源和不可再生能源共存的托管站点的全球网络来动态地确定适合于在给定时间点处理应用程序实例来处理传入工作负载的最佳数据中心(站点)。 应用实例可以在受到能源可用性和动态功率定价之间的数据中心之间移动,例如,在日间市场和实时市场的时间间隔可能会在几分钟内变化。
    • 4. 发明授权
    • Managing runtime execution of applications on cloud computing systems
    • 管理云计算系统上应用程序的运行时执行
    • US08719804B2
    • 2014-05-06
    • US12774203
    • 2010-05-05
    • Navendu Jain
    • Navendu Jain
    • G06F9/44G06F9/45
    • G06F11/3082G06F11/3006G06F11/301G06F11/3089G06F11/3476G06F11/3495G06F2201/865H04L43/0829H04L43/0894
    • Instances of a same application execute on different respective hosts in a cloud computing environment. Instances of a monitor application are distributed to concurrently execute with each application instance on a host in the cloud environment, which provides user access to the application instances. The monitor application may be generated from a specification, which may define properties of the application/cloud to monitor and rules based on the properties. Each rule may have one or more conditions. Each monitor instance running on a host, monitors execution of the corresponding application instance on that host by obtaining from the host information regarding values of properties on the host per the application instance. Each monitor instance may evaluate the local host information or aggregate information collected from hosts running other instances of the monitor application, to repeatedly determine whether a rule condition has been violated. On violation, a user-specified handler is triggered.
    • 相同应用程序的实例在云计算环境中的不同的相应主机上执行。 分布式监视器应用程序的实例,以与云环境中的主机上的每个应用程序实例同时执行,从而提供用户对应用程序实例的访问。 监视器应用程序可以从规范生成,规范可以根据属性定义应用程序/云的监视属性和规则。 每个规则可能有一个或多个条件。 在主机上运行的每个监视器实例通过从主机获取关于每个应用程序实例的主机上的属性值的信息来监视该主机上相应的应用程序实例的执行。 每个监视器实例可以评估从运行监视应用程序的其他实例的主机收集的本地主机信息或聚合信息,以重复确定是否违反规则条件。 在违规时,触发用户指定的处理程序。
    • 6. 发明申请
    • ALLOCATION OF COMPUTATIONAL RESOURCES WITH POLICY SELECTION
    • 计算资源分配政策选择
    • US20130246208A1
    • 2013-09-19
    • US13421959
    • 2012-03-16
    • Navendu JainIshai MenacheOhad Shamir
    • Navendu JainIshai MenacheOhad Shamir
    • G06F9/46G06Q30/08
    • G06F9/50G06F2209/501
    • A method for adaptively allocating resources to a plurality of jobs. The method comprises selecting a first policy from a plurality of policies for a first job in the plurality of jobs by using a policy selection mechanism, allocating at least one resource to the first job in accordance with the first policy, and in response to completion of the first job, updating the policy selection mechanism to obtain an updated policy selection mechanism by using at least one processor. Updating the policy selection mechanism comprises evaluating the performance of the first policy with respect to the first job by calculating a value of a metric of utility for the first policy based on conditions associated with execution of the first job and updating the policy selection mechanism based on the calculated value and a delay of execution of the first job.
    • 一种用于将资源自适应地分配给多个作业的方法。 该方法包括通过使用策略选择机制从多个策略中的第一作业的多个策略中选择第一策略,根据第一策略向第一作业分配至少一个资源,以及响应于完成 第一工作,通过使用至少一个处理器来更新策略选择机制以获得更新的策略选择机制。 更新策略选择机制包括:基于与第一作业的执行相关联的条件计算第一策略的效用度量的值来评估第一策略相对于第一作业的性能,并基于 计算的值和第一个作业的执行延迟。
    • 8. 发明申请
    • ELASTIC SCALING FOR CLOUD-HOSTED BATCH APPLICATIONS
    • 用于云计算批量应用的弹性测量
    • US20130007753A1
    • 2013-01-03
    • US13171425
    • 2011-06-28
    • Navendu Jain
    • Navendu Jain
    • G06F9/46
    • G06F9/46G06F9/4881G06F2209/483
    • An elastic scaling cloud-hosted batch application system and method that performs automated elastic scaling of the number of compute instances used to process batch applications in a cloud computing environment. The system and method use automated elastic scaling to minimize job completion time and monetary cost of resources. Embodiments of the system and method use a workload-driven approach to estimate a work volume to be performed. This is based on task arrivals and job execution times. Given the work volume estimate, an adaptive controller dynamically adapts the number of compute instances to minimize the cost and completion time. Embodiments of the system and method also mitigate startup delays by computing a work volume in the near future and gradually starting up additional compute instances before they are needed. Embodiments of the system and method also ensure fairness among batch applications and concurrently executing jobs.
    • 弹性扩展云托管批处理应用系统和方法,用于在云计算环境中执行用于处理批处理应用程序的计算实例数量的自动弹性缩放。 该系统和方法使用自动弹性缩放来最小化作业完成时间和资源的货币成本。 系统和方法的实施例使用工作负载驱动的方法来估计要执行的工作量。 这是基于任务到达和作业执行时间。 考虑到工作量估计,自适应控制器动态地调整计算实例的数量以最小化成本和完成时间。 系统和方法的实施例还通过在不久的将来计算工作量并在需要之前逐渐启动额外的计算实例来减轻启动延迟。 系统和方法的实施例还确保批量应用程序和并发执行作业之间的公平性。