Using a task dependency job-scheduling method to make energy savings in a cloud computing environment

Chen, Rongli; Chen, Xiaozhong; Yang, Cairu

doi:10.1007/s11227-021-04035-5

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…It nullifies the requirement to re-transmit the entire VM when any part gets corrupted or lost during migration. It also prevents page faults and paged-out issues in all circumstances [19].…”

Section: Decisively This Research Comprehends An Advanced Memory Mana...mentioning

confidence: 99%

“…The time and energy penalties associated with VM migration are influenced significantly by the necessary migration time. The energy penalty rises concurrently with the increased power consumption [19]. Therefore, along with minimizing time and energy consumption, the proposed split migration technique makes the migration of any massive VM rather scalable and practical.…”

Section: Overcoming Of Time Constraintsmentioning

confidence: 99%

“…The corruption and data losses in any extensive VM transportation system are widespread incidents. It may happen because of bandwidth limitation, channel congestion, transmission errors, channel noise, unavailability of adequate storage space at the host machines, specific security attacks, etc [19,20]. Data loss or data corruption in any VM migration mechanism may usually collapse the VM restoring tool.…”

Section: Problem Statementmentioning

confidence: 99%

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An Integrated Technique for Securing Large Virtual Machine Migration

Bhattacharjee,

Choudhury,

Abdelmoniem

et al. 2024

Preprint

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Maintaining data confidentiality and integrity during the large VM migration is quite challenging. Simultaneously, the use of complex encryption or steganography for managing them increases data and time overheads. These may cause data loss. The transportation of large VMs further consumes significant bandwidth and causes page faults. However, these issues aren't dealt with in modern literature, despite many research attempts. Moreover, unlawful intrusions and various transmission errors make these matters worse. Hence, this research work proposes an efficient technique that addresses such outstanding issues in a unified way. The suggested solution has a special data compression method that reduces big VM sizes to 53.9%, a new steganography method to enhance data confidentiality and integrity, and a smart split migration method to stop page faults as well as lower data loss to 0.0009%. The results further show that it cuts down on downtime by 10% more than the existing VM migration methods. The obtained results justify its efficiencies over other existing ones in distinct dimensions.

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Section: Decisively This Research Comprehends An Advanced Memory Mana...mentioning

confidence: 99%

Section: Overcoming Of Time Constraintsmentioning

confidence: 99%

Section: Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Technique for Securing Large Virtual Machine Migration

Bhattacharjee,

Choudhury,

Abdelmoniem

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…ey presented a classification approach to better represents the workload variations for resource scheduling. Chen et al [22] considered the heterogeneous characteristics of data centers and modeled energy consumption based on the frequency and kernel number of the virtual machine CPU.…”

Section: Related Workmentioning

confidence: 99%

PAS: Performance-Aware Job Scheduling for Big Data Processing Systems

Shen³

et al. 2022

Security and Communication Networks

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Big data analytics has become increasingly vital in many modern enterprise applications such as user profiling and business process optimization. Today’s big data processing systems, such as Hadoop MapReduce, Spark, and Hive, treat big data applications as a batch of jobs for scheduling. Existing schedulers in production systems often maintain fair allocation without considering application performance and resource utilization simultaneously. It is challenging to perform job scheduling in big data systems to achieve both low turnaround time and high resource utilization due to the high complexity in data processing logics and the dynamic variation in workloads. In this article, we propose a performance-aware scheduler, referred to as PAS, which dynamically schedules big data jobs in Hadoop YARN and Spark and autonomously adjusts scheduling policies to improve application performance and resource utilization. Specifically, PAS schedules multiple concurrent jobs using different policies based on the predicted job completion time and employs a greedy approach and a one-step lookahead strategy to opportunistically maximize the average job performance while still maintaining a satisfactory level of resource utilization. We implement PAS in Hadoop YARN and evaluate its performance with HiBench, a well-known big data processing benchmark. Experimental results show that PAS reduces the average turnaround time by 25% and the makespan by 15% in comparison with four state-of-the-art schedulers.

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“…Cooperative learning is a well-established teaching method, and it is a common strategy in the field of research and teaching. Since most studies show the positive impact of cooperative learning, how to bring the greatest positive impact using this learning strategy is worthy of in-depth discussion (Dyson et al, 2004;Chen et al, 2021a).…”

Section: Quiz-basic Score Scorementioning

confidence: 99%

The Efficiency of Cooperative Learning in Physical Education on the Learning of Action Skills and Learning Motivation

Yang

Chen

et al. 2021

Front. Psychol.

Self Cite

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This paper proposes a cooperative learning method for use in physical education, involving two different grouping methods: S-type heterogeneous grouping and “free” grouping. Cooperative learning was found to enhance the effectiveness of basketball skills learning and learning motivation. A comparison was made of the differences between action skills grouping (the control group) and “free” grouping (the experimental group). The ARCS Motivation Scale and Basketball Action Skills Test were used to measure results, and SPSS statistical analysis software was used for relevant statistical processing (with α set to.05). The results showed that overall skills, dribbling and passing among the action skills groups and “free” groupings significantly improved, but results for shooting were not significant; motivation levels for the two grouping methods significantly improved overall, and no significant differences in learning motivation and learning effectiveness were found between the different grouping methods. It is clear that teachers should first establish a good relationship between and with students, and free grouping methods can be used to good effect. Teachers using cooperative learning should intervene in a timely manner and choose suitable grouping methods according to the teaching goals.

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Using a task dependency job-scheduling method to make energy savings in a cloud computing environment

Cited by 16 publications

References 41 publications

An Integrated Technique for Securing Large Virtual Machine Migration

An Integrated Technique for Securing Large Virtual Machine Migration

PAS: Performance-Aware Job Scheduling for Big Data Processing Systems

The Efficiency of Cooperative Learning in Physical Education on the Learning of Action Skills and Learning Motivation

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