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Farewell Editorial IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2020-03-06 Hui Lei
Presents the farewell editorial from the editor for this issue of the publication.
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A Carbon-Aware Incentive Mechanism for Greening Colocation Data Centers IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-27 Mohammad A. Islam; Hasan Mahmud; Shaolei Ren; Xiaorui Wang
The massive energy consumption of data centers worldwide has resulted in a large carbon footprint, raising serious concerns to sustainable IT initiatives and attracting a great amount of research attention. Nonetheless, the current efforts to date, despite encouraging, have been primarily centered around owner-operated data centers (e.g., Google data center), leaving out another major segment of data
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A Resource Usage Intensity Aware Load Balancing Method for Virtual Machine Migration in Cloud Datacenters IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-09 Haiying Shen; Liuhua Chen
To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety
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A Scalable Attribute-Based Access Control Scheme with Flexible Delegation cum Sharing of Access Privileges for Cloud Storage IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-09-12 Rohit Ahuja; Sraban Kumar Mohanty
Nowadays cloud servers have become the primary choice to store and share data with multiple users across the globe. The major challenge in sharing data using cloud servers is to protect data against untrusted cloud service provider and illegitimate users. Attribute-Based Encryption (ABE) has emerged as a useful cryptographic technique to securely share data with legitimate recipients in fine-grained
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Achieving Multi-Hop PRE via Branching Program IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-18 Zengpeng Li; Chunguang Ma; Ding Wang
Proxy re-encryption ( $\mathsf {PRE}$ PRE ) is a fundamental cryptographic primitive in secure data sharing and e-mail forwarding, etc. To our knowledge, most existing efficient lattice-based $\mathsf {PRE}$ PRE schemes focus on the construction of single-hop, key-private, multi-bit and chosen-ciphertext attack ( $\text{CCA}$ CCA ), etc. Few works of literature discussed the detailed multi-hop construction
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Amazon EC2 Spot Price Prediction Using Regression Random Forests IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-12-06 Veena Khandelwal; Anand Kishore Chaturvedi; Chandra Prakash Gupta
Spot instances were introduced by Amazon EC2 in December 2009 to sell its spare capacity through auction based market mechanism. Despite its extremely low prices, cloud spot market has low utilization. Spot pricing being dynamic, spot instances are prone to out-of bid failure. Bidding complexity is another reason why users today still fear using spot instances. This work aims to present Regression
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Bulk Savings for Bulk Transfers: Minimizing the Energy-Cost for Geo-Distributed Data Centers IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-14 Xingjian Lu; Fanxin Kong; Xue Liu; Jianwei Yin; Qiao Xiang; Huiqun Yu
With the fast proliferation of cloud computing, major cloud service providers, e.g., Amazon, Google, Facebook, etc., have been deploying more and more geographically distributed data centers to provide customers with better reliability and quality of services. A basic demand in such a geo-distributed data center system is to transfer bulk volumes of data from one data center to another. Geographic
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Delay-Sensitive Multicast in Inter-Datacenter WAN Using Compressive Latency Monitoring IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-11-07 Tracy Yingying Cheng; Xiaohua Jia
Multicast routing in inter-datacenter wide area networks is to compute a multicast tree from a source datacenter to multiple destination datacenters. Software-defined networking enables optimal delay-sensitive routing for multicast sessions in inter-datacenter WANs. Delay-sensitive routing requires the controller to obtain the up-to-date latency of every link. However, the communication overhead and
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Design and Implementation of an Overlay File System for Cloud-Assisted Mobile Apps IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-16 Nafize Rabbani Paiker; Jianchen Shan; Cristian Borcea; Narain Gehani; Reza Curtmola; Xiaoning Ding
With cloud assistance, mobile apps can offload their resource-demanding computation tasks to the cloud. This leads to a scenario where computation tasks in the same program run concurrently on both the mobile device and the cloud. An important challenge is to ensure that the tasks are able to access and share the files on both the mobile and the cloud in a manner that is efficient, consistent, and
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Disaster Recovery Layer for Distributed OpenStack Deployments IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-29 Luis Tomás; Panagiotis Kokkinos; Vasilis Anagnostopoulos; Oshrit Feder; Dimosthenis Kyriazis; Kalman Meth; Emmanouel Varvarigos; Theodora Varvarigou
We present the Disaster Recovery Layer (DRL) that enables OpenStack-managed datacenter workloads, Virtual Machines (VMs) and Volumes, to be protected and recovered in another datacenter, in case of a disaster. This work has been carried out in the context of the EU FP7 ORBIT project that develops technologies for enabling business continuity as a service. The DRL framework is based on a number of autonomous
