Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00139
Alejandro Jesus Capella Del Solar, J. M. Solé, Felix Freitag
LoRa is a popular communication technology used in Internet of Things (IoT) applications. Typically, in the LoRaWAN architecture, an end node periodically sends a LoRaWAN message to a gateway connected to the Internet. Recent works, however, showed the possibility of LoRa mesh networks where LoRa nodes communicate with each other. In this paper we present a monitoring system as a tool for observing the traffic and the operation of such LoRa mesh networks. The client side is implemented at the LoRa nodes which periodically send to a server detailed information about the nodes’ in-and outgoing LoRa packets. The server visualizes the information through a dashboard. Thus the monitoring tool allows network administrators to further analyze such LoRa mesh networks.
{"title":"Towards a Monitoring System for a LoRa Mesh Network","authors":"Alejandro Jesus Capella Del Solar, J. M. Solé, Felix Freitag","doi":"10.1109/ICDCS54860.2022.00139","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00139","url":null,"abstract":"LoRa is a popular communication technology used in Internet of Things (IoT) applications. Typically, in the LoRaWAN architecture, an end node periodically sends a LoRaWAN message to a gateway connected to the Internet. Recent works, however, showed the possibility of LoRa mesh networks where LoRa nodes communicate with each other. In this paper we present a monitoring system as a tool for observing the traffic and the operation of such LoRa mesh networks. The client side is implemented at the LoRa nodes which periodically send to a server detailed information about the nodes’ in-and outgoing LoRa packets. The server visualizes the information through a dashboard. Thus the monitoring tool allows network administrators to further analyze such LoRa mesh networks.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124940055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination’s perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmission for AoI optimization. While at wireless-powered network edge, the charging plan of source nodes also requires to be computed in advance, which means the system AoI is determined by not only the data transmission decision but also the charging plan. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge with a directional charger. Firstly, to minimize the weighted sum of average peak AoI, the AoI minimization problem is transformed to a charging time optimization problem with respect to the overlapped charging areas and average peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Then, an age-based scheduling algorithm is proposed to compute the charging and data transmission decisions for each source node simultaneously, which can not only optimize the weighted sum of average peak AoI but also guarantee the maximum peak AoI for each source node. The proposed algorithm is proved to have an approximation ratio of up to (1+φ), where φ is a much smaller value related to the weight of each source node. Finally, the simulation results verify the high performance of proposed algorithms in terms of average and maximum peak AoI.
{"title":"AoI Minimization Charging at Wireless-Powered Network Edge","authors":"Q. Chen, Song Guo, Wenchao Xu, Zhipeng Cai, Lianglun Cheng, Hongyang Gao","doi":"10.1109/ICDCS54860.2022.00074","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00074","url":null,"abstract":"Age of Information (AoI) has emerged as a new metric to measure data freshness from the destination’s perspective. The problem of optimizing AoI has been attracting extensive interests recently. However, existing works mainly focused on scheduling data transmission for AoI optimization. While at wireless-powered network edge, the charging plan of source nodes also requires to be computed in advance, which means the system AoI is determined by not only the data transmission decision but also the charging plan. Thus, in this paper, we investigate the first work to optimize the weighted peak AoI from the point of charging at wireless-powered network edge with a directional charger. Firstly, to minimize the weighted sum of average peak AoI, the AoI minimization problem is transformed to a charging time optimization problem with respect to the overlapped charging areas and average peak AoI, and an approximate algorithm is proposed to obtain the required charging time for each source node. Then, an age-based scheduling algorithm is proposed to compute the charging and data transmission decisions for each source node simultaneously, which can not only optimize the weighted sum of average peak AoI but also guarantee the maximum peak AoI for each source node. The proposed algorithm is proved to have an approximation ratio of up to (1+φ), where φ is a much smaller value related to the weight of each source node. Finally, the simulation results verify the high performance of proposed algorithms in terms of average and maximum peak AoI.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125140509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00013
Pierre Civit, Seth Gilbert, V. Gramoli, R. Guerraoui, Jovan Komatovic, Zarko Milosevic, Adi Serendinschi
A decision task is a distributed input-output problem in which each process starts with its input value and eventually produces its output value. Examples of such decision tasks are broad and range from consensus to reliable broadcast to lattice agreement. A distributed protocol solves a decision task if it enables processes to produce admissible output values despite arbitrary (Byzantine) failures. Unfortunately, it has been known for decades that many decision tasks cannot be solved if the system is overly corrupted, i.e., safety of distributed protocols solving such tasks can be violated in unlucky scenarios.By contrast, only recently did the community discover that some of these distributed protocols can be made accountable by ensuring that correct processes irrevocably detect some faulty processes responsible for any safety violation. This realization is particularly surprising (and positive) given that accountability is a powerful tool to mitigate safety violations in distributed protocols. Indeed, exposing crimes and introducing punishments naturally incentivize exemplarity.In this paper, we propose a generic transformation, called τscr, of any non-synchronous distributed protocol solving a decision task into its accountable version. Our τscr transformation is built upon the well-studied simulation of crash failures on top of Byzantine failures and increases the communication complexity by a quadratic multiplicative factor in the worst case.
