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LCRC: A Dependency-aware Cache Management Policy for Spark 16

LCRC: A Dependency-aware Cache Management Policy for Spark

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Wang, Bo Tang, Jie Zhang, Rui Ding, Wei Qi, Deyu Beijing Inst Technol Univ Sch Comp Sci & Technol Beijing Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou Guangdong Peoples R China Yanan Univ Coll Educ Sci Yanan Peoples R China Henan Univ Technol Coll Informat Sci & Engn Zhengzhou Henan Peoples R China

ISBN: (纸本)9781728111414

Memory is a constrained resource for in-memory big data computing systems. Efficient memory management plays a pivotal role in performance improvement for these systems. However, simple history-based cache replacement strategies, such as Least Recently Used (LRU), usually have poor performance when applied in cluster applications, due to their lack of data dependency knowledge. Even though Least Reference Count (LRC) can be aware of dependency by giving blocks with bigger reference count high priority to reside in memory. However, these blocks will not be accessed in some stages during their entire life cycle, leading to available memory deterioration. To eliminate this shortcoming, we propose LCRC, a dependency-aware cache management policy that considers both intra-stage and inter-stage dependency. By providing a prefetching mechanism, we can rewrite these inter-stages accessed blocks into memory before its next use. Experiments show that compared with previous methods the proposed mechanism can improve computing performance over 65%.

关键词： Big data In-memory computing framework Cache replacement Prefetching

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Parallel Model Checking on Pushdown Systems 16

Parallel Model Checking on Pushdown Systems

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Wei, Hansheng Chen, Xinyu Ye, Xin Fu, Neng Huang, Yanhong Shi, Jianqi East China Normal Univ Natl Trusted Embedded Software Engn Technol Res C Shanghai Peoples R China Hardware Software Codesign Technol & Applicat Eng Shanghai Peoples R China Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China LIPN Paris France Paris Univ 7 Paris France

ISBN: (纸本)9781728111414

Pushdown systems (PDSs) have been widely used in program verification, such as recursive programs, malware detection, and other model-checking problems. As programs become more complicated, the model-checking process of PDSs represents a real challenge, which is the well-known state explosion problem. this problem may be cost expensive both in terms of memory and time. Parallel computing has been introduced to overcome this limitation, especially GPU computing. In this paper, we propose novel parallel algorithms to accelerate model checking on PDSs according to the characteristics of automata-theoretic. To represent the model-checking process on parallel architectures, we propose two new models: multi-threaded P-automaton and multi-threaded Buchi pushdown systems. Moreover, we design a highly efficient data structure for PDSs and dynamic task management to accommodate the GPU. Compared with Moped, a popular model checker for PDSs, our approach achieves promising performance.

关键词： CUDA GPU Model Checking Parallel computing Pushdown Systems

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Importance of User's Profile Attributes in Identity Matching Across Multiple Online Social networking Sites 8

Importance of User's Profile Attributes in Identity Matching...

引用

8th international conference Confluence on Cloud computing, Data Science and Engineering, Confluence 2018

作者： Kumar Srivastava, Deepesh Roychoudhury, Basav Vardhan Samalia, Harsh Indian Institute of Management Shillong India

ISBN: (纸本)9781538617182

this paper is an attempt to explore the importance of user's profile attributes that can make a significant impact on identity resolution techniques across multiple online social networks. In this study, we proposed a method that is helpful to find out the most significant profile attributes in identity matching schemes. For example, a unique identity number (like a mobile number) can be significant attributes in identity matching, but most of the user's profiles don't disclose this kind of information because of privacy issues. So there is an opportunity for other profile attributes to be significant because of easily accessible on social networks from identity matching point of view. this method will be helpful in increasing the accuracy of the identity matching frameworks. © 2018 IEEE.

关键词： User profile

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Power and Cost-aware Virtual Machine Placement in Geo-distributed Data Centers 8

Power and Cost-aware Virtual Machine Placement in Geo-distri...

