Analysis of Mixed Workloads from Shared Cloud Infrastructure

Klusáček, Dalibor; Parák, Boris

doi:10.1007/978-3-319-77398-8_2

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…We also consider the case of straggler jobs, which are migrated to other free mdcs in the system, thereby improving overall application performance. Simulations comparing RT − SAN E with iF ogstor and cdc − only on real workload data on the CERIT system [28] and Google [29] show that RT-SANE provides a better system performance, due to a higher value of Success Ratio.…”

Section: Resultsmentioning

confidence: 99%

“…Some researchers have characterized and compared "real-life" workloads [28], [29]. In [29], Google's data center workloads have been studied, including over 25 million tasks, spread over 12,500 hosts.…”

Section: Related Workmentioning

confidence: 99%

“…This work is an extension of [31], where we considered jobs whose characteristics were randomly generated. In this paper, we consider "real-life" workloads from the Czech CERIT Scientific Cloud (CERIT-SC) [28], consisting of both interactive & batch jobs. Interactive jobs require fast response times, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…For the simulations, we have considered the workload from the CERIT-SC system [28], which is a scientific cloud from Czechoslovakia, hosted by Masaryk University. This cloud has a capacity of roughly 5500 CPU cores & 5 Petabytes of storage.…”

Section: Workloadmentioning

confidence: 99%

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Scheduling Real-Time Security Aware Tasks in Fog Networks

Auluck

Rana

Nepal

et al. 2021

IEEE Trans. Serv. Comput.

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Fog computing brings the cloud closer to a user with the help of a micro data center (mdc), leading to lower response times for delay sensitive applications. RT-SANE (Real-Time Security Aware scheduling on the Network Edge) supports batch and interactive applications, taking account of their deadline and security constraints. RT-SANE chooses between an mdc (in proximity to a user) and a cloud data center (cdc) by taking account of network delay and security tags. Jobs submitted by a user are tagged as: private, semi-private and public, and mdcs and cdcs are classified as: trusted, semi-trusted and untrusted. RT-SANE executes private jobs on a user's local mdcs or pre-trusted cdcs, and semi-private and public jobs on remote mdcs and cdcs. A security and performance-aware distributed orchestration architecture and protocol is made use of in RT-SANE. For evaluation, workload traces from the CERIT-SC Cloud system are used. The effect of slow executing straggler jobs on the Fog framework are also considered, involving migration of such jobs. Experiments reveal that RT-SANE offers a higher "success ratio" (successfully completed jobs) to comparable algorithms, including consideration of security tags. Index Terms-Fog computing, mobile data center, cloud data center, security aware services, real-time systems.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Workloadmentioning

confidence: 99%

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Scheduling Real-Time Security Aware Tasks in Fog Networks

Auluck

Rana

Nepal

et al. 2021

IEEE Trans. Serv. Comput.

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“…For the simulations, we used the CERIT‐SC system workload, which is a workload of a scientific cloud from Czechoslovakia, hosted by Masaryk University. This cloud has a capacity of roughly 5500 CPU cores and 5 Petabytes of storage.…”

Section: Simulation Results and Discussionmentioning

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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A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

Kaur

Kumar

2021

Concurrency and Computation

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The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real‐time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta‐analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.

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Analysis of Mixed Workloads from Shared Cloud Infrastructure

Cited by 8 publications

References 11 publications

Scheduling Real-Time Security Aware Tasks in Fog Networks

Scheduling Real-Time Security Aware Tasks in Fog Networks

Load balancing aware scheduling algorithms for fog networks

A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions

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