The Impact of Task Runtime Estimate Accuracy on Scheduling Workloads of Workflows

Ilyushkin, Alexey; Epema, Dick

doi:10.1109/ccgrid.2018.00048

Cited by 13 publications

(9 citation statements)

References 31 publications

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“…Workflow scheduling: With s f = 0 and task sizes equal to machine capacities q f = Q, the problem reduces to workflow scheduling. [21] measures how inaccurate runtime estimates influence the schedules -which complements our study (as we assumed that estimates are known). [22] analyzes possible performance benefits of resource interleaving across the parallel stages.…”

Section: Related Workmentioning

confidence: 97%

Scheduling Methods to Reduce Response Latency of Function as a Service

Żuk¹,

Rządca²

2020

Preprint

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Function as a Service (FaaS) permits cloud customers to deploy to cloud individual functions, in contrast to complete virtual machines or Linux containers. All major cloud providers offer FaaS products (Amazon Lambda, Google Cloud Functions, Azure Serverless); there are also popular open-source implementations (Apache OpenWhisk) with commercial offerings (Adobe I/O Runtime, IBM Cloud Functions). A new feature of FaaS is function composition: a function may (sequentially) call another function, which, in turn, may call yet another function -forming a chain of invocations. From the perspective of the infrastructure, a composed FaaS is less opaque than a virtual machine or a container. We show that this additional information enables the infrastructure to reduce the response latency. In particular, knowing the sequence of future invocations, the infrastructure can schedule these invocations along with environment preparation. We model resource management in FaaS as a scheduling problem combining (1) sequencing of invocations, (2) deploying execution environments on machines, and (3) allocating invocations to deployed environments. For each aspect, we propose heuristics. We explore their performance by simulation on a range of synthetic workloads. Our results show that if the setup times are long compared to invocation times, algorithms that use information about the composition of functions consistently outperform greedy, myopic algorithms, leading to significant decrease in response latency.

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Section: Related Workmentioning

confidence: 97%

Scheduling Methods to Reduce Response Latency of Function as a Service

Żuk¹,

Rządca²

2020

Preprint

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“…Now, let us turn to the more complicated terms (the "queueing time" terms) in ( 10) and (11). First, (10):…”

Section: T 2 (S Imentioning

confidence: 99%

“…Next, let us turn to the first, more complicated term in (11). We wish to show its derivative is smaller than (15): 16)…”

Section: T 2 (S Imentioning

confidence: 99%

“…Many job scheduling systems ask for estimates from users of how long their jobs will take [4,5,11,12,24]. As a modern example, the open source SLURM workload manager for clusters (generally) requires users to provide a time limit for their job; jobs that exceed their specified time are killed, and jobs with smaller time requirements receive preference in their scheduling priority [1].…”

Section: Introductionmentioning

confidence: 99%

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Incentive Compatible Queues Without Money

Grosof¹,

Mitzenmacher²

2022

Preprint

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For job scheduling systems, where jobs require some amount of processing and then leave the system, it is natural for each user to provide an estimate of their job's time requirement in order to aid the scheduler. However, if there is no incentive mechanism for truthfulness, each user will be motivated to provide estimates that give their job precedence in the schedule, so that the job completes as early as possible.We examine how to make such scheduling systems incentive compatible, without using monetary charges, under a natural queueing theory framework. In our setup, each user has an estimate of their job's running time, but it is possible for this estimate to be incorrect. We examine scheduling policies where if a job exceeds its estimate, it is with some probability "punished" and re-scheduled after other jobs, to disincentivize underestimates of job times. However, because user estimates may be incorrect (without any malicious intent), excessive punishment may incentivize users to overestimate their job times, which leads to less efficient scheduling. We describe two natural scheduling policies, BlindTrust and MeasuredTrust. We show that, for both of these policies, given the parameters of the system, we can efficiently determine the set of punishment probabilities that are incentive compatible, in that users are incentivized to provide their actual estimate of the job time. Moreover, we prove for MeasuredTrust that in the limit as estimates converge to perfect accuracy, the range of punishment probabilities that are incentive compatible converges to [0, 1]. Our formalism establishes a framework for studying further queue-based scheduling problems where job time estimates from users are utilized, and the system needs to incentivize truthful reporting of estimates.1 An M/G/1 queue has Poisson (memoryless) arrivals (denoted by the M), general i.i.d. service times governed by a common distribution (denoted by the G), and a single server (denoted by the 1).

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“…Both approaches have in common that they need at least comprehensive knowledge about execution times of all tasks on all available nodes. However, these values are not available in advance but must be determined either by asking users for estimates [18,22,23], by analyzing historical traces [35,36,42], or by using some form of online learning [43,45]. Lotaru aims to estimate the runtime for all task-node pairs in a cluster to enable the use of existing scheduling methods in real-world systems.…”

Section: Scheduling Workflow Tasks Onto Heterogeneous Clustersmentioning

confidence: 99%

Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters

Bader,

Lehmann,

Thamsen

et al. 2022

Preprint

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Many scientific workflow scheduling algorithms need to be informed about task runtimes a-priori to conduct efficient scheduling. In heterogeneous cluster infrastructures, this problem becomes aggravated because these runtimes are required for each task-node pair. Using historical data is often not feasible as logs are typically not retained indefinitely and workloads as well as infrastructure changes. In contrast, online methods, which predict task runtimes on specific nodes while the workflow is running, have to cope with the lack of example runs, especially during the start-up.In this paper, we present Lotaru, a novel online method for locally estimating task runtimes in scientific workflows on heterogeneous clusters. Lotaru first profiles all nodes of a cluster with a set of shortrunning and uniform microbenchmarks. Next, it runs the workflow to be scheduled on the user's local machine with drastically reduced data to determine important task characteristics. Based on these measurements, Lotaru learns a Bayesian linear regression model to predict a task's runtime given the input size and finally adjusts the predicted runtime specifically for each task-node pair in the cluster based on the micro-benchmark results. Due to its Bayesian approach, Lotaru can also compute robust uncertainty estimates and provides them as an input for advanced scheduling methods.Our evaluation with five real-world scientific workflows and different datasets shows that Lotaru significantly outperforms the baselines in terms of prediction errors for homogeneous and heterogeneous clusters. CCS CONCEPTS• Information systems → Information systems applications; • Computer systems organization → Distributed architectures; • Software and its engineering → Software architectures.

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The Impact of Task Runtime Estimate Accuracy on Scheduling Workloads of Workflows

Cited by 13 publications

References 31 publications

Scheduling Methods to Reduce Response Latency of Function as a Service

Scheduling Methods to Reduce Response Latency of Function as a Service

Incentive Compatible Queues Without Money

Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters

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