A dual-direction technique for fast file downloads with dynamic load balancing in the Cloud

Mohamed, Nader; Al-Jaroodi, Jameela; Eid, Abdulla

doi:10.1016/j.jnca.2013.01.006

Cited by 30 publications

(15 citation statements)

References 11 publications

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“…For example, in [2] the approach was introduced to parallelize file downloads (DDFTP) using multiple replicas of the file. In [11] the approach was further refined and adapted to suit the Cloud Computing paradigm and allow for efficient and load balanced parallel download of large data sets. Further, in [10] the same approach was further extended to also offer parallelization of certain types of computational problems (DDPar) using dual direction operations (DDOps).…”

Section: Background and Related Workmentioning

confidence: 99%

“…Other agents will continue working normally until another replica becomes free and is assigned to help another agent. Several heuristics can be used to optimize the selection of which agent to help first and how the work is distributed between the original agent and the newly assigned helping agent as explained in [11]. Based on this set up we can have multiple streams downloading the data from different locations which reduces the download time significantly and overcomes some of the problem that may be encountered on the networks in use.…”

Section: The Proposed Approachmentioning

confidence: 99%

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Dual direction big data download and analysis

Al-Jaroodi¹,

Mohamed

Eid

2014

SIGMETRICS Perform. Eval. Rev.

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The term Big Data was recently coined as the amount of generated and stored digital data has grown so rapidly that it has become very hard to store, manage and analyze without coming up with new techniques that can cope with such challenges. Finding innovative approaches to support big data analysis has become a priority as both the research community and the industry are trying to make use of these huge amounts of available data. In this paper we introduce a new approach to enhance the overall big data analysis performance. The approach calls for utilizing data set replication, parallel download, and parallel processing over multiple compute nodes. The main concept calls for simultaneously parallelizing the download of the data (in partitions) from multiple replicated sites to multiple compute nodes that will also perform the analysis in parallel. Then the results are given to the client that requested the analysis.

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Section: Background and Related Workmentioning

confidence: 99%

Section: The Proposed Approachmentioning

confidence: 99%

Dual direction big data download and analysis

Al-Jaroodi¹,

Mohamed

Eid

2014

SIGMETRICS Perform. Eval. Rev.

Self Cite

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“…Network traffic partitioning is a key to load balancing, and has been extensively applied in network applications, such as stateful firewalling [1], intrusion detection [2][3], traffic measurement [4], high-speed packet switching [5], and content delivery [6] [7]. In particular, emerging network paradigms such as software-sified into three types: direct hashing[14] [15], hash space division [18] and flow table based dynamic schemes [21] [22][23] [24].…”

Section: Introductionmentioning

confidence: 99%

Robust dynamic network traffic partitioning against malicious attacks

Yang

Zhao

et al. 2017

Journal of Network and Computer Applications

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The continual growth of network traffic rates results in heavy traffic processing overloads, and a typical solution is to partition traffic into multiple network processors for parallel processing especially in software-defined networks. This paper proposes an efficient and robust network traffic partitioning scheme called DTP-ICM, which dynamically distributes packet traffic with flow granularity, and assigns a new flow to a network processor with the lightest traffic load level. The scheme is first improved by building an initial connection separation mechanism to isolate false TCP connections, which results in the initial connection

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“…Also there are many studies about load balancing in distributed and peer to peer networks [19][20][21][22][23], but a little comprehensive research about load balancing in the field of cloud computing has been done yet and we just can refer to some papers that there are in the field of load balancing in cloud computing [24][25][26][27][28]. In this paper we present a survey of the algorithms, architectures and all techniques which have proposed for cloud computing load balancing.…”

Section: Introductionmentioning

confidence: 99%

Load Balancing in Cloud Computing: A State of the Art Survey

Mesbahi

Rahmani

2016

IJMECS

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Abstract-Cloud computing has proposed a new perspective for provisioning the large-scale computing resources by using virtualization technology and a payper-use cost model. Load balancing is taken into account as a vital part for parallel and distributed systems. It helps cloud computing systems by improving the general performance, better computing resources utilization, energy consumption management, enhancing the cloud services' QoS, avoiding SLA violation and maintaining system stability through distribution, controlling and managing the system workloads. In this paper we study the necessary requirements and considerations for designing and implementing a suitable load balancer for cloud environments. In addition we represent a complete survey of current proposed cloud load balancing solutions which according to our classification, they can be classified into three categories: General Algorithm-based, Architectural-based and Artificial Intelligence-based load balancing mechanisms. Finally, we propose our evaluation of these solutions based on suitable metrics and discuss their pros and cons.

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A dual-direction technique for fast file downloads with dynamic load balancing in the Cloud

Cited by 30 publications

References 11 publications

Dual direction big data download and analysis

Dual direction big data download and analysis

Robust dynamic network traffic partitioning against malicious attacks

Load Balancing in Cloud Computing: A State of the Art Survey

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