Insight driven resource management & schedulingJuly 25th, 2016 • Comments Off on Insight driven resource management & scheduling
Future data center resource and workload managers – and their [distributed]schedulers – will require a new key integrate capability: analytics. Reason for this is the the pure scale and the complexity of the disaggregation of resources and workloads which requires getting deeper insights to make better actuation decisions.
For data center management two major factors play a role: the workload (processes, tasks, containers, VMs, …) and the resources (CPUs, MEM, disks, power supplies, fans, …) under control. These form the service and resource landscape and are specific to the context of the individual data center. Different providers use different (heterogeneous) hardware (revisions) resource and have different customer groups running different workloads. The landscape overall describes how the entities in the data center are spatially connected. Telemetry systems allow for observing how they behave over time.
The following diagram can be seen as a metaphor on how the two interact: the workload create a impact on the landscape. The box represent a simple workload having an impact on the resource landscape. The landscape would be composed of all kind of different entities in the data center: from the air conditioning facility all the way to the CPU. Obviously the model taken here is very simple and in real-life a service would span multiple service components (such as load-balancers, DBs, frontends, backends, …). Different kinds of workloads impact the resource landscape in different ways.
Current data center management systems are too focused on understanding resources behavior only and while external analytics capabilities exists, it becomes crucial that these capabilities need to move to the core of it and allow for observing and deriving insights for both the workload and resource behavior:
- workload behavior: Methodologies such as described in this paper, become key to understand the (heterogeneous) workload behavior and it’s service life-cycle over space and time. This basically means learning the shape of the workload – think of it as the form and size of the tile in the game Tetris.
- resource behavior: It needs to be understood how a) heterogeneous workloads impact the resources (especially in the case of over-subscription) and b) how features of the resource can impact the workload performance. Think of the resources available as the playing field of the game Tetris. Concept as described in this paper help understand how features like SR-IOV impact workload performance, or how to better dimension the service component’s deployment.
Deriving insights on how workloads behave during the life-cycle, and how resources react to that impact, as well as how they can enhance the service delivery is ultimately key to finding the best match between service components over space and time. Better matching (aka actually playing Tetris – and smartly placing the title on the playing field) allows for optimized TCO given a certain business objective. Hence it is key that the analytical capabilities for getting insights on workload and resource behavior move to the very core of the workload and resource management systems in future to make better insightful decisions. This btw is key on all levels of the system hierarchy: on single resource, hosts, resource group and cluster level.
Note: parts of this were discussed during the 9th workshop on cloud control.