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Efficient Decentralized Attribute Based Access Control for Mobile Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-09-19 Sourya Joyee De; Sushmita Ruj
Fine grained access control is a requirement for data stored in untrusted servers like clouds. Owing to the large volume of data, decentralized key management schemes are preferred over centralized ones. Often encryption and decryption are quite expensive and not practical when users access data from resource constrained devices. We propose a decentralized attribute based encryption (ABE) scheme with
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Efficient Proofs of Retrievability with Public Verifiability for Dynamic Cloud Storage IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-30 Binanda Sengupta; Sushmita Ruj
Cloud service providers offer various facilities to their clients. The clients with limited resources opt for some of these facilities. They can outsource their bulk data to the cloud server. The cloud server maintains these data in lieu of monetary benefits. However, a malicious cloud server might delete some of these data to save some space and offer this extra amount of storage to another client
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Fair Online Power Capping for Emergency Handling in Multi-Tenant Cloud Data Centers IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-12 Qihang Sun; Shaolei Ren; Chuan Wu
In view of the high capital expense for scaling up power capacity to meet the escalating demand, maximizing the utilization of built capacity has become a top priority for multi-tenant data center operators, where many cloud providers house their physical servers. The traditional power provisioning guarantees a high availability, but is very costly and results in a significant capacity under-utilization
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Google Users as Sequences: A Robust Hierarchical Cluster Analysis Study IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-25 Omar Arif Abdul-Rahman; Kento Aida
In this era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. By understanding user behavior in constructing applications and interacting with typical cloud infrastructures, cloud managers can develop better systems that improve the users’ experience. In this paper, we analyze a large dataset of a Google cluster to characterize
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Green Cloudlet Network: A Sustainable Platform for Mobile Cloud Computing IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-19 Xiang Sun; Nirwan Ansari
In the Green Cloudlet Network (GCN) architecture, each User Equipment (UE) is associated with an Avatar (a private virtual machine for executing its UE's offloaded tasks) in a cloudlet located at the network edge. In order to reduce the operational expenditure for maintaining the distributed cloudlets, each cloudlet is powered by green energy and uses on-grid power as a backup. Owing to the spatial
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Heterogeneous Job Allocation Scheduler for Hadoop MapReduce Using Dynamic Grouping Integrated Neighboring Search IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-09-04 Chi-Ting Chen; Ling-Ju Hung; Sun-Yuan Hsieh; Rajkumar Buyya; Albert Y. Zomaya
MapReduce is a crucial framework in the cloud computing architecture, and is implemented by Apache Hadoop and other cloud computing platforms. The resources required for executing jobs in a large data center vary according to the job types. In general, there are two types of jobs, CPU-bound and I/O-bound, which require different resources but run simultaneously in the same cluster. The default job
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How to Extract Image Features Based on Co-Occurrence Matrix Securely and Efficiently in Cloud Computing IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-11 Yanli Ren; Xinpeng Zhang; Guorui Feng; Zhenxing Qian; Fengyong Li
High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted
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ODDS: Optimizing Data-Locality Access for Scientific Data Analysis IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-09-20 Jun Wang; Dezhi Han; Jiangling Yin; Xiaobo Zhou; ChangJun Jiang
Whereas traditional scientific applications are computationally intensive, recent applications require more data-intensive analysis and visualization to extract knowledge from the explosive growth of scientific information and simulation data. As the computational power and size of compute clusters continue to increase, the I/O read rates and associated network for these data-intensive applications
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Optimizing Timeliness and Cost in Geo-Distributed Streaming Analytics IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-09-11 Benjamin Heintz; Abhishek Chandra; Ramesh K. Sitaraman
Rapid data streams are generated continuously from diverse sources including users, devices, and sensors located around the globe. This results in the need for efficient geo-distributed streaming analytics to extract timely information. A typical geo-distributed analytics service uses a hub-and-spoke model, comprising multiple edges connected by a wide-area-network (WAN) to a central data warehouse
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Predicted Affinity Based Virtual Machine Placement in Cloud Computing Environments IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-09 Xiong Fu; Chen Zhou
In cloud data centers, an appropriate Virtual Machine (VM) placement has become an effective method to improve the resource utilization and reduce the energy consumption. However, most current solutions regard the VM placement as a bin-packing problem and each VM is seen as a single object. None of them have taken the relationships between VMs into consideration, which supplies us with a kind of new
