An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Jung, Daeyong; Choi, Hee Seok; Lee, Dae Won; Yu, Heonchang; Lee, Eun-Young

doi:10.1007/978-3-662-44917-2_55

Cited by 2 publications

(2 citation statements)

References 4 publications

(2 reference statements)

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“…Jung et. al. propose multi-user workload scheduling on various CUs based on genetic algorithms [20]. Beyond resource allocation and scheduling, a major challenge in CaaS frameworks is the varying delay in the completion of various multimedia processing workloads [4], [21].…”

Section: B Related Workmentioning

confidence: 99%

Dithen: A Computation-as-a-Service Cloud Platform for Large-Scale Multimedia Processing

Doyle

Giotsas²,

Anam

et al. 2019

IEEE Trans. Cloud Comput.

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We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel execution of large-scale multimedia tasks. Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to minimize the cloud infrastructure cost under deadline-abiding execution. Dithen combines three key properties: (i) the reactive assignment of individual multimedia tasks to available computing units according to availability and predetermined time-to-completion constraints; (ii) optimal resource estimation based on Kalmanfilter estimates; (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of units servicing workloads. The deployment of Dithen over Amazon EC2 spot instances is shown to be capable of processing more than 80,000 video transcoding, face detection and image processing tasks (equivalent to the processing of more than 116 GB of compressed data) for less than $1 in billing cost from EC2. Moreover, the proposed AIMD-based control mechanism, in conjunction with the Kalman estimates, is shown to provide for more than 27% reduction in EC2 spot instance cost against methods based on reactive resource estimation. Finally, Dithen is shown to offer a 38% to 500% reduction of the billing cost against the current state-of-the-art in CaaS platforms on Amazon EC2 (Amazon Lambda and Amazon Autoscale). A baseline version of Dithen is currently available at

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Section: B Related Workmentioning

confidence: 99%

Dithen: A Computation-as-a-Service Cloud Platform for Large-Scale Multimedia Processing

Doyle

Giotsas²,

Anam

et al. 2019

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

“…The subtraction in (10) contrasts between the workload's CUS requirement, r w [t], and the TTC requirement, d w [t]. In addition, following proportional fairness problems in other resource allocation work (notably in cellular network scheduling algorithms [18]), we opted for the use of the natural logarithm in the demand side of the objective function and pursue the maximization of f (s w [t]).…”

Section: Workload Execution With Confirmed Ttcmentioning

confidence: 99%

Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms

Doyle¹,

Giotsas²,

Anam³

et al. 2016

2016 IEEE International Conference on Cloud Engineering (IC2E)

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Abstract-Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints; (ii) accurate resource prediction; (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints; (ii) Kalman-filter estimates for resource prediction; and (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).

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An Estimation-Based Task Load Balancing Scheduling in Spot Clouds

Cited by 2 publications

References 4 publications

Dithen: A Computation-as-a-Service Cloud Platform for Large-Scale Multimedia Processing

Dithen: A Computation-as-a-Service Cloud Platform for Large-Scale Multimedia Processing

Cloud Instance Management and Resource Prediction for Computation-as-a-Service Platforms

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