Modelling of Communication in Unified Parallel and Distributed Computing Environment

In parallel and distributed computing environment such as "The Grid", anticipating the behavior of the resources and tasks based on certain scheduling algorithm is a great challenging. Thus, studying and improving these types of environments becomes very difficult. Out of this, the developers have spent remarkable efforts to come up with simulators which facilitate the studies in this domain. In addition, these simulators have a significant role in enhancing and proposing many scheduling algorithms, and this in turn has reflected efficiently on the Grid. In this paper, we will present some of these tools, which are: GridSim for large scales distributed computing and parallel environment, Alea for tackling dynamic scheduling problems, Sim-G-Batch grid simulator for simulating the security and energy concept and Balls simulator for evaluation peer-to-peer with integrated load balancing algorithm. Furthermore, this paper aims to guide and assist the researcher to choose the proper tool that can fit the studied research area, by providing functionality analysis for the reviewed simulators..

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Dynamic Power-Aware Scheduling Algorithms for Real-Time Task Sets with Fault-Tolerance in Parallel and Distributed Computing Environment

19th IEEE International Parallel and Distributed Processing Symposium ◽

10.1109/ipdps.2005.178 ◽

2005 ◽

Cited By ~ 2

Author(s):

Jian-Jun Han ◽

Qing-Hua Li

Keyword(s):

Fault Tolerance ◽

Distributed Computing ◽

Real Time ◽

Scheduling Algorithms ◽

Computing Environment ◽

Parallel And Distributed Computing ◽

Dynamic Power ◽

Time Task ◽

Task Sets ◽

Power Aware Scheduling

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Partitioning B+-tree in parallel and distributed computing environment.

10.22215/etd/1995-02952 ◽

1995 ◽

Author(s):

K. Wong

Keyword(s):

Distributed Computing ◽

Computing Environment ◽

Parallel And Distributed Computing

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Das OSF Distributed Computing Environment

10.1007/978-3-642-60731-8 ◽

1997 ◽

Cited By ~ 5

Author(s):

Alexander Schill

Keyword(s):

Distributed Computing ◽

Computing Environment

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IoT-enabled directed acyclic graph in spark cluster

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-020-00195-6 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Jahwan Koo ◽

Nawab Muhammad Faseeh Qureshi ◽

Isma Farah Siddiqui ◽

Asad Abbas ◽

Ali Kashif Bashir

Keyword(s):

Distributed Computing ◽

Real Time ◽

Directed Acyclic Graph ◽

Random Access ◽

Heterogeneous Data ◽

The Body ◽

Computing Environment ◽

Time Data ◽

Acyclic Graph ◽

Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

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