RECORD
Tromino: Demand and DRF Aware Multi-Tenant Queue Manager for Apache Mesos Cluster
Performance · Submitted 21 May 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant cluster environment. Computational resources (i.e., CPU, memory, disk, etc. ) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multiuser environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. This lack of awareness can result in users without any pending task receiving more resource offers while users with a queue of pending tasks starve due to their high dominant shares. We have developed a policy-driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant cluster environment. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Computational resources (i.e., CPU, memory, disk, etc. ) are distributed according to the Dominant Resource Fairness (DRF) policy. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s2 | Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s3 | We have observed that multiple frameworks can cause fairness imbalance in a multiuser environment. | result 0.44 | 0.83 | 0.05 | asserted 0.99 |
| s4 | For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. | background 0.90 | 0.38 | 0.03 | asserted 0.65 |
| s5 | The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s6 | However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. | gap 0.93 | 0.17 | 0.05 | asserted 1.00 |
| s7 | This lack of awareness can result in users without any pending task receiving more resource offers while users with a queue of pending tasks starve due to their high dominant shares. | gap 0.62 | 0.30 | 0.07 | hedged possibility 0.85 |
| s8 | We have developed a policy-driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. | contribution 0.92 | 0.95 | 0.18 | asserted 1.00 |
| s9 | Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. | interpretation 0.41 | 0.65 | 0.74 | hedged possibility 0.86 |
| s10 | In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy. | result 0.84 | 0.67 | 0.54 | hedged possibility 0.96 |
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The paper
Original abstract
Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant cluster environment. Computational resources (i.e., CPU, memory, disk, etc. ) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multiuser environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. This lack of awareness can result in users without any pending task receiving more resource offers while users with a queue of pending tasks starve due to their high dominant shares. We have developed a policy-driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy.
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