Revising Max-Min for Scheduling in a Cloud Computing Context

Moggridge, Paul; Helian, Na; Sun, Yi; Lilley, Mariana; Veneziano, Vito; Eaves, Martin

doi:10.1109/wetice.2017.58

Cited by 7 publications

(1 citation statement)

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“…Max–Min algorithm acts similar to the Min–Min one, but it selects the biggest tasks for execution at first, and the tasks are executed on machines, which leads to the shortest execution times, just like the previous algorithm. In this algorithm, short tasks should wait for the completion of longer ones, which could face the system throughput and load balancing with serious challenges 53–55 …”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

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SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

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Section: Organization Of the Task Schedulingmentioning

confidence: 99%