Leveraging Computational Reuse for Cost- and QoS-Efficient Task Scheduling in Clouds

Denninnart, Chavit; Salehi, Mohsen Amini; Toosi, Adel Nadjaran; Li, Xiangbo

doi:10.1007/978-3-030-03596-9_59

Cited by 11 publications

(15 citation statements)

References 7 publications

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“…We would like to thank the anonymous reviewers of the paper. This is a substantially extended version of a paper presented at the 16th International Conference on Service-Oriented Computing (ICSOC '18) [41]. This research is supported by the National Science Foundation under award# CNS-2007209 and CNS-2047144.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Denninnart¹,

Salehi²

2021

Preprint

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Cloud-based computing systems can get oversubscribed due to the budget constraints of their users or limitations in certain resource types. The oversubscription can, in turn, degrade the users perceived Quality of Service (QoS). The approach we investigate to mitigate both the oversubscription and the incurred cost is based on smart reusing of the computation needed to process the service requests (i.e., tasks). We propose a reusing paradigm for the tasks that are waiting for execution. This paradigm can be particularly impactful in serverless platforms where multiple users can request similar services simultaneously. Our motivation is a multimedia streaming engine that processes the media segments in an on-demand manner. We propose a mechanism to identify various types of "mergeable" tasks and aggregate them to improve the QoS and mitigate the incurred cost. We develop novel approaches to determine when and how to perform task aggregation such that the QoS of other tasks is not affected. Evaluation results show that the proposed mechanism can improve the QoS by significantly reducing the percentage of tasks missing their deadlines and reduce the overall time (and subsequently the incurred cost) of utilizing cloud services by more than 9%.

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Section: Acknowledgmentsmentioning

confidence: 99%

Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Denninnart¹,

Salehi²

2021

Preprint

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“…More importantly, the caching approach cannot achieve reusing for similar tasks and uncacheable tasks such as those generated from live video streaming [5]. A novel approach to achieve reusing for similar tasks is to aggregate them in the waiting and running states [2]. Aggregating (a.k.a.…”

Section: Arriving Requestmentioning

confidence: 99%

“…In our prior work [2], we proposed an on-demand video streaming system based on a serverless cloud. In this system, video transcoding services (e.g., altering codec, resolution, frame-rate, and bit-rate) transform the format of a source video to fit the viewer's device and bandwidth requirements.…”

Section: A On-demand Video Processingmentioning

confidence: 99%

Descriptive and Predictive Analysis of Aggregating Functions in Serverless Clouds: the Case of Video Streaming

Denninnart

Li³

et al. 2020

Preprint

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Serverless clouds allocate multiple tasks (e.g., microservices) from multiple users on a shared pool of computing resources. This enables serverless cloud providers to reduce their resource usage by transparently aggregate similar tasks of a certain context (e.g., video processing) that share the whole or part of their computation. To this end, it is crucial to know the amount of time-saving achieved by aggregating the tasks. Lack of such knowledge can lead to uninformed merging and scheduling decisions that, in turn, can cause deadline violation of either the merged tasks or other following tasks. Accordingly, in this paper, we study the problem of estimating execution-time saving resulted from merging tasks with the example in the context of video processing. To learn the execution-time saving in different forms of merging, we first establish a set of benchmarking videos and examine a wide variety of video processing tasks-with and without merging in place. We observed that although merging can save up to 44% in the execution-time, the number of possible merging cases is intractable. Hence, in the second part, we leverage the benchmarking results and develop a method based on Gradient Boosting Decision Tree (GBDT) to estimate the time-saving for any given task merging case. Experimental results show that the method can estimate the time-saving with the error rate of 0.04, measured based on Root Mean Square Error (RMSE).

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“…A task in our study is modeled as an independent video segment in the form of Group Of Pictures (GOPs) that is sequentially processed (e.g., transcoded [17]) within a deadline constraint. Each task has an individual hard deadline, which is the presentation time of that video segment [15], [25]. As there is no value in executing a task that has missed its deadline, the task must be dropped from the system.…”

Section: Defer Dropmentioning

confidence: 99%

Improving Robustness of Heterogeneous Serverless Computing Systems via Probabilistic Task Pruning

Denninnart

Gentry

Salehi

2019

2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Cloud-based serverless computing is an increasingly popular computing paradigm. In this paradigm, different services have diverse computing requirements that justify deploying an inconsistently Heterogeneous Computing (HC) system to efficiently process them. In an inconsistently HC system, each task needed for a given service, potentially exhibits different execution times on each type of machine. An ideal resource allocation system must be aware of such uncertainties in execution times and be robust against them, so that Quality of Service (QoS) requirements of users are met. This research aims to maximize the robustness of an HC system utilized to offer a serverless computing system, particularly when the system is oversubscribed. Our strategy to maximize robustness is to develop a task pruning mechanism that can be added to existing task-mapping heuristics without altering them. Pruning tasks with a low probability of meeting their deadlines improves the likelihood of other tasks meeting their deadlines, thereby increasing system robustness and overall QoS. To evaluate the impact of the pruning mechanism, we examine it on various configurations of heterogeneous and homogeneous computing systems. Evaluation results indicate a considerable improvement (up to 35%) in the system robustness.

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Leveraging Computational Reuse for Cost- and QoS-Efficient Task Scheduling in Clouds

Cited by 11 publications

References 7 publications

Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Harnessing the Potential of Function-Reuse in Multimedia Cloud Systems

Descriptive and Predictive Analysis of Aggregating Functions in Serverless Clouds: the Case of Video Streaming

Improving Robustness of Heterogeneous Serverless Computing Systems via Probabilistic Task Pruning

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