IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS 2020)
Session G1
Scheduling and Resource Management
Conference
1:30 PM
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2:50 PM HKT
Local
Dec 2 Wed, 11:30 PM
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12:50 AM CST
Compressive Sensing based Predictive Online Scheduling with Task Colocation in Cloud Data Center
Yunhin Chan, Ke Luo and Xu Chen
0
With the growing size of the cloud data center, the high scheduling efficiency over massive-scale cloud servers is hard to achieve, particularly when the scheduler requires the full real-time cloud resource information for decision making. Moreover, most data centers only run latency-critical online services, resulting in low resource utilization. To solve these problems, we propose a Compressive Sensing based Predictive Online Scheduling (CSPOS) algorithm. To mitigate the bottleneck of transferring massive resource information of all cloud servers to the scheduler, we propose to transfer sampled data from a small subset of servers to the scheduler and recover the full cloud resource information by compressive sensing. We then propose a predictive online learning algorithm that efficiently colocates the online services and batch jobs, in order to boost the resource utilization of the data center. Our experiments show that the CSPOS model achieves outstanding scheduling efficiency under various settings and is able to greatly increase the resource usage of a data center. We also illustrate that the running time of the CSPOS model is very small and has negligible effects on the scheduling system.
Non-Technical Losses Detection in Smart Grids: An Ensemble Data-Driven Approach
Yufeng Xing, Lei Guo, Zongchao Xie, Lei Cui, Longxiang Gao and Shui Yu
0
Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid datadriven detection framework. In particular, we introduce a wide & deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.
Virtual Machine Consolidation for NUMA Systems: A Hybrid Heuristic Grey Wolf Approach
Kangli Hu, Weiwei Lin, Tiansheng Huang, Keqin Li and Like Ma
0
Virtual machines consolidation is known as a powerful means to reduce the number of activated physical machines (PMs), so as to achieve energy-saving for the data centers. Although the consolidation technique is widely studied in non-NUMA systems, we could only trace a few studies targeting NUMA systems. But the virtual machines (VMs) deployment of NUMA systems is quite different from that of non-NUMA systems. More specifically, consolidating VMs in NUMA systems need to decide both target physical machines and NUMA architectures to host the VMs, and more complicated constraints originated from the real usage of NUMA systems that need to be considered. Being motivated by these challenges, we in this paper formally derive the system model according to the real business model of NUMA systems and based on which, we propose a hybrid heuristics swarm intelligence optimization algorithm HHGWA for an efficient solution. To do the evaluation, extensive simulations that integrate real VM and PM information are conducted, the result of which indicates a superior performance of our proposed algorithm.
A Novel Classification Model to Predict Batch Job Failures in Co-located Cloud
Yurui Li, Weiwei Lin, Keqin Li, James Z. Wang, Fagui Liu and Jie Liu
0
Nowadays, cloud co-location is often used for data centers to improve the utilization of computing resources. However, batch jobs in a Co-location Datacenter (CLD) are vulnerable to failures due to the competition for limited resources with online service jobs. Such failed batch jobs would be rescheduled and failed repeatedly, resulting in the waste of computing resources and instability of the computing clusters. Therefore, we propose a method to accurately predict the potential failures of batch jobs for CLD. The core of the proposed method is STLF (SMOTE Tomek and LightGBM [5] Framework), which is divided into three parts. First, we use the co-feature extraction method to generate Co-located Feature Dataset (CLFD). Then SMOTE Tomek is used to oversampling the CLFD to ensure that the classifier can learn more minority features. Finally, we use LightGBM classifier to predict batch jobs�� failure. The performance experiments conducted on the Ali Trace 2018 dataset show that our proposed STLF significantly outperforms the existing popular classifiers in terms of the ROC curve, the area under the ROC curve (AUC), precision, and recall.
Session Chair
Zaipeng Xie (Hohai University)
Session G2
Distributed System Design and Implementation
Conference
1:30 PM
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2:50 PM HKT
Local
Dec 2 Wed, 11:30 PM
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12:50 AM CST
ABC: An Auction-Based Blockchain Consensus-Incentive Mechanism
Zhengpeng Ai, Yuan Liu and Xingwei Wang
0
The rapid development of blockchain technology and its various applications have attracted huge attention in the last five years. The consensus mechanism and incentive mechanism are the backbone of a blockchain network. The consensus mechanism plays a crucial role in sustaining the network security, integrity, and efficiency. The incentive mechanism motivates the distributed nodes to ��mine�� so as to participate the consensus mechanism. The existing mechanisms bear the fairness and justice issues. In this paper, from the perspective of mechanism design, we propose a consensus-incentive mechanism through applying continuous double auction theory, which is abbreviated as ABC mechanism. Our mechanism consists of four stages, including initiation stage, auction stage, completion stage, and confirmation stage. The auction model in use is the continuous double auction to ensure the transactions are stored in a real-time manner. Through extensive experimental evaluations, our mechanism is proven to improve the fairness and justice of the blockchain network.
A Trustworthy Blockchain-based Decentralised Resource Management System in the Cloud
Zhiming Zhao, Chunming Rong and Martin Gilje Jaatun
0
Quality Critical Decentralised Applications (QCDApp) have high requirements for system performance and service quality, involve heterogeneous infrastructures (Clouds, Fogs, Edges and IoT), and rely on the trustworthy collaborations among participants of data sources and infrastructure providers to deliver their business value. The development of the QCDApp has to tackle the low-performance challenge of the current blockchain technologies due to the low collaboration efficiency among distributed peers for consensus. On the other hand, the resilience of the Cloud has enabled significant advances in software-defined storage, networking, infrastructure, and every technology; however, those rich programmabilities of infrastructure (in particular, the advances of new hardware accelerators in the infrastructures) can still not be effectively utilised for QCDApp due to lack of suitable architecture and programming model.
