MapReduce in the Clouds for Science

Gunarathne, Thilina; Wu, Tak-Lon; Qiu, Judy; Fox, Geoffrey

doi:10.1109/cloudcom.2010.107

Cited by 120 publications

(60 citation statements)

References 9 publications

(11 reference statements)

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“…So load balancing works on the proper satisfaction of the users by the proper resource utilization. So an efficient load balancing technique will be the one which will minimize the resource consumption [10]. In a cloud computing hypervisor act as a virtual machine monitor which works for the proper resource management as (Virtual machine sharing) in a cloud network.…”

Section: Why Load Balancing In Cloud Computingmentioning

confidence: 99%

Virtual Machine Load Balancing: State-of-the-Art and its Research Challenges

Yadav¹,

Joshi²,

Verma³

2015

IJCA

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Cloud computing is the most demanding technologies in today's era due to the reduction of cost and complexity of applications, as a result number of users demanding more services of cloud. Thus, for overall cloud performance load balancing in the cloud is an important research area. Cloud handles large number of users as it is working on powerful datacenters. So for better efficiency it has to be featured with reliable load balancer for handling the load in the cloud. Many load balancing algorithms have been proposed for the better management of user request among cloud resources. This paper presents our research with a survey of various existing load balancing algorithms along with state of the art and its research challenges.

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Section: Why Load Balancing In Cloud Computingmentioning

confidence: 99%

Virtual Machine Load Balancing: State-of-the-Art and its Research Challenges

Yadav¹,

Joshi²,

Verma³

2015

IJCA

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“…However, these algorithms are more precise and can perform better at load balancing. Later in this article a brief explanation will be provided for static algorithms such as ant colony [9], CLDBM [10], enhanced Map-Reduce [11], VM-mapping [12] as well as dynamic algorithms such as INS [13], ESWLC [14] and DDFTP [15]. Finally these algorithms will be evaluated and compared.…”

Section: Related Workmentioning

confidence: 99%

EDLT: An Extended DLT to Enhance Load Balancing in Cloud Computing

Fereydooni¹,

Shamsi²,

Ghobaei‐Arani³

2014

IJCA

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In recent years, cloud computing has gained prominence and most companies are applying some kind of cloud computing technology in their company or part of it. Growing number of clients who are willing to use cloud services has made load balancing an eminent challenge in this field. A general approach to load balancing is the application of Divisible Load Theory (DLT). In DLT, the workload is divided among master systems. For their turn, master systems divide the load among their slave systems. This article presents an enhanced technique for load balancing based on DLT. Simulated results indicate that the expanded DLT reduces measurement/report time and shows an improved performance at a lower failure rate.

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“…At the end of Oct. 2013, DSS was used by thousands of Web applications to store/access various files, such as images, videos, logs, pages, and so on. DPS is similar to Amazon EMR (Elastic MapReduce) [42] and Microsoft Azure MapReduce [43]. DPS is designed for various companies and organizations to build data warehouses and execute offline data analytical jobs.…”

Section: A Overview Of Acloudmentioning

confidence: 99%

Towards realistic benchmarking for cloud file systems: Early experiences

Ren

Shi

Wan

2014

2014 IEEE International Symposium on Workload Characterization (IISWC)

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Abstract-Over the past few years, cloud file systems such as Google File System (GFS) and Hadoop Distributed File System (HDFS) have received a lot of research efforts to optimize their designs and implementations. A common issue for these efforts is performance benchmarking. Unfortunately, many system researchers and engineers face challenges on making a benchmark that reflects real-life workload cases, due to the complexity of cloud file systems and vagueness of I/O workload characteristics. They could easily make incorrect assumptions about their systems and workloads, leading to the benchmark results differing from the fact.As the preliminary step for designing a realistic benchmark, we make an effort to explore the characteristics of data and I/O workload in a production environment. We collected a two-week I/O workload trace from a 2,500-node production cluster, which is one of the largest cloud platforms in Asia. This cloud platform provides two public cloud services: data storage service (DSS) and data processing service (DPS). We analyze the commonalities and individualities between both cloud services in multiple perspectives, including the request arrival pattern, request size, data population and so on. Eight key observations are highlighted from the comprehensive study, including the arrival rate of requests follows a Lognormal distribution rather than a Poisson distribution, request arrival presents multiple periodicities, cloud file systems fit partly-open model rather than purely open model or closed model. Based on the comparative analysis results, we derive several interesting implications on guiding system researchers and engineers to build a realistic benchmark on their own systems. Finally, we discuss several open issues and challenges raised on benchmarking cloud file systems.

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MapReduce in the Clouds for Science

Cited by 120 publications

References 9 publications

Virtual Machine Load Balancing: State-of-the-Art and its Research Challenges

Virtual Machine Load Balancing: State-of-the-Art and its Research Challenges

EDLT: An Extended DLT to Enhance Load Balancing in Cloud Computing

Towards realistic benchmarking for cloud file systems: Early experiences

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