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_Elektron_ is a [Mesos](mesos.apache.org) framework that behaves as a playground to experiment with different scheduling policies to schedule ad-hoc jobs in docker containers. It is designed as a lightweight, configurable framework, which can be used in conjunction with built-in power-capping policies to reduce the peak power and/or energy usage of co-scheduled tasks.
However, in addition to being a scheduler, Elektron also takes advantage of tools such as [Performance Co-Pilot](http://pcp.io/) and [RAPL](https://01.org/blogs/2014/running-average-power-limit--rapl) to help contain the power envelope within defined thresholds, reduce peak power consumption, and also reduce total energy consumption. Elektron is able to leverage the Mesos-provided resource abstraction to allow different algorithms to decide how to consume resource offers made by a Mesos Master.
_Elektron_ is comprised of three main components: _Task Queue_, _Scheduler_ and _Power Capper_.
* **Task Queue** - Maintains tasks that are yet to be scheduled.
* **Scheduler** - Matches tasks' resource requirements with Mesos resource offers. Tasks that matched offers are then launched on the corresponding nodes.
* **Power Capper** - The Power Capper monitors the power consumption of the nodes in the cluster through the use of [Performance Co-Pilot](http://pcp.io/). A power capping policy uses this information and decides to power cap or power uncap one or more nodes in the cluster using [RAPL](https://01.org/blogs/2014/running-average-power-limit--rapl).
* Pradyumna Kaushik, Madhusudhan Govindaraju, Srinidhi Raghavendra, Devesh Tiwari, "Exploring the Potential of using Power as a First Class Parameter for Resource Allocation in Apache Mesos Managed Clouds", in the 13th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2020), 2020. [doi](https://doi.org/10.1109/UCC48980.2020.00040), [pdf](http://cloud.cs.binghamton.edu/wordpress/wp-content/uploads/2020/11/Exploring_the_Potential_of_using_Power_as_a_First_Class_Parameter_for_Resource_Allocation_in_Apache_Mesos_Managed_Clouds_CameraReady.pdf)
* Pradyumna Kaushik, Akash Kothawale, Renan DelValle, Abhishek Jain, Madhusudhan Govindaraju, “Analysis of Dynamically Switching Energy-Aware Scheduling Policies for Varying Workloads”, in the 11th IEEE International Conference on Cloud Computing (IEEE Cloud), 2018. \[[pdf](http://cloud.cs.binghamton.edu/wordpress/wp-content/uploads/2018/05/analysis-of-energy-aware-scheduling-policy-switching-for-varying-workloads.pdf)\]
* Renan Delvalle, Pradyumna Kaushik, Abhishek Jain, Jessica Hartog, Madhusudhan Govindaraju, “Exploiting Efficiency Opportunities Based on Workloads with Electron on Heterogeneous Clusters”, in the The 10th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2017), 2017. \[[pdf](http://cloud.cs.binghamton.edu/wordpress/wp-content/uploads/2017/10/ExploitingEfficiencyOpportunitiesBasedonWorkloadswithElectrononHeterogeneousClusters.pdf)\]
* Renan DelValle, Abhishek Jain, Pradyumna Kaushik, Jessica Hartog, Madhusudhan Govindaraju, “Electron: Towards Efficient Resource Management on Heterogeneous Clusters with Apache Mesos”, in the IEEE International Conference on Cloud Computing (CLOUD), Applications Track, 2017. \[[pdf](http://cloud.cs.binghamton.edu/wordpress/wp-content/uploads/2017/07/electron-1.pdf)\]
*`-schedWindowSize` - Specify the size of the scheduling window. If no scheduling window size specified and `fixSchedWindow` option is enabled, the default size of 200 is used.
*`-schedPolSwitchCriteria` - Criteria to be used when deciding the next scheduling policy to switch to. Default criteria is task distribution (_taskDist_) based. However, one can either switch based on a Round Robin (_round-robin_) or Reverse Round Robin (_rev-round-robin_) order.