Query Caching Technique Over Cloud-Based MapReduce System: A Survey

Vijay, Vandana; Nanda, Ruchi

doi:10.1007/978-981-15-6014-9_3

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Kalia and Gupta [2] evaluated Hadoop scheduling algorithms by focusing on factors such as locality and fairness. Vijay [8] proposed MapReduce with cache (MRC) to optimize job performance using in-memory caching, thereby reducing I/O costs and execution times. Maleki et al [9] presented TMaR, a two-stage map and reduce task scheduler, outperforming Hadoop-stock and Hadoop-A in terms of makespan and network traffic.…”

Section: Significance Of Studymentioning

confidence: 99%

Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads

Dhamee,

Chitrakar

2024

Preprint

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Section: Significance Of Studymentioning

confidence: 99%

Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads

Dhamee,

Chitrakar

2024

Preprint

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Crowd tracking and monitoring middleware via Map-Reduce

Gazis

Katsiri

2022

International Journal of Parallel, Emergent and Distributed Sys

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This paper presents the design, implementation, and operation of a novel distributed fault-tolerant middleware. It uses interconnected WSNs that implement the Map-Reduce paradigm, consisting of several low-cost and low-power mini-computers (Raspberry Pi). Specifically, we explain the steps for the development of a novice, fault-tolerant Map-Reduce algorithm which achieves high system availability, focusing on network connectivity. Finally, we showcase the use of the proposed system based on simulated data for crowd monitoring in a real case scenario, i.e., a historical building in Greece (M. Hatzidakis' residence). The technical novelty of this article lies in presenting a viable low-cost and low-power solution for crowd sensing without using complex and resource-intensive AI structures or image/video recognition techniques.

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Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

Seyyedsalehi

Khansari

2022

IEEE Access

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Big data and cloud computing are two advanced technologies that have overcome many computing and analytical challenges in recent years. With the rise in the applications of these technologies, the necessity of efficiency and optimization in the utilization of related resources has made sense. The procedure of locating virtual machines (VM) in physical machines (PM) affects the performance, speed, and costs of cloud computing services. VM placement in cloud computing is an NP-hard problem. Indeed, the problem is more complicated in big data tasks due to the need for transferring high volumes of traffic between VMs. This paper proposes a new approach for VM placement in a multi data center (DC) cloud environment. The aware genetic algorithm first fit (AGAFF) is a context-aware algorithm that distinguishes big data tasks with an input tag and uses a structure to minimize the traffic between MapReduce nodes. This multi-objective algorithm is based on the genetic algorithm, which is incorporated with the first fit methodology. The algorithm minimizes energy usage by minimizing the number of used servers, intra-DC traffic of big data tasks, and VMs' live migration while maximizing relevant usage of CPU and RAM in every server. Furthermore, it improves job execution time, especially in big data processing, and reduces service level agreement (SLA) violations. A comparison between the results of AGAFF and four other algorithms shows by about 61% energy consumption reduction on average on different scales and approves a decrease in the number of needed PMs, intra-DC traffic of big data processing, and the number of live migrations.

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Query Caching Technique Over Cloud-Based MapReduce System: A Survey

Cited by 3 publications

References 13 publications

Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads

Turbocharging Hadoop Fair Scheduler using Dynamic Job Grouping in Multi-Job Workloads

Crowd tracking and monitoring middleware via Map-Reduce

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

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