Cooper: Task Colocation with Cooperative Games

Llull, Qiuyun; Fan, Songchun; Zahedi, Seyed Majid; Lee, Benjamin C.

doi:10.1109/hpca.2017.22

Cited by 18 publications

(10 citation statements)

References 34 publications

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“…Computational phases span seconds, and when the workload transitions from phase to phase, the colocation manager should respond quickly. These short time scales differ from prior studies that manage pairwise colocations that persist until workloads complete [14,24].…”

Section: Introductionmentioning

confidence: 73%

“…Finally, machine learning applications compute on diverse datasets, which determine memory intensity and shared resource contention. This diversity precludes offline profiling, popular in prior studies [14,24,29], because profiling every machine learning library for every dataset is intractable. Colocation managers should analyze dynamics within the batch workload online, updating models and policies as the server runs.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Colocation Policies with Reinforcement Learning

Sun

Lee

2020

ACM Trans. Archit. Code Optim.

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We draw on reinforcement learning frameworks to design and implement an adaptive controller for managing resource contention. During runtime, the controller observes the dynamic system conditions and optimizes control policies that satisfy latency targets yet improve server utilization. We evaluate a physical prototype that guarantees 95th percentile latencies for a search engine and improves server utilization by up to 70%, compared to exclusively reserving servers for interactive services, for varied batch workloads in machine learning.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Dynamic Colocation Policies with Reinforcement Learning

Sun

Lee

2020

ACM Trans. Archit. Code Optim.

Self Cite

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“…In this case, the value of k becomes 2 and so the complexities of MAF and CA-based RR become O(n 3 ) and O(n 2 ), respectively. Note that in many previous scheduling algorithms which reflect the effect of co-runners, only pairwise co-location of applications on a node is considered due to the intricacy of understanding the effect of a large number of co-runners on the same node [25]- [27]. Furthermore, with regard to MAF, the co-runner affinity values of users for a platform are recomputed for each iteration of the while loop in order to exclude users who cannot be used for mapping anymore.…”

Section: Time Complexity Of the Second Level Policiesmentioning

confidence: 99%

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

Hwang

Kim

Choi

2021

IEEE Trans. Serv. Comput.

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In a heterogeneous distributed system composed of various types of computing platforms such as supercomputers, grids, and clouds, a two-level scheduling approach can be used to effectively distribute resources of the platforms to users in the first-level, and map tasks of the users in nodes for each platform in the second-level for executing many-task applications. When scheduling heterogeneous resources, service providers of the system should consider the fairness among multiple users as well as the system efficiency. However, the fairness cannot be achieved by simply distributing an equal amount of resources from each platform to every user. In this paper, we investigate how to address the fairness issue among multiple users in a heterogeneous distributed system. We present three first-level resource allocation policies of a provider affinity first policy, an application affinity first policy, and a platform affinity based round-robin policy, and two second-level task mapping policies of a most affected first policy and a co-runner affinity based round-robin policy. Using trace-based simulations, we evaluate the performance of various combinations of the first and second level scheduling policies. Our extensive simulation results demonstrate that the first-level policy plays a crucial role to achieve relatively good fairness.

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“…Paragon is a QoS-aware scheduler that considers heterogeneous resources and interference in a large scale datacenter for single node applications [2]. A fair placement technique based on game theory is proposed to perform pairwise task collocations [30]. A QoS-aware management system called Quasar is proposed to handle resource allocation and assignment of incoming workloads, including distributed applications, in a heterogeneous cluster [15].…”

Section: Related Workmentioning

confidence: 99%

Holistic VM Placement for Distributed Parallel Applications in Heterogeneous Clusters

Kim

Pham

Baek

et al. 2021

IEEE Trans. Serv. Comput.

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In a heterogeneous cluster, virtual machine (VM) placement for a distributed parallel application is challenging due to numerous possible ways of placing the application and complexity of estimating the performance of the application. This study investigates a holistic VM placement technique for distributed parallel applications in a heterogeneous cluster, aiming to maximize the efficiency of the cluster and consequently reduce the costs for service providers and users. The proposed technique accommodates various factors that have an impact on performance in a combined manner. First, we analyze the effects of the heterogeneity of resources, different VM configurations, and interference between VMs on the performance of distributed parallel applications with a wide diversity of characteristics, including scientific and big data analytics applications. We then propose a placement technique that uses a machine learning algorithm to estimate the runtime of a distributed parallel application. To train a performance estimation model, a distributed parallel application is profiled against synthetic workloads that mostly utilize the dominant resource of the application, which strongly affects the application performance, reducing the profiling space dramatically. Through experimental and simulation studies, we show that the proposed placement technique can find good VM placement configurations for various workloads.

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Cooper: Task Colocation with Cooperative Games

Cited by 18 publications

References 34 publications

Dynamic Colocation Policies with Reinforcement Learning

Dynamic Colocation Policies with Reinforcement Learning

Achieving Fairness-Aware Two-Level Scheduling for Heterogeneous Distributed Systems

Holistic VM Placement for Distributed Parallel Applications in Heterogeneous Clusters

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