引用

8th international conference on Cloud computing and Services Science (CLOSER)

作者： Rawas, Soha Zekri, Ahmed El Zaart, Ali Beirut Arab Univ Dept Math & Comp Sci Beirut Lebanon

ISBN: (纸本)9789897582950

the proliferation of cloud computing due to its attracting on-demand services leads to the establishment of geo-distributed data centers (DCs) with thousands of computing and storage nodes. Consequently, many challenges exist for cloud providers to run such an environment. One important challenge is to minimize cloud users' network latency while accessing services from the DCs. the other is to decrease the DCs' energy consumption that contributes to high operational cost rates, low profits for cloud providers, and high carbon non-environment friendly emissions. In this paper, we studied the problem of virtual machine placement that results in less energy consumption, less CO2 emission, and less access latency towards large-scale cloud providers operational cost minimization. the problem was formulated as multi-objective function and an intelligent machine-learning model constructed to improve the performance of the proposed model. To evaluate the proposed model, extensive simulation is conducted using the CloudSim simulator. the simulation results reveal the effectiveness of PCVM model compared to other competing virtual machine placement methods in terms of network latency, energy consumption, CO2 emission and operational cost minimization.

关键词： Carbon Footprint Energy-efficient Latency Geo-distributed Data Centres

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Reverse Engineering Technique (RET) to Predict Resource Allocation in a Google Cloud System 8

Reverse Engineering Technique (RET) to Predict Resource Allo...

引用

8th international conference Confluence on Cloud computing, Data Science and Engineering, Confluence 2018

作者： Ray, Biplob R. Chowdhury, Sujan Cairns Australia

ISBN: (纸本)9781538617182

this paper presents a reverse engineering machine learning technique for resource allocation in cloud computing system. Efficient and timely resource allocation is a crucial task for complex operations in a large scale distributed system like cloud computing. Furthermore, to support Service Level Agreement (SLA) like priority, latency, and efficiency, the resource provisioning should be highly influenced by SLA requirements of the system. therefore in this paper, we propose the Reverse Engineering Technique (RET) which highly influence by priority to improve resource allocation accuracy. the paper used neural network based deep learning and Levenberg-Marquardt training algorithm for resource allocation prediction. the dataset of Google cloud computing system, which is publicly available dataset for research, is used to test the proposed RET. Our experiment shows that the proposed technique improves resource provisioning accuracy for cloud based systems. © 2018 IEEE.

关键词： Cloud computing

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Fully distributed consensus in Multi Agent Systems in the presence of disturbances 8

Fully distributed consensus in Multi Agent Systems in the pr...

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8th international conference on Advances in computing & Communications (ICACC)

作者： Pary, Najla Jacob, Jeevamma Natl Inst Technol Dept Elect Engn Calicut 673601 Kerala India

Consensus analysis in Multi Agent Systems (MAS) has been a topic of utmost interest among researchers due to its applications in diverse fields such as rescue and surveillance, mobile sensor networks and co-operative control of unmanned aerial vehicles. Consensus in MAS with a fixed directed network topology and consisting of agents with double integrator dynamics using static state feedback control, when the agents are subjected to external disturbances, is investigated in this paper. the two scenarios considered includes semi distributed leaderless consensus using the knowledge of information about the entire network topology and fully distributed adaptive consensus where the agents follows a leader, when global information is not available. the proposed Linear Matrix Inequality (LMI) based protocols simulated using MATLAB in MAS with double integrator agents are found to be effective in driving the agents towards consensus with a guaranteed Hoo disturbance rejection performance. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： Consensus adaptive distributed control H infinity performance Linear Matrix Inequalities Double Integrator agents

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Delay-Aware Secure Computation Offloading Mechanism in a Fog-Cloud Framework 16

Delay-Aware Secure Computation Offloading Mechanism in a Fog...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Yang, Yang Chang, Xiaolin Han, Zhen Li, Lin Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Key Lab Secur & Privacy Intelligent Trans Beijing Peoples R China