{"title":"Crime and Punishment in Distributed Byzantine Decision Tasks","authors":"Pierre Civit, Seth Gilbert, V. Gramoli, R. Guerraoui, Jovan Komatovic, Zarko Milosevic, Adi Serendinschi","doi":"10.1109/ICDCS54860.2022.00013","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00013","url":null,"abstract":"A decision task is a distributed input-output problem in which each process starts with its input value and eventually produces its output value. Examples of such decision tasks are broad and range from consensus to reliable broadcast to lattice agreement. A distributed protocol solves a decision task if it enables processes to produce admissible output values despite arbitrary (Byzantine) failures. Unfortunately, it has been known for decades that many decision tasks cannot be solved if the system is overly corrupted, i.e., safety of distributed protocols solving such tasks can be violated in unlucky scenarios.By contrast, only recently did the community discover that some of these distributed protocols can be made accountable by ensuring that correct processes irrevocably detect some faulty processes responsible for any safety violation. This realization is particularly surprising (and positive) given that accountability is a powerful tool to mitigate safety violations in distributed protocols. Indeed, exposing crimes and introducing punishments naturally incentivize exemplarity.In this paper, we propose a generic transformation, called τscr, of any non-synchronous distributed protocol solving a decision task into its accountable version. Our τscr transformation is built upon the well-studied simulation of crash failures on top of Byzantine failures and increases the communication complexity by a quadratic multiplicative factor in the worst case.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128394591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00112
Yuefeng Du, Anxin Zhou, Cong Wang
The flourishing development of blockchain and cryptocurrency has made it a hotbed for cyber-criminals to implement virtually untraceable scams. Consequently, the blockchain ecosystem urgently needs an effective method to help users stay away from scams in order to create an enticing investment environment. Despite the massive deployment of blocklist query APIs for malicious and scam domains/URLs in the industry, we identify two core reasons why existing blocklist services find it difficult to thrive in the cryptocurrency paradigm: 1) the compelling need to protect a user query due to sensitivity and high value of query content, i.e., payment addresses; 2) the thorny issue of evaluating the quality of blocklists effectively, in the face of common practices of incompetent providers.To this end, we first provide a private and highly efficient blocklist query scheme as a basic design, which conveniently achieves backward compatibility with current blockchain payment systems at a considerably low cost. Based on this design, we propose a new framework for shareholders to evaluate the quality of blocklists. Our framework provides stronger security guarantees than other similar works, as it is capable of suppressing both individual biasing and coercive manipulation at the same time. We provide a complete game-theoretic analysis and demonstrate comprehensive evaluation results to confirm the effectiveness and efficiency of our solutions, under the settings of a practical number of shareholders.
{"title":"Enhancing Cryptocurrency Blocklisting: A Secure, Trustless, and Effective Realization","authors":"Yuefeng Du, Anxin Zhou, Cong Wang","doi":"10.1109/ICDCS54860.2022.00112","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00112","url":null,"abstract":"The flourishing development of blockchain and cryptocurrency has made it a hotbed for cyber-criminals to implement virtually untraceable scams. Consequently, the blockchain ecosystem urgently needs an effective method to help users stay away from scams in order to create an enticing investment environment. Despite the massive deployment of blocklist query APIs for malicious and scam domains/URLs in the industry, we identify two core reasons why existing blocklist services find it difficult to thrive in the cryptocurrency paradigm: 1) the compelling need to protect a user query due to sensitivity and high value of query content, i.e., payment addresses; 2) the thorny issue of evaluating the quality of blocklists effectively, in the face of common practices of incompetent providers.To this end, we first provide a private and highly efficient blocklist query scheme as a basic design, which conveniently achieves backward compatibility with current blockchain payment systems at a considerably low cost. Based on this design, we propose a new framework for shareholders to evaluate the quality of blocklists. Our framework provides stronger security guarantees than other similar works, as it is capable of suppressing both individual biasing and coercive manipulation at the same time. We provide a complete game-theoretic analysis and demonstrate comprehensive evaluation results to confirm the effectiveness and efficiency of our solutions, under the settings of a practical number of shareholders.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124329860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a promising framework for distributed learning in mobile edge computing scenarios, federated learning (FL) allows multiple mobile devices to train a model collaboratively without transferring raw data and exposing user privacy. However, vanilla FL schemes are still facing to problems in edge computing, where the diversity of tasks and devices causes the non-IID and multi-granularity data with model heterogeneity. It becomes a pressing challenge to jointly training edge devices accompanied by these problems, while vanilla FL only discusses them separately. To this end, we consider tailoring FL to adapt to mobile edge environments, which focus on solving the problems of collaborative training of edge devices with multi-granularity heterogeneous models under different data distributions. In particular, we proposed a distance-based FL for the same type of edge devices that provides personalized models to avoid the negative impact of non-IID data on model aggregation. Further, we design a bi-directional guidance method with a prior attention mechanism, which can transfer knowledge among edge devices with multi-granulairty and multi-scale models. The experimental results show that our proposed mechanisms significantly improve training performance compared to other baselines on IID and non-IID data. Furthermore, the bi-directional guidance significantly improves convergence efficiency and accuracy performance for finer and coarser granularity edge devices, respectively.