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Predicting Workflow Task Execution Time in the Cloud Using A Two-Stage Machine Learning Approach IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-21 Thanh-Phuong Pham; Juan J. Durillo; Thomas Fahringer
Many techniques such as scheduling and resource provisioning rely on performance prediction of workflow tasks for varying input data. However, such estimates are difficult to generate in the cloud. This paper introduces a novel two-stage machine learning approach for predicting workflow task execution times for varying input data in the cloud. In order to achieve high accuracy predictions, our approach
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Providing Performance Guarantees for Cloud-Deployed Applications IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-11-10 Anshul Gandhi; Parijat Dube; Alexei Karve; Andrzej Kochut; Li Zhang
Applications with a dynamic workload demand need access to a flexible infrastructure to meet performance guarantees and minimize resource costs. While cloud computing provides the elasticity to scale the infrastructure on demand, cloud service providers lack control and visibility of user space applications, making it difficult to accurately scale the infrastructure. Thus, the burden of scaling falls
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Scheduling Live Migration of Virtual Machines IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-09-19 Vincent Kherbache; Éric Madelaine; Fabien Hermenier
Every day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Although VM placement problems are carefully studied, the underlying migration schedulers rely on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations. We present mVM, a new and extensible migration scheduler. To provide schedules with
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TIMER-Cloud: Time-Sensitive VM Provisioning in Resource-Constrained Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-11-27 Rehana Begam; Wei Wang; Dakai Zhu
Resource management is a vital factor for better performance in cloud systems and many resource allocation algorithms have been studied. In this work, focusing on applications with timing constraints (i.e., deadlines) running on resource-constrained clouds that have multiple heterogeneous nodes of computing resources (e.g., CPU cores and memory), we propose TIMER-Cloud , a time-sensitive resource allocation
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Toward Practical Privacy-Preserving Frequent Itemset Mining on Encrypted Cloud Data IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-14 Shuo Qiu; Boyang Wang; Ming Li; Jiqiang Liu; Yanfeng Shi
Frequent itemset mining, which is the essential operation in association rule mining, is one of the most widely used data mining techniques on massive datasets nowadays. With the dramatic increase on the scale of datasets collected and stored with cloud services in recent years, it is promising to carry this computation-intensive mining process in the cloud. Amount of work also transferred the approximate
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2019 Index IEEE Transactions on Cloud Computing Vol. 7 IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2020-03-06
Presents the 2019 subject/author index for this publication.
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Comments on “Secure Data Sharing in Cloud Computing Using Revocable-Storage Identity-Based Encryption” IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2020-02-13 Kwangsu Lee
Cloud computing can provide a flexible way to effectively share data among multiple users since it can overcome the time and location constraints of computing resource usage. However, the users of cloud computing are still reluctant to share sensitive data to a cloud server since the cloud server should be treated as an untrusted entity. In order to support secure and efficient data sharing in a cloud
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A Logic-Based Benders Decomposition Approach for the VNF Assignment Problem IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-02 Sara Ayoubi; Samir Sebbah; Chadi Assi
Middleboxes have gained popularity due to the significant value-added services these network elements provide to traffic flows, in terms of enhanced performance and security. Policy-aware traffic flows usually need to traverse multiple middleboxes in a predefined order to satisfy their associated policy, also known as Service Function Chaining . Typically, Middleboxes run on specialized hardware, which
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An Adaptive and Fuzzy Resource Management Approach in Cloud Computing IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-03 Parinaz Haratian; Faramarz Safi-Esfahani; Leili Salimian; Akbar Nabiollahi
Resource management plays a key role in the cloud-computing environment in which applications face with dynamically changing workloads. However, such dynamic and unpredictable workloads can lead to performance degradation of applications, especially when demands for resources are increased. To meet Quality of Service (QoS) requirements based on Service Level Agreements (SLA), resource management strategies
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Application-Aware Big Data Deduplication in Cloud Environment IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-05-31 Yinjin Fu; Nong Xiao; Hong Jiang; Guyu Hu; Weiwei Chen
Deduplication has become a widely deployed technology in cloud data centers to improve IT resources efficiency. However, traditional techniques face a great challenge in big data deduplication to strike a sensible tradeoff between the conflicting goals of scalable deduplication throughput and high duplicate elimination ratio. We propose AppDedupe , an application-aware scalable inline distributed deduplication
