SPO: A Secure and Performance-aware Optimization for MapReduce Scheduling

Maleki, Neda; Rahmani, Amir Masoud; Conti, Mauro

doi:10.1016/j.jnca.2020.102944

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…Maleki et al [77] presented a secure and performanceaware optimization framework called SPO, to minimize the makespan of tasks using a two-stage scheduler. SPO applies the HEFT algorithm in Map and Reduce stage, respectively, and considers network traffic in the shuffling phase.…”

Section: Makespan-aware Schedulersmentioning

confidence: 99%

MapReduce scheduling algorithms in Hadoop: a systematic study

Hedayati,

Maleki,

Olsson

et al. 2023

J Cloud Comp

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Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

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Section: Makespan-aware Schedulersmentioning

confidence: 99%

MapReduce scheduling algorithms in Hadoop: a systematic study

Hedayati,

Maleki,

Olsson

et al. 2023

J Cloud Comp

Self Cite

View full text Add to dashboard Cite

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“…As for the off-chain solutions, the second-layer solutions such as the first blockchain's lightning network, Ethereum's Raiden network [17], and Plasma [18] are in full swing. Along with the development of scaling technology, some security issues are also drawing attention and many related studies [19][20][21][22] have started to be proposed. As the technology tends to mature, applications of blockchain [23,24] in various scenarios continue to emerge.…”

Section: Backgroundsmentioning

confidence: 99%

A Multipath Payment Scheme Supporting Proof of Payment

Qian

You

2022

Wireless Communications and Mobile Computing

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Blockchain technology has always been plagued by performance problems. Given this problem, many scaling schemes have been put forward. A layer 2 network is a technology that solves the performance problem of blockchain. Connected parties in this network can set up channels to send digital currency to each other. Since the interaction with the blockchain is only required when the channel is established and closed, a large number of transactions do not need to be recorded on the blockchain, thus reducing the blockchain capacity. Due to the special structure of the payment channel, the distribution of funds in the channel is often unbalanced, which limits the route payment to a certain extent. This paper improves the original payment method in the second layer network by introducing new scripts. The new payment scheme supports proof of payment which is integral to the nature of the lightning network and divides the payment into several subpayments, so the large payment can be divided into relatively small payments. Due to the capacity limitation of the payment channel, theoretically, the success rate of the micropayment route is higher. This paper tests the new payment scheme on the simulated network and validates the nature of this solution to have a high routing success rate while supporting proof of payment.

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“…If VM has newly arrived, then VM is rejected for scheduling (see lines [12][13][14][15]. If the size of the eligibleServers list is one, then the server is added to assignedServer list (see lines [17][18][19]. The list of assignedServer is used to store the optimal server on which VM can be scheduled.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…If A ′ does not have acceptable stability, then A ′ and A ′′ are two compromised solutions. The server which has the highest resource utilization is stored in the assignedServer list (see lines [16][17][18][19]. If the best alternative A ′ does not have an acceptable advantage, then there are several compromised solutions available.…”

Section: Proposed Algorithmmentioning

confidence: 99%

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Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

2021

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This paper presents a Power and Resource Utilization-Aware Virtual Machine Scheduling (PRUVMS) algorithm for strengthening resource utilization and diminishing the energy consumption of servers in the cloud environment. The PRUVMS algorithm enhances the resource utilization by migrating the VMs from the underloaded/overloaded servers to a normal server, and it reduces the energy consumption by shutting down the underloaded servers after migrating the VMs. For selecting the suitable server for the VM placement, the ranking of the available servers is evaluated. An illustrative example is presented to validate the PRUVMS algorithm. Further, the PRUVMS algorithm is tested on the PlanetLab workload using the Cloud-Sim simulator. The proposed PRUVMS algorithm improves resource utilization by 68.22% and 37.53% and decreases the energy consumption by 35.53% and 31.34% in comparison with PABFD and CAVMP algorithms, respectively. The improvement in computational results shows the acceptability of the proposed scheduling algorithm in the cloud environment.

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SPO: A Secure and Performance-aware Optimization for MapReduce Scheduling

Cited by 14 publications

References 41 publications

MapReduce scheduling algorithms in Hadoop: a systematic study

MapReduce scheduling algorithms in Hadoop: a systematic study

A Multipath Payment Scheme Supporting Proof of Payment

Optimal virtual machine scheduling in virtualized cloud environment using VIKOR method

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