{"title":"Multi-granularity Weighted Federated Learning in Heterogeneous Mobile Edge Computing Systems","authors":"Shangxuan Cai, Yunfeng Zhao, Zhicheng Liu, Chao Qiu, Xiaofei Wang, Qinghua Hu","doi":"10.1109/ICDCS54860.2022.00049","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00049","url":null,"abstract":"As a promising framework for distributed learning in mobile edge computing scenarios, federated learning (FL) allows multiple mobile devices to train a model collaboratively without transferring raw data and exposing user privacy. However, vanilla FL schemes are still facing to problems in edge computing, where the diversity of tasks and devices causes the non-IID and multi-granularity data with model heterogeneity. It becomes a pressing challenge to jointly training edge devices accompanied by these problems, while vanilla FL only discusses them separately. To this end, we consider tailoring FL to adapt to mobile edge environments, which focus on solving the problems of collaborative training of edge devices with multi-granularity heterogeneous models under different data distributions. In particular, we proposed a distance-based FL for the same type of edge devices that provides personalized models to avoid the negative impact of non-IID data on model aggregation. Further, we design a bi-directional guidance method with a prior attention mechanism, which can transfer knowledge among edge devices with multi-granulairty and multi-scale models. The experimental results show that our proposed mechanisms significantly improve training performance compared to other baselines on IID and non-IID data. Furthermore, the bi-directional guidance significantly improves convergence efficiency and accuracy performance for finer and coarser granularity edge devices, respectively.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127700336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00045
Yutao Yang, Yinbin Miao, K. Choo, R. Deng
With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatio-textual data is outsourced to the cloud server to reduce the local high storage and computing burdens, but at the same time causes security issues such as data privacy leakage. Thus, extensive privacy-preserving spatial keyword query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) for encryption, but ASPE has proven to be insecure. And the existing spatial range query schemes require users to provide more information about the query range and generate a large amount of ciphertext, which causes high storage and computational burdens. To solve these issues, in this paper we introduce some random numbers and a random permutation to enhance the security of ASPE scheme, and then propose a novel privacy-preserving Spatial Keyword Query (SKQ) scheme based on the enhanced ASPE and Geohash algorithm. In addition, we design a more Lightweight Spatial Keyword Query (LSKQ) scheme by using a unified index for spatial range and multiple keywords, which not only greatly decreases SKQ’s storage and computational costs but also requires users to provide little information about query region. Finally, formal security analysis proves that our schemes have Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our enhanced scheme is efficient and practical.