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Cloud Resource Management for Analyzing Big Real-Time Visual Data from Network Cameras IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-27 Ahmed S. Kaseb; Anup Mohan; Youngsol Koh; Yung-Hsiang Lu
Thousands of network cameras stream real-time visual data for different environments, such as streets, shopping malls, and natural scenes. The big visual data from these cameras can be useful for many applications, but analyzing the large quantities of data requires significant amounts of resources. These resources can be obtained from cloud vendors offering cloud instances (referred to as instances
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Cloudy Knapsack Algorithm for Offloading Tasks from Large Scale Distributed Applications IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-07-04 Harisankar Haridas; Sriram Kailasam; Janakiram Dharanipragada
Offloading of tasks to the cloud is one of the approaches to improve the performance of distributed applications. When monetary constraints are present, selection of the tasks to be offloaded becomes important in order to ensure efficient use of the available cloud resources. This becomes a challenge for large scale distributed applications as the decisions on offloading have to be made locally at
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Dynamic Resource Provisioning for Energy Efficient Cloud Radio Access Networks IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-15 Nuo Yu; Zhaohui Song; Hongwei Du; Hejiao Huang; Xiaohua Jia
Energy saving is critical for the cloud radio access networks (C-RANs), which are composed by massive radio access units (RAUs) and energy-intensive computing units (CUs) that host numerous virtual machines (VMs). We attempt to minimize the energy consumption of C-RANs, by leveraging the RAU sleep scheduling and VM consolidation strategies. We formulate the energy saving problem in C-RANs as a joint
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Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-01 Muhammad Usman; Xiangjian He; Kin-Man Lam; Min Xu; Syed Mohsin Matloob Bokhari; Jinjun Chen; Mian Ahmad Jan
The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and
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Facilitating Secure and Efficient Spatial Query Processing on the Cloud IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-07-11 Ayesha M. Talha; Ibrahim Kamel; Zaher Al Aghbari
Database outsourcing is a common cloud computing paradigm that allows data owners to take advantage of its on-demand storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties i.e., cloud service provider, as well as providing relevant query results in real-time to authenticated users. Existing approaches either compromise confidentiality
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Fast Phrase Search for Encrypted Cloud Storage IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-05-29 Hoi Ting Poon; Ali Miri
Cloud computing has generated much interest in the research community in recent years for its many advantages, but has also raise security and privacy concerns. The storage and access of confidential documents have been identified as one of the central problems in the area. In particular, many researchers investigated solutions to search over encrypted documents stored on remote cloud servers. While
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Fault Tolerant Stencil Computation on Cloud-Based GPU Spot Instances IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-05-31 Jun Zhou; Yan Zhang; Weng-Fai Wong
This paper describes a fault tolerant framework for distributed stencil computation on cloud-based GPU clusters. It uses pipelining to overlap the data movement with computation in the halo region as well as parallelises data movement within the GPUs. Instead of running stencil codes on traditional clusters and supercomputers, the computation is performed on the Amazon Web Service GPU cloud, and utilizes
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Game-Theory Based Power and Spectrum Virtualization for Optimizing Spectrum Efficiency in Mobile Cloud-Computing Wireless Networks IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-07-13 Xi Zhang; Qixuan Zhu
Mobile cloud-computing is a wireless network environment that focuses on sharing the publicly available wireless resources. Wireless network virtualization provides an efficient technique to implement the mobile cloud-computing by enabling multiple virtual wireless networks to be mapped onto one physical substrate wireless network. One of the most important challenges of this technique lies in how
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Hierarchical Stochastic Models for Performance, Availability, and Power Consumption Analysis of IaaS Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-10-09 Ehsan Ataie; Reza Entezari-Maleki; Leila Rashidi; Kishor S. Trivedi; Danilo Ardagna; Ali Movaghar
Infrastructure as a Service (IaaS) is one of the most significant and fastest growing fields in cloud computing. To efficiently use the resources of an IaaS cloud, several important factors such as performance, availability, and power consumption need to be considered and evaluated carefully. Evaluation of these metrics is essential for cost-benefit prediction and quantification of different strategies
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Learn to Play Maximum Revenue Auction IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-05 Xiaotie Deng; Tao Xiao; Keyu Zhu
