EdgeTuner: Fast Scheduling Algorithm Tuning for Dynamic Edge-Cloud Workloads and Resources

Han, Rui; Wen, Shilin; Liu, Chi Harold; Yuan, Ye; Wang, Guoren; Chen, Lydia Y.

doi:10.1109/infocom48880.2022.9796792

Cited by 18 publications

(2 citation statements)

References 37 publications

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“…[ 25 ] proposes a hybrid multi-objective optimization algorithm that combines the distribution estimation algorithm and deep Q-network to solve FJSP. Therefore, the cluster’s performance largely depends on the configurations of scheduling algorithms [ 26 , 27 , 28 , 29 ] when workloads dynamically change. To this end, we need to select the optimal one from all the algorithms by comparing the scheduling result of each scheduling algorithm.…”

Section: Background and Related Workmentioning

confidence: 99%

K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization

et al. 2023

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In recent years, Kubernetes (K8s) has become a dominant resource management and scheduling system in the cloud. In practical scenarios, short-running cloud workloads are usually scheduled through different scheduling algorithms provided by Kubernetes. For example, artificial intelligence (AI) workloads are scheduled through different Volcano scheduling algorithms, such as GANG_MRP, GANG_LRP, and GANG_BRA. One key challenge is that the selection of scheduling algorithms has considerable impacts on job performance results. However, it takes a prohibitively long time to select the optimal algorithm because applying one algorithm in one single job may take a few minutes to complete. This poses the urgent requirement of a simulator that can quickly evaluate the performance impacts of different algorithms, while also considering scheduling-related factors, such as cluster resources, job structures and scheduler configurations. In this paper, we design and implement a Kubernetes simulator called K8sSim, which incorporates typical Kubernetes and Volcano scheduling algorithms for both generic and AI workloads, and provides an accurate simulation of their scheduling process in real clusters. We use real cluster traces from Alibaba to evaluate the effectiveness of K8sSim, and the evaluation results show that (i) compared to the real cluster, K8sSim can accurately evaluate the performance of different scheduling algorithms with similar CloseRate (a novel metric we define to intuitively show the simulation accuracy), and (ii) it can also quickly obtain the scheduling results of different scheduling algorithms by accelerating the scheduling time by an average of 38.56×.

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Section: Background and Related Workmentioning

confidence: 99%

K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization

et al. 2023

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“…Table 7 summarizes existing scheduling techniques from the following three perspectives. Cluster resource management systems Many cluster resource management systems have been developed to allocate available resources to their jobs [16,25,26,45,49,83]. For example, Mesos [32] is the first cluster resource management system released by UC Berkeley.…”

Section: Related Workmentioning

confidence: 99%

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

et al. 2023

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Edge-cloud applications are rapidly prevailing in recent years and pose the challenge of using both resource-strenuous edge devices and elastic cloud resources under dynamic workloads. Efficient resource allocation on edge-cloud jobs via cluster schedulers (e.g. Kubernetes/Volcano scheduler) is essential to guarantee their performance, e.g. tail latency, and such allocation is sensitive to scheduler configurations such as applied scheduling algorithms and task restart/discard policy. Deep reinforcement learning (DRL) is increasingly applied to optimize scheduling decisions. However, DRL faces the conundrum of achieving high rewards at a dauntingly long training time (e.g. hours or days), making it difficult to tune the scheduler configurations online in accordance to dynamically changing edge-cloud workloads and resources. For such an issue, this paper proposes EdgeTuner, a fast scheduler configuration tuning approach that efficiently leverages DRL to reduce tail latency of edge-cloud jobs. The enabling feature of EdgeTuner is to effectively simulate the execution of edge-cloud jobs under different scheduler configurations and thus quickly estimate these configurations’ influence on job performance. The simulation results allow EdgeTuner to timely train a DRL agent in order to properly tune scheduler configurations in dynamic edge-cloud environment. We implement EdgeTuner in both Kubernetes and Volcano schedulers and extensively evaluate it on real workloads driven by Alibaba production traces. Our results show that EdgeTuner outperforms prevailing scheduling algorithms by achieving much lower tail latency while accelerating DRL training speed by an average of 151.63x.

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SMCoEdge: Simultaneous Multi-server Offloading for Collaborative Mobile Edge Computing

Xu,

Li,

Chu

et al. 2024

Lecture Notes in Computer Science

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EdgeTuner: Fast Scheduling Algorithm Tuning for Dynamic Edge-Cloud Workloads and Resources

Cited by 18 publications

References 37 publications

K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization

K8sSim: A Simulation Tool for Kubernetes Schedulers and Its Applications in Scheduling Algorithm Optimization

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

SMCoEdge: Simultaneous Multi-server Offloading for Collaborative Mobile Edge Computing

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