Optimal Server Selection for Straggler Mitigation

Badita, Ajay; Parag, Parimal; Aggarwal, Vaneet

doi:10.1109/tnet.2020.2973224

Cited by 18 publications

(20 citation statements)

References 31 publications

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“…Much recent work in big data systems focuses on improving workflows [16], [17], [18], programming framework [19], [20], [21], task scheduling [22], [23], [24].…”

Section: Related Workmentioning

confidence: 99%

AutoDiagn: An Automated Real-Time Diagnosis Framework for Big Data Systems

Demirbaga

Wen

Noor

et al. 2022

IEEE Trans. Comput.

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Big data processing systems, such as Hadoop and Spark, usually work in large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. Several systems and methods exist to detect big data processing systems' performance degradation, perform root-cause analysis, and even overcome the issues causing such degradation. However, these solutions focus on specific problems such as stragglers and inefficient resource utilization. There is a lack of a generic and extensible framework to support the real-time diagnosis of big data systems. In this paper, we propose, develop and validate AutoDiagn. This generic and flexible framework provides holistic monitoring of a big data system while detecting performance degradation and enabling root-cause analysis. We present an implementation and evaluation of AutoDiagn that interacts with a Hadoop cluster deployed on a public cloud and tested with real-world benchmark applications. Experimental results show that AutoDiagn can offer a high accuracy root-cause analysis framework, at the same time as offering a small resource footprint, high throughput and low latency.

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“…Much recent work in big data systems focuses on improving workflows [16], [17], [18], programming framework [19], [20], [21], task scheduling [22], [23], [24].…”

Section: Related Workmentioning

confidence: 99%

AutoDiagn: An Automated Real-Time Diagnosis Framework for Big Data Systems

Demirbaga

Wen

Noor

et al. 2022

IEEE Trans. Comput.

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“…However, in large-scale setups, monitoring data across all host machines is inefficient and can create network bandwidth contention, negatively impacting job response times. The work in [30] proposes a task replication approach for job scheduling to minimize the effect of the Long-Tail problem. The authors analyze the impact of this approach in a heterogeneous platform.…”

Section: Related Workmentioning

confidence: 99%

“…This allows their model to run multiple instances in datacenters with powerful computational resources. However, the approach can handle only a single job system with the same workload characteristics and fails in the presence of diverse workloads as pointed by [30].…”

Section: Related Workmentioning

confidence: 99%

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START: Straggler Prediction and Mitigation for Cloud Computing Environments using Encoder LSTM Networks

Tuli

Gill

Garraghan

et al. 2021

IEEE Trans. Serv. Comput.

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Modern large-scale computing systems distribute jobs into multiple smaller tasks which execute in parallel to accelerate job completion rates and reduce energy consumption. However, a common performance problem in such systems is dealing with straggler tasks that are slow running instances that increase the overall response time. Such tasks can significantly impact the system's Quality of Service (QoS) and the Service Level Agreements (SLA). To combat this issue, there is a need for automatic straggler detection and mitigation mechanisms that execute jobs without violating the SLA. Prior work typically builds reactive models that focus first on detection and then mitigation of straggler tasks, which leads to delays. Other works use prediction based proactive mechanisms, but ignore heterogeneous host or volatile task characteristics. In this paper, we propose a Straggler Prediction and Mitigation Technique (START) that is able to predict which tasks might be stragglers and dynamically adapt scheduling to achieve lower response times. Our technique analyzes all tasks and hosts based on compute and network resource consumption using an Encoder Long-Short-Term-Memory (LSTM) network. The output of this network is then used to predict and mitigate expected straggler tasks. This reduces the SLA violation rate and execution time without compromising QoS. Specifically, we use the CloudSim toolkit to simulate START in a cloud environment and compare it with state-of-the-art techniques (IGRU-SD, SGC, Dolly, GRASS, NearestFit and Wrangler) in terms of QoS parameters such as energy consumption, execution time, resource contention, CPU utilization and SLA violation rate. Experiments show that START reduces execution time, resource contention, energy and SLA violations by 13%, 11%, 16% and 19%, respectively, compared to the state-of-the-art approaches.

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“…Multi-objective Optimization: Many realistic applications have multiple objectives, e.g., capacity and power usage in the communication system [ 26 , 27 ], latency and energy consumption [ 28 ], efficiency and safety in robotic systems [ 29 ]. Further, the overall aim is to optimize a non-linear function of the different objectives.…”

mentioning

confidence: 99%