ISBN: (纸本)9781728111414

Fog-Cloud framework is being regarded as a more promising technology to provide performance guarantee for IoT applications, which not only have higher requirements on computation resources, but also are delay and/or security sensitive. In this framework, a delay and security-sensitive computation task is usually divided into several sub-tasks, which could be offloaded to either fog or cloud computing servers, referred to as offloading destinations. Sub-tasks may exchange information during their processing and then have requirement on transmission bandwidth. Different destinations produce different completion delays of a sub-task, affecting the corresponding task delay. the existing offloading approaches either considered only a single type of offloading destinations or ignored delay and/or security constraint. this paper studies a computation offloading problem in the fog-cloud scenario where not only computation and security capabilities of offloading destinations may be different, but also bandwidth and delay of links may be different. We first propose a joint offloading approach by formulating the problem as a form of Mixed Integer Programming Multi-Commodity Flow to maximize the fog-cloud provider's revenue without sacrificing performance and security requirements of users. We also propose a greedy algorithm for the problem. Extensive simulation results under various network scales show that the proposed computation offloading mechanism achieves higher revenue than the conventional single-type computation offloading under delay and security constraints.

关键词： Fog-cloud computing IoT application Mixed integer programming Security Greedy algorithm

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Efficient VM Selection Heuristics for Dynamic VM Consolidation in Cloud Datacenters 16

Efficient VM Selection Heuristics for Dynamic VM Consolidati...

引用

16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Qaiser, Hammad Ur Rehman Shu, Gao Wuhan Univ Technol Sch Comp Sci & Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781728111414

Dynamic Consolidation of Virtual Machines (VMs) in a cloud data center requires live migration of VMs from over-utilized hosts. A primary but relatively an ignored part of consolidation process is the efficient selection of Virtual Machines from an over-utilized host for migration. Two VM selection policies, threshold Based Selection (TBS) and Capacity Based Selection (CBS), have been proposed in this paper. these policies are based on the idea of simultaneously minimizing multiple factors that contribute to the degradation of the quality of service due to consolidation. three degrading factors considered in the policies are, the time duration of VM migrations, time duration hosts remain over-utilized and the total number of migrations required for consolidation. Cost functions, involving these degrading factors, have been provided which formed the bases for TBS and CBS. TBS is an efficient VM selection mechanism that focuses more on the time duration of VM migrations and the total number of migrations required for the consolidation process as a trade-off between the three degrading factors. On the other hand, CBS is another efficient mechanism with more emphasis on reducing the time duration for which hosts remain over-utilized. Experiment results obtained by using Cloudsim simulating toolkit have shown that our proposed policies outperformed conventional VM selection policies like MMT, MU, and RC on indicators such as energy consumption, SLA violations, and overall performance efficiency.

关键词： Virtual Machine Consolidation Virtual Machine Selection Policies Energy Efficient computing Cloud computing Efficient Resource Management System

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Human Activity Recognition Using Federated Learning 16

Human Activity Recognition Using Federated Learning

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Sozinov, Konstantin Vlassov, Vladimir Girdzijauskas, Sarunas KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781728111414

State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. the global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

关键词： Federated Learning Human Activity Recognition Privacy distributed Machine Learning

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Delay-aware Resource Allocation for Data Analysis in Cloud-Edge System 16

Delay-aware Resource Allocation for Data Analysis in Cloud-E...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Li, Xin Lian, Zhen Qin, Xiaolin Abawajy, Jemal Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (纸本)9781728111414

there is a strong need for data analysis in information systems to support various services. Traditional cloud data centers provide powerful ability to conduct data analysis jobs. However, the data transmission consumes a large amount of time and leads to a long service delay. the QoS (Quality of Service) caused by long service delay is unacceptable for real-time services or applications. the collaboration with edge computing is an opportunity for service delay reduction. In this paper, we investigate the task placement problem for reducing service delay in cloud-edge system. We use the W-DAG (Weighted Directed Acyclic Graph) to model the data-intensive service or business logic. We analyze the data and resource requirements for the tasks, which constitute the integrated service, and make resource allocation between cloud data center and edge nodes. then, we propose the task placement algorithm to achieve shorter service delay. the core idea is to make a tradeoff between data transmission time and data analysis time. the simulation results show that our algorithm has significant performance improvement on service delay reduction.

关键词： delay-aware data-driven edge computing resource allocation task placement

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