考虑数据中心和分布式能源接入的配电网双层规划方法

在大数据时代背景下，社会对数据处理需求的增加导致电网中数据中心的负荷不断增长，若按数据中心原始负荷接入电网，会对配电网运行产生较大的负担。而数据中心的批处理负载与配备的储能设备决定了其可以作为灵活性负荷接入电网参与需求响应。通过选取包含数据中心、常规负荷与光伏机组的配电网作为研究对象，采用双层规划方法建立了考虑数据中心运行灵活性和分布式光伏接入的配电网规划模型，上层模型以最小化配电网系统的规划与运行成本为目标，对配电网线路以及其内部的光伏机组与燃气轮机容量进行规划，下层模型以最小化数据中心运营商的规划与运行成本为目标，在考虑数据中心需求响应特性的同时对数据中心的储能设备进行容量配置。最后以IEEE-33节点配电网系统为例进行验证，算例表明所提规划模型利用了配电网内数据中心自身的灵活性，同时降低了配电网系统与数据中心的规划成本与运行成本

Defining Service Level Agreements in Serverless Computing

The emergence of serverless computing has brought significant advancements to the delivery of computing resources to cloud users. With the abstraction of infrastructure, ecosystem, and execution environments, users could focus on their code while relying on the cloud provider to manage the abstracted layers. In addition, desirable features such as autoscaling and high availability became a provider’s responsibility and can be adopted by the user\u27s application at no extra overhead. Despite such advancements, significant challenges must be overcome as applications transition from monolithic stand-alone deployments to the ephemeral and stateless microservice model of serverless computing. These challenges pertain to the uniqueness of the conceptual and implementation models of serverless computing. One of the notable challenges is the complexity of defining Service Level Agreements (SLA) for serverless functions. As the serverless model shifts the administration of resources, ecosystem, and execution layers to the provider, users become mere consumers of the provider’s abstracted platform with no insight into its performance. Suboptimal conditions of the abstracted layers are not visible to the end-user who has no means to assess their performance. Thus, SLA in serverless computing must take into consideration the unique abstraction of its model. This work investigates the Service Level Agreement (SLA) modeling of serverless functions\u27 and serverless chains’ executions. We highlight how serverless SLA fundamentally differs from earlier cloud delivery models. We then propose an approach to define SLA for serverless functions by utilizing resource utilization fingerprints for functions\u27 executions and a method to assess if executions adhere to that SLA. We evaluate the approach’s accuracy in detecting SLA violations for a broad range of serverless application categories. Our validation results illustrate a high accuracy in detecting SLA violations resulting from resource contentions and provider’s ecosystem degradations. We conclude by presenting the empirical validation of our proposed approach, which could detect Execution-SLA violations with accuracy up to 99%