References of "Taheri, Javid"
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See detailReal-Time Virtual Network Function (VNF) Migration toward Low Network Latency in Cloud Environments
Cho, Daewoong; Taheri, Javid; Zomaya, Albert Y. et al

in IEEE 10th International Conference on Cloud Computing (CLOUD), 2017 (2017, June)

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers ... [more ▼]

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to minimize network latency in the rapidly changing network environments. Although many researchers have already studied in the field of Virtual Machine (VM) migration and Virtual Network Function (VNF) placement for efficient resource management in CDCs, VNF migration problem for low network latency among VNFs has not been studied yet to the best of our knowledge. To address this issue in this article, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 70.90% after latency-aware VNF migrations. [less ▲]

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See detailHopfield neural network for simultaneous job scheduling and data replication in grids
Taheri, Javid; Zomaya, Albert; Bouvry, Pascal UL et al

in Future Generation Computer Systems (2013), 29(8), 1885-1900

This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all ... [more ▼]

This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all jobs as well as the overall delivery time of all data files to their dependent jobs is concurrently minimized. JDS-HNN is inspired by a natural distribution of a variety of stones among different jars and utilizes a Hopfield Neural Network in one of its optimization stages to achieve its goals. The performance of JDS-HNN has been measured by using several benchmarks varying from medium- to very-large-sized systems. JDS-HNN’s results are compared against the performance of other algorithms to show its superiority under different working conditions. These results also provide invaluable insights into scheduling and replicating dependent jobs and data files as well as their performance related issues for various grid environments. [less ▲]

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