Auctions for allocating resources and determining prices have become widely applied for services over the Internet, Cloud Computing, and Internet of Things in recent years. Very often, such auctions are conducted multiple times. They may be expected to gradually reveal participants’ true value distributions, with which, it eventually would result in a possibility to fully apply the celebrated Myerson's
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Robust Performance-Based Resource Provisioning Using a Steady-State Model for Multi-Objective Stochastic Programming IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2016-09-12 Kyle M. Tarplee; Anthony A. Maciejewski; Howard Jay Siegel
Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resources to fulfill their high-performance computing needs. With cloud computing it is possible to tailor your computing infrastructure to perform best for your particular type of workload by
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Service Chaining for Hybrid Network Function IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-29 Huawei Huang; Song Guo; Jinsong Wu; Jie Li
In the Service-Function-Chaining (SFC) enabled networks, various sophisticated policy-aware network functions, such as intrusion detection, access control and unified threat management, can be realized in either physical middleboxes or virtualized network function (VNF) appliances. In this paper, we study the service chaining towards the hybrid SFC clouds, where both physical appliances and VNF appliances
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Shaving Data Center Power Demand Peaks Through Energy Storage and Workload Shifting Control IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-08-25 Mehiar Dabbagh; Bechir Hamdaoui; Ammar Rayes; Mohsen Guizani
This paper proposes efficient strategies that shave Data Centers (DCs)’ monthly peak power demand with the aim of reducing the DCs’ monthly expenses. Specifically, the proposed strategies allow to decide: $i)$i) when and how much of the DC's workload should be delayed given that the workload is made up of multiple classes where each class has a certain delay tolerance and delay cost, and $ii)$ii) when
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Stackelberg Game for Energy-Aware Resource Allocation to Sustain Data Centers Using RES IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-15 Gagangeet Singh Aujla; Mukesh Singh; Neeraj Kumar; Albert Y. Zomaya
Smart Grid (SG) has emerged as one of the most powerful technologies of the modern era for an efficient energy management by integrating information and communication technologies (ICT) in the existing infrastructure. Among various ICT, cloud computing (CC) has emerged as one of the leading service providers which uses geo-distributed data centers (DCs) to serve the requests of users in SG. In recent
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Towards Declarative and Data-Centric Virtual Machine Image Management in IaaS Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-07-17 Haikun Liu; Bingsheng He; Xiaofei Liao; Hai Jin
Virtual machine image (VMI) management has become one of the key infrastructure components in Infrastructure as a Service (IaaS) cloud systems. Any “good” VMI management system should support flexible and efficient VMI services to cloud users, and offer scalable, easy-to-maintain and efficient VMI management for cloud providers. While there have been a number of systems and optimizations for VMI management
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Two-Aggregator Topology Optimization Using Multiple Paths in Data Center Networks IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-06 Soham Das; Sartaj Sahni
In this paper we focus on the problem of data aggregation using two aggregators in a data center network, where the source racks are allowed to split their data and send to the aggregators using multiple paths. We show that the problem of finding a topology that minimizes aggregation time is NP-hard for k = 2, 3, 4, where k is the maximum degree of each ToR switch (number of uplinks in a top-of-rack
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Video Stream Analysis in Clouds: An Object Detection and Classification Framework for High Performance Video Analytics IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2016-01-13 Ashiq Anjum; Tariq Abdullah; M. Fahim Tariq; Yusuf Baltaci; Nick Antonopoulos
Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers an
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Virtual Machine Migration Planning in Software-Defined Networks IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-05-31 Huandong Wang; Yong Li; Ying Zhang; Depeng Jin
Live migration is a key technique for virtual machine (VM) management in data center networks, which enables flexibility in resource optimization, fault tolerance, and load balancing. Despite its usefulness, the live migration still introduces performance degradations during the migration process. Thus, there has been continuous efforts in reducing the migration time in order to minimize the impact
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When I/O Interrupt Becomes System Bottleneck: Efficiency and Scalability Enhancement for SR-IOV Network Virtualization IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2017-06-06 Jian Li; Shuai Xue; Wang Zhang; Ruhui Ma; Zhengwei Qi; Haibing Guan
High performance networking interface cards (NIC) have become essential networking devices in commercial cloud computing environments. Therefore, efficient and scalable I/O virtualization is one of the primary challenges on virtualized cloud computing platforms. Single Root I/O Virtualization (SR-IOV) is a network interface technology that eliminates the overhead of redundant data copies and the virtual
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2019 Reviewers List* IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2019-12-04
Presents the list of reviewers who contributed to this publication in 2019.