{"title":"Lightweight Privacy-Preserving Spatial Keyword Query over Encrypted Cloud Data","authors":"Yutao Yang, Yinbin Miao, K. Choo, R. Deng","doi":"10.1109/ICDCS54860.2022.00045","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00045","url":null,"abstract":"With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatio-textual data is outsourced to the cloud server to reduce the local high storage and computing burdens, but at the same time causes security issues such as data privacy leakage. Thus, extensive privacy-preserving spatial keyword query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) for encryption, but ASPE has proven to be insecure. And the existing spatial range query schemes require users to provide more information about the query range and generate a large amount of ciphertext, which causes high storage and computational burdens. To solve these issues, in this paper we introduce some random numbers and a random permutation to enhance the security of ASPE scheme, and then propose a novel privacy-preserving Spatial Keyword Query (SKQ) scheme based on the enhanced ASPE and Geohash algorithm. In addition, we design a more Lightweight Spatial Keyword Query (LSKQ) scheme by using a unified index for spatial range and multiple keywords, which not only greatly decreases SKQ’s storage and computational costs but also requires users to provide little information about query region. Finally, formal security analysis proves that our schemes have Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our enhanced scheme is efficient and practical.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126303702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00114
Javad Forough, M. Bhuyan, E. Elmroth
Web application services and networks become a major target of low-rate Distributed Denial of Service (DDoS) attacks such as Very Short Intermittent DDoS (VSI-DDoS). These threats exploit the TCP congestion control mechanism to cause transient resource outage and impute delays for legitimate users’ requests, while they bypass the secure systems. Besides that, cross-layer VSI-DDoS attacks, where the performed attacks are towards the different layers of the edge cloud infrastructures, are able to cause violation of customers’ Service-Level Agreements (SLAs) with less visible behavioral patterns. In this work, we propose a novel Deep Ensemble Learning Approach named DELA for detection of cross-layer VSI-DDoS on the edge cloud. This approach is developed based on Long Short-Term Memory (LSTM), ensemble learning, and a new voting mechanism based on Feed-Forward Neural Network (FFNN). In addition, it employs a novel training and detection algorithm to combat such attacks in web services and networks. The model shows improved results due to the utilization of historical information in decision- making and also the usage of neural network as aggregator instead of a static threshold-based aggregation. Moreover, we propose a novel overlapped data chunking algorithm that is able to ameliorate the detection performance. Furthermore, the evaluation of DELA shows its superior performance over our testbed and benchmark datasets. Accordingly, DELA achieves on average 4.88% higher F 1 score compared to state-of-the-art methods.
{"title":"DELA: A Deep Ensemble Learning Approach for Cross-layer VSI-DDoS Detection on the Edge","authors":"Javad Forough, M. Bhuyan, E. Elmroth","doi":"10.1109/ICDCS54860.2022.00114","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00114","url":null,"abstract":"Web application services and networks become a major target of low-rate Distributed Denial of Service (DDoS) attacks such as Very Short Intermittent DDoS (VSI-DDoS). These threats exploit the TCP congestion control mechanism to cause transient resource outage and impute delays for legitimate users’ requests, while they bypass the secure systems. Besides that, cross-layer VSI-DDoS attacks, where the performed attacks are towards the different layers of the edge cloud infrastructures, are able to cause violation of customers’ Service-Level Agreements (SLAs) with less visible behavioral patterns. In this work, we propose a novel Deep Ensemble Learning Approach named DELA for detection of cross-layer VSI-DDoS on the edge cloud. This approach is developed based on Long Short-Term Memory (LSTM), ensemble learning, and a new voting mechanism based on Feed-Forward Neural Network (FFNN). In addition, it employs a novel training and detection algorithm to combat such attacks in web services and networks. The model shows improved results due to the utilization of historical information in decision- making and also the usage of neural network as aggregator instead of a static threshold-based aggregation. Moreover, we propose a novel overlapped data chunking algorithm that is able to ameliorate the detection performance. Furthermore, the evaluation of DELA shows its superior performance over our testbed and benchmark datasets. Accordingly, DELA achieves on average 4.88% higher F 1 score compared to state-of-the-art methods.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00140
Zhenyu Zhang, Huan Zhouand, Liang Zhao, Victor C. M. Leung
This paper considers the joint optimization of computation offloading, service caching, and resource allocation in the Digital Twin Edge Network (DTEN), and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP), whose goal is to minimize the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and outperform other benchmark algorithms under different scenarios.
{"title":"Digital Twin Assisted Computation Offloading and Service Caching in Mobile Edge Computing","authors":"Zhenyu Zhang, Huan Zhouand, Liang Zhao, Victor C. M. Leung","doi":"10.1109/ICDCS54860.2022.00140","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00140","url":null,"abstract":"This paper considers the joint optimization of computation offloading, service caching, and resource allocation in the Digital Twin Edge Network (DTEN), and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP), whose goal is to minimize the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and outperform other benchmark algorithms under different scenarios.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122053108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00014
Gongming Zhao, Jingzhou Wang, Yangming Zhao, Hongli Xu, C. Qiao
There are two conventional methods to establish an entanglement connection in a Quantum Data Network (QDN). One is to create single-hop entanglement links first and then connect them with quantum swapping, and the other is for-warding one of the entangled photons from one end to the other via all-optical switching at intermediate nodes to directly establish an entanglement connection. Since a photon is easy to be lost during a long distance transmission, all existing works are adopting the former method. However, in a room size network, the success probability of delivering a photon across multiple links via all-optical switching is not that low. In addition, with an all-optical switching technique, we can save quantum memory at the intermediate nodes. Accordingly, we are expecting to establish significantly more entanglement connections with limited quantum resources by first creating entanglement segments, each spanning multiple quantum links, using all-optical switching, and then connecting them with quantum swapping.In this paper, we design SEE, a Segmented Entanglement Establishment approach that seamlessly integrates quantum swapping and all-optical switching to maximize quantum network throughput. SEE first creates entanglement segments over one or multiple quantum links with all-optical switching, and then connect them with quantum swapping. It is clear that an entanglement link is only a special entanglement segment. Accordingly, SEE can theoretically outperform conventional entanglement link based approaches. Large scale simulations show that SEE can achieve up to 100.00% larger throughput compared with the state-of-the-art entanglement link based approach, i.e., REPS.
