Task scheduling algorithms for multi-cloud systems: allocation-aware approach

Panda, Sanjaya Kumar; Gupta, Indrajeet; Jana, Prasanta K.

doi:10.1007/s10796-017-9742-6

Cited by 49 publications

(20 citation statements)

References 26 publications

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“…Cloud computing elements have been around for some time, with companies such as Google offering certain aspects of cloud, such as email. However, in recent years the cloud solution has developed and matured to offer the full range of IT services (Panda et al 2017). According to a report by Forrester Research, the global public cloud market is expected to reach $191 billion by 2020, growing significantly from 2013's market size of $58 billion (Columbus 2014).…”

Section: Introductionmentioning

confidence: 99%

Risks and rewards of cloud computing in the UK public sector: A reflection on three Organisational case studies

Jones¹,

Irani

Sivarajah

et al. 2017

Inf Syst Front

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Government organisations have been shifting to cloud-based services in order to reduce their total investments in IT infrastructures and resources (e.g. data centers), as well as capitalise on cloud computing's numerous rewards. However, just like any other technology investments there are also concerns over the potential risks of implementing cloud-based technologies. Such concerns and the paucity of scholarly literature focusing on cloud computing from a governmental context confirm the need for exploratory research and to draw lessons for government authorities and others in order to ensure a reduction in costly mistakes. This paper therefore investigates the implementation of cloud computing in both a practical setting and from an organisational user perspective via three UK local government authorities. Through the qualitative case study enquiries, the authors are able to extrapolate perceived rewards and risks factors which are mapped against the literature so that emergent factors can be identified. All three cloud deployments resulted in varying outcomes which included key rewards such as improved information management, flexibility of work practices and also posed risks such as loss of control and lack of data ownership to the organisations. These findings derived from the aggregated organisational user perspectives will be of benefit to both academics and practitioners engaged in cloud computing research and its strategic implementation in the public sector.

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Section: Introductionmentioning

confidence: 99%

Risks and rewards of cloud computing in the UK public sector: A reflection on three Organisational case studies

Jones¹,

Irani

Sivarajah

et al. 2017

Inf Syst Front

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show abstract

“…Panda et al (2019) have proposed three designation-aware affair scheduling algorithms for a multi-cloud setting. These algorithms depend on the old min-min and max-min algorithm and extend the multi-cloud setting.…”

Section: Review Of the Selected Scheduling Methodsmentioning

confidence: 99%

Scheduling mechanisms in the cloud environment: a methodological analysis

Zhang

Odbal

Abnoosian

2020

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Purpose Today, with the rapid growth of cloud computing (CC), there exist several users that require to execute their tasks by the available resources to obtain the best performance, reduce response time and use resources. However, despite the significance of the scheduling issue in CC, as far as the authors know, there is not any systematic and inclusive paper about studying and analyzing the recent methods. This paper aims to review the current mechanisms and techniques, which can be addressed in this area. Design/methodology/approach The central purpose of this paper refers to offering a complete study of the state-of-the-art planning algorithms in the cloud and also instructions for future research. Besides, this paper offers a methodological analysis of the scheduling mechanisms in the cloud environment. Findings The central role of this paper is to present a summary of the present issues related to scheduling in the cloud environment, providing a structure of some popular techniques in cloud scheduling scope and defining key areas for the development of cloud scheduling techniques in the future research. Research limitations/implications In this paper, scheduling mechanisms are classified into two main categories include deterministic and non-deterministic algorithms; however, it can also be classified into different categories. In addition, the selection of all related papers could not be ensured. It is possible that some appropriate and related papers were removed in the search process. Practical implications According to the results of this paper, the requirement for more suitable algorithms exists to allocate tasks for resources in cloud environments. In addition, some principal rules in cloud scheduling should be re-evaluated to achieve maximum productivity and minimize wasted expense and effort. In this direction, to stay away from overloading and under loading of components and resources, the proposed method should execute workloads in an adaptable and scalable way. As the number of users increased in cloud environments, the number of tasks in the cloud that needed to be scheduled proportionally increased. Thus, an efficient mechanism is needed for scheduling tasks in these environments. Originality/value The general information gathered in this study makes the researchers acquainted with the state-of-the-art scheduling area of the cloud. Entirely, the answers to the research questions summarized the main objective of scheduling, current challenges, mechanisms and methods in the cloud systems. The authors hope that the results of this paper lead researchers to present more efficient scheduling techniques in cloud systems.

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“…It measures the number of completed tasks per unit time [11,12,39,40]. The throughput is calculated through the following Eqs.…”

Section: Throughputmentioning

confidence: 99%

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Mokhtari¹,

Motameni²,

Bayat³

2022

Computer Systems Science and Engineering

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In this study, a new feature is added to the smart message passing interface (SMPI) approach (SMPIA) based on the prioritization method, which can completely eliminate the task starvation and lack of sufficient resources problems through prioritizing the tasks. The proposed approach is based on prioritizing the tasks and the urgency of implementation. Tasks are prioritized based on execution time, workload, the task with a more sensitive priority is executed earlier by the free source. The idea of demand-bound functions (DBFs) was extended to the SMPIA setting based on partitions and caps. For each task, two DBFs are constructed, DBFLO and DBFHI, for the LO and HI criticality modes, respectively. The simulation results returned by MATLAB showed that with the optimized SMPIA (O-SMPIA), the parameters of maximum service execution time, response time, delay time, and throughput improved in this work. In addition, the results confirmed that the reduction of execution time, completion time, and resource consumption time did not affect the response time and throughput of workflow tasks and did not cause inefficient use of resources in virtual machines (VMs) and data centers (DCs). The evaluation of performance metrics showed that the delay, response time of the Greedy algorithm was less than that of Max-Min and Min-Min. At the same time, the execution time of Max-Min was less than the others and the throughput of the Greedy was longer. The effect and throughput of O-SMPIA became more obvious as change to the job count and the number of cloud workloads increased. It is also worth mentioning that one of the main advantages of the O-SMPIA to other methods is the efficient use of time to execute all the defined tasks by CPU.

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Task scheduling algorithms for multi-cloud systems: allocation-aware approach

Cited by 49 publications

References 26 publications

Risks and rewards of cloud computing in the UK public sector: A reflection on three Organisational case studies

Risks and rewards of cloud computing in the UK public sector: A reflection on three Organisational case studies

Scheduling mechanisms in the cloud environment: a methodological analysis

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

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