activity
20122024
most citedAI for Next Generation Computing: Emerging Trends and Future Directions

681 citations · 1.8k across the 26 of their papers we have counts for

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13 papers · 1 filter

cs.DC2024★ 95 cited

Characterising resource management performance in Kubernetes

Víctor Medel, Rafael Tolosana-Calasanz, José Ángel Bañares +2

A key challenge for supporting elastic behaviour in cloud systems is to achieve a good performance in automated (de-)provisioning and scheduling of computing resources. One of the…

cs.DC2022★ 260 cited

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

Sundas Iftikhar, Sukhpal Singh Gill, Chenghao Song +15

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature o…

cs.DC2022★ 681 cited

AI for Next Generation Computing: Emerging Trends and Future Directions

Sukhpal Singh Gill, Minxian Xu, Carlo Ottaviani +19

Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundament…

cs.DC2021★ 90 cited

HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing

Shreshth Tuli, Sukhpal Singh Gill, Minxian Xu +8

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive indu…

cs.DC2020

WattsApp: Power-Aware Container Scheduling

Hemant Mehta, Paul Harvey, Omer Rana +2

Containers are becoming a popular workload deployment mechanism in modern distributed systems. However, there are limited software-based methods (hardware-based methods are expensi…

cs.DC2020★ 56 cited

ThermoSim: Deep Learning based Framework for Modeling and Simulation of Thermal-aware Resource Management for Cloud Computing Environments

Sukhpal Singh Gill, Shreshth Tuli, Adel Nadjaran Toosi +8

Current cloud computing frameworks host millions of physical servers that utilize cloud computing resources in the form of different virtual machines (VM). Cloud Data Center (CDC)…

cs.DC2019★ 13 cited

Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey

Bin Qian, Jie Su, Zhenyu Wen +11

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly…

cs.DC2018★ 18 cited

Realizing Edge Marketplaces: Challenges and Opportunities

Blesson Varghese, Massimo Villari, Omer Rana +4

The edge of the network has the potential to host services for supporting a variety of user applications, ranging in complexity from data preprocessing, image and video rendering,…

cs.DC2018★ 375 cited

The Internet of Things, Fog and Cloud Continuum: Integration and Challenges

Luiz F. Bittencourt, Roger Immich, Rizos Sakellariou +7

The Internet of Things needs for computing power and storage are expected to remain on the rise in the next decade. Consequently, the amount of data generated by devices at the edg…

cs.DC2017★ 69 cited

A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade

Rajkumar Buyya, Satish Narayana Srirama, Giuliano Casale +22

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured…

cs.DC2017★ 17 cited

Managing Service-Heterogeneity using Osmotic Computing

Vishal Sharma, Kathiravan Srinivasan, Dushantha Nalin K. Jayakody +2

Computational resource provisioning that is closer to a user is becoming increasingly important, with a rise in the number of devices making continuous service requests and with th…

cs.DC2016★ 1 cited

Introducing Distributed Dynamic Data-intensive (D3) Science: Understanding Applications and Infrastructure

Shantenu Jha, Daniel S. Katz, Andre Luckow +3

A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growi…

cs.DC2012★ 3 cited

Survey and Analysis of Production Distributed Computing Infrastructures

Daniel S. Katz, Shantenu Jha, Manish Parashar +2

This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. Th…