{"title":"Segmented Entanglement Establishment for Throughput Maximization in Quantum Networks","authors":"Gongming Zhao, Jingzhou Wang, Yangming Zhao, Hongli Xu, C. Qiao","doi":"10.1109/ICDCS54860.2022.00014","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00014","url":null,"abstract":"There are two conventional methods to establish an entanglement connection in a Quantum Data Network (QDN). One is to create single-hop entanglement links first and then connect them with quantum swapping, and the other is for-warding one of the entangled photons from one end to the other via all-optical switching at intermediate nodes to directly establish an entanglement connection. Since a photon is easy to be lost during a long distance transmission, all existing works are adopting the former method. However, in a room size network, the success probability of delivering a photon across multiple links via all-optical switching is not that low. In addition, with an all-optical switching technique, we can save quantum memory at the intermediate nodes. Accordingly, we are expecting to establish significantly more entanglement connections with limited quantum resources by first creating entanglement segments, each spanning multiple quantum links, using all-optical switching, and then connecting them with quantum swapping.In this paper, we design SEE, a Segmented Entanglement Establishment approach that seamlessly integrates quantum swapping and all-optical switching to maximize quantum network throughput. SEE first creates entanglement segments over one or multiple quantum links with all-optical switching, and then connect them with quantum swapping. It is clear that an entanglement link is only a special entanglement segment. Accordingly, SEE can theoretically outperform conventional entanglement link based approaches. Large scale simulations show that SEE can achieve up to 100.00% larger throughput compared with the state-of-the-art entanglement link based approach, i.e., REPS.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127975255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00053
Hongzhou Liu, Wenli Zheng, Li Li, Minyi Guo
The emerging edge computing technique provides support for the computation tasks that are delay-sensitive and compute-intensive, such as deep neural network inference, by offloading them from a user-end device to an edge server for fast execution. The increasing offloaded tasks on an edge server are gradually facing the contention of both the network and computation resources. The existing offloading approaches often partition the deep neural network at a place where the amount of data transmission is small to save network resource, but rarely consider the problem caused by computation resource shortage on the edge server. In this paper, we design LoADPart, a deep neural network offloading system. LoADPart can dynamically and jointly analyze both the available network bandwidth and the computation load of the edge server, and make proper decisions of deep neural network partition with a light-weighted algorithm, to minimize the end-to-end inference latency. We implement LoADPart for MindSpore, a deep learning framework supporting edge AI, and compare it with state-of-the-art solutions in the experiments on 6 deep neural networks. The results show that under the variation of server computation load, LoADPart can reduce the end-to-end latency by 14.2% on average and up to 32.3% in some specific cases.
{"title":"LoADPart: Load-Aware Dynamic Partition of Deep Neural Networks for Edge Offloading","authors":"Hongzhou Liu, Wenli Zheng, Li Li, Minyi Guo","doi":"10.1109/ICDCS54860.2022.00053","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00053","url":null,"abstract":"The emerging edge computing technique provides support for the computation tasks that are delay-sensitive and compute-intensive, such as deep neural network inference, by offloading them from a user-end device to an edge server for fast execution. The increasing offloaded tasks on an edge server are gradually facing the contention of both the network and computation resources. The existing offloading approaches often partition the deep neural network at a place where the amount of data transmission is small to save network resource, but rarely consider the problem caused by computation resource shortage on the edge server. In this paper, we design LoADPart, a deep neural network offloading system. LoADPart can dynamically and jointly analyze both the available network bandwidth and the computation load of the edge server, and make proper decisions of deep neural network partition with a light-weighted algorithm, to minimize the end-to-end inference latency. We implement LoADPart for MindSpore, a deep learning framework supporting edge AI, and compare it with state-of-the-art solutions in the experiments on 6 deep neural networks. The results show that under the variation of server computation load, LoADPart can reduce the end-to-end latency by 14.2% on average and up to 32.3% in some specific cases.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115853525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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