Junaid Shuja
I am an assistant professor at CUI, Abbottabad. My current research interest is the application of ML techniques for caching in edge networks. Other interests encompass topics like ARM emulation and SIMD instruction cross-platform execution, data center energy efficiency, renewable energy-based data centers, content caching in edge networks. I am looking to extend my research network and collaborations.
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virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious
characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential
global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical
measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and
socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for
diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligencebased
analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought
to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in
the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized
as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission
estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment
analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly
articles covering COVID-19.We survey and compare research works in these directions that are accompanied by open source
data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will
provide the scientific community with an initiative to start open source extensible and transparent research in the collective
fight against the COVID-19 pandemic.
comprehensive MCC framework. In this paper, we evaluate the overhead of the system and application virtualization techniques and emulation frameworks that enable MCC offloading mechanisms. We find that the overhead of system and application virtualization can be as high as 4.51% and 55.18% respectively for the SciMark benchmark. Moreover, ARM to Intel device emulation overhead can be as high as 55.53%. We provide a
proof of concept of emulation speedup by utilizing efficient Single Instruction, Multiple Data (SIMD) translations. We conclude that the overhead of virtualization and emulation techniques need to be reduced for efficient MCC offloading frameworks.
virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious
characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential
global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical
measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and
socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for
diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligencebased
analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought
to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in
the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized
as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission
estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment
analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly
articles covering COVID-19.We survey and compare research works in these directions that are accompanied by open source
data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will
provide the scientific community with an initiative to start open source extensible and transparent research in the collective
fight against the COVID-19 pandemic.
comprehensive MCC framework. In this paper, we evaluate the overhead of the system and application virtualization techniques and emulation frameworks that enable MCC offloading mechanisms. We find that the overhead of system and application virtualization can be as high as 4.51% and 55.18% respectively for the SciMark benchmark. Moreover, ARM to Intel device emulation overhead can be as high as 55.53%. We provide a
proof of concept of emulation speedup by utilizing efficient Single Instruction, Multiple Data (SIMD) translations. We conclude that the overhead of virtualization and emulation techniques need to be reduced for efficient MCC offloading frameworks.