DCVP: Distributed Collaborative Video Stream Processing in Edge Computing
Shijing Yuan, Jie Li, Chentao Wu, Yusheng Ji and Yongbing Zhang
0
In edge computing, computation offloading of video stream tasks and collaboration processing among edge nodes is a huge challenge. The previous research mainly focuses on the selection of computing modes and resource allocation, but taking no joint consideration of computation offloading and collaborative processing of edge node groups. In order to jointly tackle these issues in edge computing, we propose an innovative distributed collaborative video stream processing framework for edge computing(DCVP) where the video tasks are assigned to mobile edge computing (MEC) nodes or edge groups based on the offloading decision. First, we design a method for the group formation, which matches video subtasks to appropriate edge groups. In addition, we present two offloading modes for video streaming tasks, e.g., offloading to MEC nodes or edge groups, to handle computationally intensive video tasks. Furthermore, we formulate the joint optimization problem for offloading decision and collaborative processing of video subtasks into a distributed optimization problem. Finally, we employ an alternating direction method of multipliers (ADMM)-based algorithm to solve the problem. Simulation results under multiple parameters show the proposed schemes outperform other typical schemes.
Efficient Post-quantum Identity-based Encryption with Equality Test
Willy Susilo, Dung Hoang Duong and Huy Quoc Le
0
Public key encryption with equality test (PKEET) enables the testing whether two ciphertexts encrypt the same message. Identity-based encryption with equality test (IBEET) simplify the certificate management of PKEET, which leads to many potential applications such as in smart city applications or Wireless Body Area Networks. Lee et al. (ePrint 2016) proposed a generic construction of IBEET scheme in the standard model utilising a 3-level hierachy IBE together with a one-time signature scheme, which can be instantiated in lattice setting. Duong et al. (ProvSec 2019) proposed the first direct construction of IBEET in standard model from lattices. However, their scheme achieve CPA security only. In this paper, we improve the Duong et al.��s construction by proposing an IBEET in standard model which achieves CCA2 security and with smaller ciphertext and public key size.
Session Chair
Jia Liu (Nanjing University)
Session G3
Federated Learning and Deep Learning
Conference
1:30 PM
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2:30 PM HKT
Local
Dec 2 Wed, 11:30 PM
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12:30 AM CST
Robust Federated Learning Approach for Travel Mode Identification from Non-IID GPS Trajectories
Yuanshao Zhu, Shuyu Zhang, Yi Liu, Dusit Niyato and James J.Q. Yu
0
GPS trajectory is one of the most significant data sources in intelligent transportation systems (ITS). A simple application is to use these data sources to help companies or organizations identify users�� travel behavior. However, since GPS trajectory is directly related to private data (e.g., location) of users, citizens are unwilling to share their private information with the third-party. How to identify travel modes while protecting the privacy of users is a significant issue. Fortunately, Federated Learning (FL) framework can achieve privacy-preserving deep learning by allowing users to keep GPS data locally instead of sharing data. In this paper, we propose a Roust Federated Learning-based Travel Mode Identification System to identify travel mode without compromising privacy. Specifically, we design an attention augmented model architectures and leverage robust FL to achieve privacy-preserving travel mode identification without accessing raw GPS data from the users. Compared to existing models, we are able to achieve more accurate identification results than the centralized model. Furthermore, considering the problem of non-Independent and Identically Distributed (non-IID) GPS data in the realworld, we develop a secure data sharing strategy to adjust the distribution of local data for each user, thereby the proposed model with non-IID data can achieve accuracy close to the distribution of IID data. Extensive experimental studies on a real-world dataset demonstrate that the proposed model can achieve accurate identification without compromising privacy and being robust to real-world non-IID data.
Deep Spatio-Temporal Attention Model for Grain Storage Temperature Forecasting
Shanshan Duan, Weidong Yang, Xuyu Wang, Shiwen Mao and Yuan Zhang
0
Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatio-temporal attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods.
Proactive Content Caching for Internet-of-Vehicles based on Peer-to-Peer Federated Learning
Zhengxin Yu, Jia Hu, Geyong Min, Han Xu and Jed Mills
0
To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and network traffic on the Internet-of-Vehicles (IoV). However, the inherent characteristics of IoV, including the high mobility of vehicles and restricted storage capability of edge nodes, cause many difficulties in the design of caching schemes. Driven by the recent advancements in machine learning, learning-based proactive caching schemes are able to accurately predict content popularity and improve cache efficiency, but they need gather and analyse users�� content retrieval history and personal data, leading to privacy concerns. To address the above challenge, we propose a new proactive caching scheme based on peer-to-peer federated deep learning, where the global prediction model is trained from data scattered at vehicles to mitigate the privacy risks. In our proposed scheme, a vehicle acts as a parameter server to aggregate the updated global model from peers, instead of an edge node. A dual-weighted aggregation scheme is designed to achieve high global model accuracy. Moreover, to enhance the caching performance, a Collaborative Filtering based Variational AutoEncoder model is developed to predict the content popularity. The experimental results demonstrate that our proposed caching scheme largely outperforms typical baselines, such as Greedy and Most Recently Used caching.
Session Chair
Bolei Zhang (Nanjing University of Posts and Telecommunications)
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