1.2k citations · 3.4k across the 50 of their papers we have counts for
23 papers · 1 filter
Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
Shuaijun Chen, Omid Tavallaie, Michael Henri Hambali +5
Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual…
Cloud-Native Computing: A Survey from the Perspective of Services
Shuiguang Deng, Hailiang Zhao, Binbin Huang +6
The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web a…
Scheduling Multi-Server Jobs with Sublinear Regrets via Online Learning
Hailiang Zhao, Shuiguang Deng, Zhengzhe Xiang +4
Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources t…
Multi-level Explanation of Deep Reinforcement Learning-based Scheduling
Shaojun Zhang, Chen Wang, Albert Zomaya
Dependency-aware job scheduling in the cluster is NP-hard. Recent work shows that Deep Reinforcement Learning (DRL) is capable of solving it. It is difficult for the administrator…
Learning to Schedule Multi-Server Jobs with Fluctuated Processing Speeds
Hailiang Zhao, Shuiguang Deng, Feiyi Chen +3
Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneousl…
Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs
Hailiang Zhao, Shuiguang Deng, Jianwei Yin +2
Multi-server jobs are imperative in modern computing clusters. A multi-server job has multiple task components and each of the task components is responsible for processing a speci…
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Jin Wang, Jia Hu, Geyong Min +2
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently…
IoTSim-Edge: A Simulation Framework for Modeling the Behaviour of IoT and Edge Computing Environments
Devki Nandan Jha, Khaled Alwasel, Areeb Alshoshan +9
This paper proposes a novel simulator IoTSim-Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure…
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…
Proofware: Proof of Useful Work Blockchain Consensus Protocol for Decentralized Applications
Zhongli Dong, Young Choon Lee, Albert Y. Zomaya
In a blockchain system, consensus protocol as an incentive and security mechanism, is to ensure the participants to build the block honestly and effectively. There are different co…
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…
Improving Raw Image Storage Efficiency by Exploiting Similarity
Binqi Zhang, Chen Wang, Bing Bing Zhou +1
To improve the temporal and spatial storage efficiency, researchers have intensively studied various techniques, including compression and deduplication. Through our evaluation, we…
Data-Intensive Workload Consolidation on Hadoop Distributed File System
Reza Moraveji, Javid Taheri, MohammadReza HosseinyFarahabady +2
Workload consolidation, sharing physical resources among multiple workloads, is a promising technique to save cost and energy in cluster computing systems. This paper highlights a…
Statistical Regression to Predict Total Cumulative CPU Usage of MapReduce Jobs
Nikzad Babaii Rizvandi, Javid Taheri, Reza Moraveji +1
Recently, businesses have started using MapReduce as a popular computation framework for processing large amount of data, such as spam detection, and different data mining tasks, i…
Pattern Matching for Self- Tuning of MapReduce Jobs
Nikzad Babaii Rizvandi, Javid Taheri, Albert Y. Zomaya
In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference da…
A Primarily Survey on Energy Efficiency in Cloud and Distributed Computing Systems
Nikzad Babaii Rizvandi, Albert Y. Zomaya
A survey of available techniques in hardware to reduce energy consumption
Network Load Analysis and Provisioning of MapReduce Applications
Nikzad Babaii Rizvandi, Javid Taheri, Reza Moraveji +1
In this paper, we study the dependency between configuration parameters and network load of fixed-size MapReduce applications in shuffle phase and then propose an analytical method…
On Modeling Dependency between MapReduce Configuration Parameters and Total Execution Time
Nikzad Babaii Rizvandi, Albert Y. Zomaya, Ali Javadzadeh Boloori +1
In this paper, we propose an analytical method to model the dependency between configuration parameters and total execution time of Map-Reduce applications. Our approach has three…
Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption
Nikzad Babaii Rizvandi, Albert Y. Zomaya, Young Choon Lee +2
In this chapter we focus on slack reclamation and propose a new slack reclamation technique, Multiple Frequency Selection DVFS (MFS-DVFS). The key idea is to execute each task with…
On Modelling and Prediction of Total CPU Usage for Applications in MapReduce Environments
Nikzad Babaii Rizvandi, Javid Taheri, Reza Moraveji +1
Recently, businesses have started using MapReduce as a popular computation framework for processing large amount of data, such as spam detection, and different data mining tasks, i…
Some Observations on Optimal Frequency Selection in DVFS-based Energy Consumption Minimization
Nikzad Babaii Rizvandi, Javid Taheri, Albert Y. Zomaya
In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware sched…
A Study on Using Uncertain Time Series Matching Algorithms in MapReduce Applications
Nikzad Babaii Rizvandi, Javid Taheri, Albert Y. Zomaya +1
In this paper, we study CPU utilization time patterns of several Map-Reduce applications. After extracting running patterns of several applications, the patterns with their statist…
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
Anton Beloglazov, Rajkumar Buyya, Young Choon Lee +1
Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains…