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Comments on “Privacy Aware Data Deduplication for Side Channel in Cloud Storage” IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-03-13 Xin Tang, Yudan Zhu, Mingjun Fu
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Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-03-12 Khlood Jastaniah, Ning Zhang, Mustafa A. Mustafa
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Evaluation of Application Layer DDoS Attack Effect in Cloud Native Applications IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-03-11 Kewei Wang, Changzhen Hu, Chun Shan
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Lightweight and Privacy-Preserving Dual Incentives for Mobile Crowdsensing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-03-05 Lin Wan, Zhiquan Liu, Yong Ma, Yudan Cheng, Yongdong Wu, Runchuan Li, Jianfeng Ma
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Context-aware Consensus Algorithm for Blockchain-empowered Federated Learning IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-03-05 Yao Zhao, Youyang Qu, Yong Xiang, Feifei Chen, Longxiang Gao
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Trustless Collaborative Cloud Federation IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-03-01 Bishakh Chandra Ghosh, Sandip Chakraborty
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Stackelberg-Game-Based Multi-User Multi-Task Offloading in Mobile Edge Computing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-28 Xinglin Zhang, Zhongling Wang, Fengsen Tian, Zheng Yang
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Partial Decode and Compare: An Efficient Verification Scheme for Coded Edge Computing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-28 Jin Wang, Wei Jiang, Jingya Zhou, Zhaobo Lu, Kejie Lu, Jianping Wang
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POLARIS: Accelerating Asynchronous Federated Learning with Client Selection IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-28 Yufei Kang, Baochun Li
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Personalized and Differential Privacy-Aware Video Stream Offloading in Mobile Edge Computing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-22 Ping Zhao, Ziyi Yang, Guanglin Zhang
In Mobile Edge Computing (MEC), the collaboration between end devices and servers guarantees the low-latency and high-accuracy video stream analysis. However, such paradigm of video stream offloading poses a serious threat to the location privacy and the usage pattern privacy of end devices. The existing works offer strict privacy guarantee for users, but they do not take the features of video stream
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Cyclic Matrix Coding to Mitigate ACK Blocking of MPTCP in Data Center Networks IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-16 Zhaoyi Li, Jiawei Huang, Shiqi Wang, Wenjun Lyu, Jianxin Wang
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Generic Construction: Cryptographic Reverse Firewalls for Public Key Encryption with Keyword Search in Cloud Storage IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-15 Yang Ming, Hang Liu, Chenhao Wang, Yi Zhao
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Achieving Low Latency for Multipath Transmission in RDMA Based Data Center Network IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-13 Zhaoyi Li, Jiawei Huang, Shiqi Wang, Jianxin Wang
Remote Direct Memory Access (RDMA) achieves ultra-low latency, high throughput and low CPU overhead in data center by implementing the transport logic in hardware network interface card (NIC). However, RDMA faces new challenges in the heterogeneous multipath environment as it is very sensitive to packet reordering. When some packets are blocked in slow paths, the other packets delivered through fast
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Enabling Multi-Layer Threat Analysis in Dynamic Cloud Environments IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-13 Salman Manzoor, Antonios Gouglidis, Matthew Bradbury, Neeraj Suri
Most Threat Analysis (TA) techniques analyze threats to targeted assets (e.g., components, services) by considering static interconnections among them. However, in dynamic environments, e.g., the Cloud, resources can instantiate, migrate across physical hosts, or decommission to provide rapid resource elasticity to its users. Existing TA techniques are not capable of addressing such requirements. Moreover
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A Cloud-Edge Collaboration Framework for Generating Process Digital Twin IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-06 Bingqing Shen, Han Yu, Pan Hu, Hongming Cai, Jingzhi Guo, Boyi Xu, Lihong Jiang
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Cloud-Assisted Laconic Private Set Intersection Cardinality IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-05 Axin Wu, Xiangjun Xin, Jianhao Zhu, Wei Liu, Chang Song, Guoteng Li
Laconic Private Set Intersection (LPSI) is a type of PSI protocols characterized by the requirement of only two-round interactions and by having a reused message in the first round that is independent of the set size. Recently, Aranha et al. (CCS’2022) proposed a LPSI protocol that utilizes the pairing-based accumulator. However, this protocol heavily relies on time-consuming bilinear pairing operations
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Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-05 Yazhou Yuan, Shicong Gao, Ziteng Zhang, Wenye Wang, Zhezhuang Xu, Zhixin Liu
With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded
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Integrating Bayesian Optimization and Machine Learning for the Optimal Configuration of Cloud Systems IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-02-01 Bruno Guindani, Danilo Ardagna, Alessandra Guglielmi, Roberto Rocco, Gianluca Palermo
Bayesian Optimization (BO) is an efficient method for finding optimal cloud configurations for several types of applications. On the other hand, Machine Learning (ML) can provide helpful knowledge about the application at hand thanks to its predicting capabilities. This work proposes a general approach based on BO, which integrates elements from ML techniques in multiple ways, to find an optimal configuration
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DRL-Based Contract Incentive for Wireless-Powered and UAV-Assisted Backscattering MEC System IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-31 Che Chen, Shimin Gong, Wenjie Zhang, Yifeng Zheng, Yeo Chai Kiat
Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this article, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim
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Efficient Verifiable Cloud-Assisted PSI Cardinality for Privacy-Preserving Contact Tracing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-30 Yafeng Chen, Axin Wu, Yuer Yang, Xiangjun Xin, Chang Song
Private set intersection cardinality (PSI-CA) allows two parties to learn the size of the intersection between two private sets without revealing other additional information, which is a promising technique to solve privacy concerns in contact tracing. Efficient PSI protocols typically use oblivious transfer, involving multiple rounds of interaction and leading to heavy local computation overheads
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Root Cause Analysis for Cloud-Native Applications IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-29 Bartosz Żurkowski, Krzysztof Zieliński
Root cause analysis (RCA) is a critical component in maintaining the reliability and performance of modern cloud applications. However, due to the inherent complexity of cloud environments, traditional RCA techniques become insufficient in supporting system administrators in daily incident response routines. This article presents an RCA solution specifically designed for cloud applications, capable
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Fault Tolerance Oriented SFC Optimization in SDN/NFV-Enabled Cloud Environment Based on Deep Reinforcement Learning IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-23 Jing Chen, Jia Chen, Kuo Guo, Renkun Hu, Tao Zou, Jun Zhu, Hongke Zhang, Jingjing Liu
In software defined network/network function virtualization (SDN/NFV)-enabled cloud environment, cloud services can be implemented as service function chains (SFCs), which consist of a series of ordered virtual network functions. However, due to fluctuations of cloud traffic and without knowledge of cloud computing network configuration, designing SFC optimization approach to obtain flexible cloud
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Alleviating Congestion via Switch Design for Fair Buffer Allocation in Datacenters IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-23 Ahmed M. Abdelmoniem, Brahim Bensaou
In data-centers, the composite origin and bursty nature of traffic, the small bandwidth-delay product and the tiny switch buffers lead to unusual congestion patterns that are not handled well by traditional end-to-end congestion control mechanisms such as those deployed in TCP. Existing works address the problem by modifying TCP to adapt it to the idiosyncrasies of data-centers. While this is feasible
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Locality-aware and Fault-tolerant Batching for Machine Learning on Distributed Datasets IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-09 Liu Liu, Zhijun Ding, Dazhao Cheng, Xiaobo Zhou
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Reversible Data Hiding in Shared Images With Separate Cover Image Reconstruction and Secret Extraction IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-09 Lizhi Xiong, Xiao Han, Ching-Nung Yang, Yun-Qing Shi
Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred
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BatOpt: Optimizing GPU-Based Deep Learning Inference Using Dynamic Batch Processing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-08 Deyu Zhang, Yunzhen Luo, Yaobo Wang, Xiaoyan Kui, Ju Ren
Deep learning (DL) has been applied in billions of mobile devices due to its astonishing performance in image, text, and audio processing. However, limited by the computing capability of mobile devices, a large amount of DL inference tasks need to be offloaded to edge or cloud servers, which makes powerful GPU servers are struggling to ensure the quality of service(QoS). To better utilize the highly
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psvCNN: A Zero-Knowledge CNN Prediction Integrity Verification Strategy IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2024-01-05 Yongkai Fan, Binyuan Xu, Linlin Zhang, Gang Tan, Shui Yu, Kuan-Ching Li, Albert Zomaya
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VSA-SD: A Service Discovery Method Based on Vector Symbol Architecture for Low-Cost IoT System Development IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-12-19 Haiming Chen, Lei Wang, Wei Qin, Xinyan Zhou, Li Cui
In recent years, with the widening applications of the Internet of Things (IoT), more and more perception services (e.g., air quality indicator services, road traffic congestion monitoring services, etc) with different arguments (e.g., data type, source location, creator, etc) will be deployed by dedicated IT infrastructure service providers for constructing customized IoT systems with low cost by
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Multi-Mode Instance-Intensive Workflow Task Batch Scheduling in Containerized Hybrid Cloud IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-12-19 An Liu, Ming Gao, Jiafu Tang
The migration of containerized microservices from virtual machines (VMs) to cloud data centers has become the most advanced deployment technique for large software applications in the cloud. This study investigates the scheduling of instance-intensive workflow (IWF) tasks to be executed in containers on a hybrid cloud when computational resources are limited. The process of scheduling these IWF tasks
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Pay-Per-Proof: Decentralized Outsourced Multi-User PoR for Cloud Storage Payment Using Blockchain IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-12-18 Hui Cui, Zhiguo Wan, Tianyu Zhaolu, Huaqun Wang, Atsuko Miyaji
Cloud computing has been widely applied in data storage, but cloud computing is not armed with an efficient integrity check mechanism for users to learn whether their large volumes of data have been kept intact by the cloud. The concept of proofs of retrievability (PoR) was introduced to address such an issue by enabling users to check the integrity of their data stored by the cloud. But PoR requires
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Efficient Approximation Algorithms for Scheduling Coflows With Total Weighted Completion Time in Identical Parallel Networks IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-12-08 Chi-Yeh Chen
This article addresses the scheduling problem of coflows in identical parallel networks, a well-known $\mathcal {NP}$ -hard problem. We consider both flow-level scheduling and coflow-level scheduling problems. In the flow-level scheduling problem, flows within a coflow can be transmitted through different network cores, while in the coflow-level scheduling problem, flows within a coflow must be transmitted
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Optimizing Cloud Data Lake Queries With a Balanced Coverage Plan IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-12-05 Grisha Weintraub, Ehud Gudes, Shlomi Dolev, Jeffrey D. Ullman
Cloud data lakes emerge as an inexpensive solution for storing very large amounts of data. The main idea is the separation of compute and storage layers. Thus, cheap cloud storage is used for storing the data, while compute engines are used for running analytics on this data in “on-demand” mode. However, to perform any computation on the data in this architecture, the data should be moved from the
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Integrated Computation Offloading, UAV Trajectory Control, Edge-Cloud and Radio Resource Allocation in SAGIN IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-12-05 Minh Dat Nguyen, Long Bao Le, André Girard
In this article, we study the computation offloading problem in hybrid edge-cloud based space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, user scheduling, edge-cloud computation, radio resource allocation, and admission control is performed. Specifically, the considered SAGIN employs multiple UAV-mounted
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A Publicly Verifiable Outsourcing Matrix Computation Scheme Based on Smart Contracts IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-11-30 Hao Wang, Chunpeng Ge, Lu Zhou, Zhe Liu, Dongwan Lan, Xiaozhen Lu, Danni Jiang
Matrix computation is a crucial mathematical tool in scientific fields such as Artificial Intelligence and Cryptographic computation. However, it is difficult for resource-limited devices to execute large-scale matrix computations independently. Outsourcing matrix computation (OMC) is a promising solution that engages a cloud server to process complicated matrix computations for resource-limited devices
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A Stochastic Approach for Scheduling AI Training Jobs in GPU-Based Systems IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-11-24 Federica Filippini, Jonatha Anselmi, Danilo Ardagna, Bruno Gaujal
In this work, we optimize the scheduling of Deep Learning (DL) training jobs from the perspective of a Cloud Service Provider running a data center, which efficiently selects resources for the execution of each job to minimize the average energy consumption while satisfying time constraints. To model the problem, we first develop a Mixed-Integer Non-Linear Programming formulation. Unfortunately, the
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Optimizing the Micro-Architectural Performance of the Current and Emerging Edge Infrastructure IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-11-17 Jianda Wang, Zhen Wang, Weili Wu, Yang Hu
The Network Function Virtualization (NFV) is the essential technology proposed to tackle the next-generation mobile system’s various flexibility features. In this article, we implement a thorough micro-architectural performance investigation on the NFV-enabled edge virtual Radio Access Network (vRAN) and the emerging 5G new-radio (nr) platform to unveil the main micro-architectural bottlenecks of the
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Leakage-Suppressed Encrypted Keyword Queries Over Multiple Cloud Servers IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-11-15 Yi Dou, Henry C. B. Chan
Searchable encryption is a technique that can support operations on encrypted data directly. However, searchable encryption is still vulnerable to attacks that exploit the leakages from encrypted query results. This article presents an effective multi-server searchable encryption scheme to prevent volume and access pattern leakages. To hide the volume leakage of a keyword, a new index construction
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Learning-Based Dynamic Memory Allocation Schemes for Apache Spark Data Processing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-11-10 Danlin Jia, Li Wang, Natalia Valencia, Janki Bhimani, Bo Sheng, Ningfang Mi
Apache Spark is an in-memory analytic framework that has been adopted in the industry and research fields. Two memory managers, Static and Unified, are available in Spark to allocate memory for caching Resilient Distributed Datasets (RDDs) and executing tasks. However, we find that the static memory manager (SMM) lacks flexibility, while the unified memory manager (UMM) puts heavy pressure on the garbage
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Improving LSM-Tree Based Key-Value Stores With Fine-Grained Compaction Mechanism IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-11-02 Hui Sun, Guanzhong Chen, Yinliang Yue, Xiao Qin
LSM-tree-based key-value stores (KV stores) render high-performance read/write services to data-intensive applications. KV stores employ an SSTable-based Coarse-Grained Compaction (CGC) mechanism, which involves a huge amount of data that do not need to be updated, thereby bringing a high write amplification (WA) and long tail latency. To address this issue, we propose a Fine-Grained Compaction (FGC)
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AoI-Aware Partial Computation Offloading in IIoT With Edge Computing: A Deep Reinforcement Learning Based Approach IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-10-30 Kai Peng, Peiyun Xiao, Shangguang Wang, Victor C. M. Leung
With the rapid growth of the Industrial Internet of Things, a large amount of industrial data that needs to be processed promptly. Edge computing-based computation offloading can well assist industrial devices to process these data and reduce the overall time overhead. However, there are dependencies among tasks and some tasks have high latency requirements, so completing computation offloading while
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Hardware-Assisted Static and Runtime Attestation for Cloud Deployments IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-10-24 Michał Kucab, Piotr Boryło, Piotr Chołda
This article is devoted to the problems of static and runtime integrity for cloud deployments. Existing remote attestation solutions for cloud infrastructure do not cover static and dynamic attestation as a whole. They evaluate either the static or dynamic part, not considering the rest. We address this gap by proposing a runtime attestation process based on hardware CET technology, as an enhancement
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Verifiable Cloud-Based Data Publish-Subscribe Service With Hidden Access Policy IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-10-23 Chunlin Li, Jinguo Li, Kai Zhang, Yan Yan, Jianting Ning
Cloud-based publish-subscribe (pub-sub) services provide a decoupling method for publishers and subscribers to effectively exchange targeted information and massive data on the cloud platform. Data publishers implement fine-grained access control to set subscription privileges for outsourced data through an access policy. However, in the context of semi-honest cloud platforms, the publisher's access
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Learning Scheduling Policies for Co-Located Workloads in Cloud Datacenters IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-26 Jialun Li, Danyang Xiao, Jieqian Yao, Yujie Long, Weigang Wu
Co-location, which deploys long running applications and batch-processing applications in the same computing cluster, has become a promising way to improve resource utility for large cloud datacenters. However, co-location brings huge challenges to task scheduling because different types of workloads may affect each other. Existing works on task scheduling rarely focus on the scenario of co-location
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A Complex Behavioral Interaction Analysis Method for Microservice Systems With Bounded Buffers IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-25 Shuo Wang, Zhijun Ding, Ru Yang, Changjun Jiang
The interaction process in microservice architectures is highly complex, making it very challenging to ensure correct behavioral interactions. The few related works focus only on the verification of interaction soundness in the case of a specific buffer k -value, without considering how to get the suitable buffer k -value. To solve the above problems, this article proposes a method to find the maximum
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MARS: A DRL-Based Multi-Task Resource Scheduling Framework for UAV With IRS-Assisted Mobile Edge Computing System IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-19 Feibo Jiang, Yubo Peng, Kezhi Wang, Li Dong, Kun Yang
This article studies a dynamic Mobile Edge Computing (MEC) system assisted by Unmanned Aerial Vehicles (UAVs) and Intelligent Reflective Surfaces (IRSs). We propose a scaleable resource scheduling algorithm to minimize the energy consumption of all UEs and UAVs in the MEC system with a variable number of UAVs. We propose a Multi-tAsk Resource Scheduling (MARS) framework based on Deep Reinforcement
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Multi-Objective Cloud Task Scheduling Optimization Based on Evolutionary Multi-Factor Algorithm IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-13 Zhihua Cui, Tianhao Zhao, Linjie Wu, A. K. Qin, Jianwei Li
Cloud platforms scheduling resources based on the demand of the tasks submitted by the users, is critical to the cloud provider's interest and customer satisfaction. In this paper, we propose a multi-objective cloud task scheduling algorithm based on an evolutionary multi-factorial optimization algorithm. First, we choose execution time, execution cost, and virtual machines load balancing as the objective
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Comment on “Multi-Keyword Searchable and Verifiable Attribute-Based Encryption Over Cloud Data” IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-07 Wan-Peng Guo, Run-Hua Shi, Xiao-Xu Zhang
In recent article Zhang, et al. 2023, the authors presented an efficient and verifiable multi-keyword attribute-based search scheme over cloud data. In this comment, we show that the key equation design in their scheme has errors, leading to the inability to perform effective searches in the case of multi-keyword. Then we propose a correction to address this issue without compromising the original
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Joint Task Offloading and Service Placement for Mobile Edge Computing: An Online Two-Timescale Approach IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-05 Xin Li, Xinglin Zhang, Tiansheng Huang
As a new computing paradigm, mobile edge computing (MEC) pushes the centralized cloud resources close to the edge network, which significantly reduces the pressure of the backbone network and meets the requirements of emerging mobile applications. To achieve high performance of the MEC system, it is essential to design efficient task offloading and service placement schemes, which are responsible for
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A Secure Client-Side Deduplication Scheme Based on Updatable Server-Aided Encryption IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-09-05 Guanxiong Ha, Chunfu Jia, Yuchen Chen, Hang Chen, Mingyue Li
The server-aided encryption is widely used in encrypted deduplication systems to protect against brute-force attacks. However, it is non-trivial to update the master key managed by the key server in existing schemes. Once the master key is leaked, all user data are vulnerable to offline brute-force attacks. In this article, we extend the server-aided encryption with the updatable encryption (UE) and
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An Energy-Efficient Tuning Method for Cloud Servers Combining DVFS and Parameter Optimization IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-08-28 Weiwei Lin, Xiaoxuan Luo, ChunKi Li, Jiechao Liang, Guokai Wu, Keqin Li
Emerging cloud computing applications place a growing demand on resources, leading to increasingly large data centers with significant energy consumption and carbon emissions. Various research conduct optimization methods to improve the energy efficiency of the server in the cloud data center. However, most existing optimization methods are designed for specific applications, thus making it difficult
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On a Meta Learning-Based Scheduler for Deep Learning Clusters IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-08-24 Jin Yang, Liang Bao, Wenjing Liu, Rong Yang, Chase Q. Wu
Deep learning (DL) has become a dominating type of workloads on AI computing platforms. The performance of such platforms highly depends on how distributed DL jobs are scheduled. Reinforcement learning (RL)-based schedulers have been extensively studied and are capable of modeling interferences between concurrent jobs competing for resources. However, existing RL-based schedulers must learn from large
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Privacy-Preserving and Forward Public Key Encryption With Field-Free Multi-Keyword Search for Cloud Encrypted Data IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-08-15 Yang Lu, Jiguo Li
With the excessive growth of data and the rapid development of cloud technology, cloud adoption is expanding rapidly nowadays. To achieve the purpose of privacy protection, the cloud data may be transmitted, stored and retrieved in enciphered form. Public key searchable encryption (PKSE) provides a feasible solution for efficient retrieval over enciphered data without decryption. However, traditional
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Multi-Tenant In-Memory Key-Value Cache Partitioning Using Efficient Random Sampling-Based LRU Model IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-08-02 Yuchen Wang, Junyao Yang, Zhenlin Wang
In-memory key-value caches are widely used as a performance-critical layer in web applications, disk-based storage, and distributed systems. The Least Recently Used (LRU) replacement policy has become the de facto standard in those systems since it exploits workload locality well. However, the LRU implementation can be costly due to the rigid data structure in maintaining object priority, as well as
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A Novel Web Attack Detection Mechanism Using Maximal-Munch With Torrent Deep Network IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-31 Seema Pillai, Vijayant Verma
A web attack is a harmful and deliberate attempt made by one person or group to gain access to another person's or group's data collection. Due to the incompatibility of the training algorithm for the Cross-Site Scripting (XSS) detection technique and the heterogeneity of attack load, the website was more frequently impacted by the detection of SQL injection attacks. Also, the language of the online
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DTFL: A Digital Twin-Assisted Graph Neural Network Approach for Service Function Chains Failure Localization IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-12 Kuo Guo, Jia Chen, Ping Dong, Tao Zou, Jun Zhu, Xu Huang, Shang Liu, Chenxi Liao
Cloud computing enables Network Function Virtualization to dynamically provide and deploy network functions (NFs) to meet business-specific requirements. This approach streamlines NFs’ lifecycle management and lowers the cost of Operation Administration and Maintenance. However, these advantages cause Service Function Chain (SFC) failure to grow in both scope and dimensionality, making it difficult
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CLUE: Systems Support for Knowledge Transfer in Collaborative Learning With Neural Nets IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-12 Harshit Daga, Yiwen Chen, Aastha Agrawal, Ada Gavrilovska
For highly distributed environments such as edge computing, collaborative learning approaches eschew the dependence on a global, shared model, in favor of models tailored for each location. Creating tailored models for individual learning contexts reduces the amount of data transfer, while collaboration among peers provides acceptable model performance. Collaboration assumes, however, the availability
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Secure Multi-Party Computation-Based Privacy-Preserving Authentication for Smart Cities IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-12 Victor Sucasas, Abdelrahaman Aly, Georgios Mantas, Jonathan Rodriguez, Najwa Aaraj
The increasing concern for identity confidentiality in the Smart City scenario has fostered research on privacy-preserving authentication based on pseudonymization. Pseudonym systems enable citizens to generate pseudo-identities and establish unlinkable anonymous accounts in cloud service providers. The citizen's identity is concealed, and his/her different anonymous accounts cannot be linked to each
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Queue-Aware Service Orchestration and Adaptive Parallel Traffic Scheduling Optimization in SDNFV-Enabled Cloud Computing IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-11 Jia Chen, Jing Chen, Kuo Guo
Owing to software defined network function virtualization (SDNFV), network services can be implemented as service function chains (SFCs) in SDNFV-enabled Cloud Computing. SFCs consist of a series of ordered virtual network functions (VNFs). Due to the dynamic of underlying network state and the unpredictability of network traffic, the traditional SFC orchestrating (SFCO) approaches based on centralized
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DCDPI: Dynamic and Continuous Deep Packet Inspection in Secure Outsourced Middleboxes IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-07 Minjun Deng, Kai Zhang, Pengfei Wu, Mi Wen, Jianting Ning
Secure outsourced middleboxes are deployed in network function virtualization services that detect malicious activities on communications, which provides privacy-preserving deep packet inspection (DPI) over encrypted traffic. To boost filtering efficiency of packets, the two-layer middlebox architecture has been adopted in recent DPI systems. Nevertheless, state-of-the-art solutions based on two-layer
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Self-Adaptive Gradient Quantization for Geo-Distributed Machine Learning Over Heterogeneous and Dynamic Networks IEEE Trans. Cloud Comput. (IF 6.5) Pub Date : 2023-07-05 Chenyu Fan, Xiaoning Zhang, Yangming Zhao, Yutao Liu, Shui Yu
Geo-Distributed Machine Learning (Geo-DML) has been proposed to collaborate geographically dispersed data centers (DCs) and train large scale machine learning (ML) models for various applications. While Geo-DML can achieve excellent performance, it also injects massive data traffic into the Wide Area Networks (WANs) in order to exchange gradients during model training process. Such a huge amount of