Scheduling Scientific Workloads in Private Cloud

Klusáček, Dalibor; Parák, Boris; Podolníková, Gabriela; Ürge, András

doi:10.1145/3147213.3147223

Cited by 7 publications

(1 citation statement)

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“…They claimed that employing fog nodes in addition to the cloud data center leads to an improvement of 35% in execution cost versus using the cloud data center in isolation. The problem of scheduling scientific workloads on the cloud has been researched in the work of Klusáček et al These workloads are different from their commercial counterparts, as the cloud resources are provided to researchers for free. This leads to a number of execution resources getting wasted or under utilized.…”

Section: Related Workmentioning

confidence: 99%

Load balancing aware scheduling algorithms for fog networks

Singh

Auluck

2019

Softw Pract Exp

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Fog networks have attracted the attention of researchers recently. The idea is that a part of the computation of a job/application can be performed by fog devices that are located at the network edge, close to the users. Executing latency sensitive applications on the cloud may not be feasible, owing to the significant communication delay involved between the user and the cloud data center (cdc). By the time the application traverses the network and reaches the cloud data center, it might already be too late. However, fog devices, also known as mobile data centers (mdcs), are capable of executing such latency sensitive applications. In this paper, we study the problem of balancing the application load while taking account of security constraints of jobs, across various mdcs in a fog network. In case a particular mdc does not have sufficient capacity to execute a job, the job needs to be migrated to some other mdc. To this end, we propose three heuristic algorithms: minimum distance, minimum load, and minimum hop distance and load (MHDL). In addition, we also propose an ILP-based algorithm called load balancing aware scheduling ILP (LASILP) for solving the task mapping and scheduling problem. The performance of the proposed algorithms have been compared with the cloud only algorithm and another heuristic algorithm called fog-cloud-placement (FCP). Simulation results performed on real-life workload traces reveal that the MHDL heuristic performs better as compared to other scheduling policies in the fog computing environment while meeting application privacy requirements.

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Section: Related Workmentioning

confidence: 99%