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On the Privacy of Matrix Masking-Based Verifiable (Outsourced) Computation IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2019-06-12 Liang Zhao; Liqun Chen
Privacy-preserving verifiable (outsourced) computation (PPVC) is a useful technique for a resource-constrained client to outsource computationally heavy but sensitive tasks to a computationally powerful but untrusted worker and to obtain expected correct results from the worker. In this paper, we analyze the privacy property of three matrix masking-based PPVC protocols, which have recently been published
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A Tight Estimate of Job Completion Time in Vehicular Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-05-08 Ryan Florin; Puya Ghazizadeh; Aida Ghazi Zadeh; Ravi Mukkamala; Stephan Olariu
Inspired by the success of conventional cloud services and by the reality of present-day vehicles endowed with powerful on-board computers that can act as servers in a datacenter, researchers have recently introduced the concept of a vehicular cloud . Our main contribution is to offer a tight theoretical analysis of the expected job completion time in vehicular clouds characterized by short vehicular
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Energy Efficient Scheduling of Servers with Multi-Sleep Modes for Cloud Data Center IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-05-08 Chonglin Gu; Zhenlong Li; Hejiao Huang; Xiaohua Jia
In a cloud data center, servers are always over-provisioned in an active state to meet the peak demand of requests, wasting a large amount of energy as a result. One of the options to reduce the power consumption of data centers is to reduce the number of idle servers, or to switch idle servers into low-power sleep states. However, the servers cannot process the requests immediately when transiting
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Online VM Auto-Scaling Algorithms for Application Hosting in a Cloud IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-27 Yang Guo; Alexander L. Stolyar; Anwar Walid
We consider the auto-scaling problem for application hosting in a cloud, where applications are elastic and the number of requests changes over time. The application requests are serviced by Virtual Machines (VMs), which reside on Physical Machines (PMs) in a cloud. We aim to minimize the number of hosting PMs by intelligently packing VMs into PMs, while the VMs are auto-scaled, i.e., dynamically acquired
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Distributed Resource Allocation for Data Center Networks: A Hierarchical Game Approach IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-24 Huaqing Zhang; Yong Xiao; Shengrong Bu; F. Richard Yu; Dusit Niyato; Zhu Han
The increasing demand of data computing and storage for cloud-based services motivates the development and deployment of large-scale data centers. This paper studies the resource allocation problem for the data center networking system when multiple data center operators (DCOs) simultaneously serve multiple service subscribers (SSs). We formulate a hierarchical game to analyze this system where the
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A Power-of-Two Choices Based Algorithm for Fog Computing IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-20 Roberto Beraldi; Hussein Alnuweiri; Abderrahmen Mtibaa
The fog computing paradigm brings together storage, communication, and computation resources closer to users’ end-devices. Therefore, fog servers are deployed at the edge of the network, offering low latency access to users. With the expansion of such fog computing services, different providers will be able to deploy multiple resources within a restricted geographical proximity. In this paper, we investigate
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virtFlow: Guest Independent Execution Flow Analysis Across Virtualized Environments IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-20 Hani Nemati; Michel Dagenais
An agent-less technique to understand virtual machines (VMs) behavior and their changes during the VM life-cycle is essential for many performance analysis and debugging tasks in the cloud environment. Because of privacy and security issues, ease of deployment and execution overhead, the method preferably limits its data collection to the physical host level, without internal access to the VMs. We
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VMGuard: A VMI-Based Security Architecture for Intrusion Detection in Cloud Environment IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-20 Preeti Mishra; Vijay Varadharajan; Emmanuel S. Pilli; Uday Tupakula
Cloud security is of paramount importance in the new era of computing. Advanced malware can hide their behavior on detection of the presence of a security tool at a tenant virtual machine (TVM). Hence, TVM-layer security solutions are not reliable. In this paper, we propose a Virtual Machine Introspection (VMI) based security architecture design for fine granular monitoring of the virtual machines
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A Robust Formulation for Efficient Application Offloading to Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-17 José Barrameda; Nancy Samaan
Application offloading to clouds is the key enabler for compute-intensive applications running on mobile devices. An offloading algorithm employs estimated averages of the execution and communication costs of application modules to decide on a modules subset to be offloaded with the objective of minimizing a certain metric (e.g., execution time or energy). This decision is highly affected by the inherent
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A Planning Approach for Reassigning Virtual Machines in IaaS Clouds IEEE Trans. Cloud Comput. (IF 4.714) Pub Date : 2018-04-13 Yacine Laalaoui; Jehad Al-Omari
Reassignment of virtual machines into clusters is an important task for the good management of cloud resources since it decisively affects the performance of the Service Provider platform. Thus, for a successful reassignment, a clear and careful reassignment plan should be constructed in advance. In this paper, we propose a planning approach to the problem of reassigning virtual machines in IaaS Cloud
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