Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00075
Chi Lin, Ziwei Yang, Jiankang Ren, Lei Wang, Wei Zhong, Guowei Wu, Qiang Zhang
Wireless rechargeable sensor networks (WRSNs), which benefit from recent breakthroughs in Wireless Power Transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods concentrate on system performance improvement while little attention has been paid to security, making them vulnerable to novel attacks. In this paper, we develop a novel Charging Spoofing Attack (CSA), in which a mobile charger (MC) is charging a node intuitively. Nevertheless, it is launching an attack based on the nonlinear superposition principle of electromagnetic waves, causing the target node to be unable to receive any energy and finally exhausted in vain. First, we explain and model the nonlinear superposition effect through experiments, which points out the potential of launching such a novel attack. Second, we formalize the attacking problem as a charging uTility optImization problem with key noDe timE window constraints (TIDE). Then, we propose an approximation algorithm termed CSA to solve the TIDE problem with a bounded performance guarantee. Theoretical analyses are presented to exploit the feature of CSA. Finally, to demonstrate the outperformed features of our scheme, extensive simulations and test-bed experiments are conducted, revealing that CSA can exhaust at least 80% of key nodes without being detected.
{"title":"Are You Really Charging Me?","authors":"Chi Lin, Ziwei Yang, Jiankang Ren, Lei Wang, Wei Zhong, Guowei Wu, Qiang Zhang","doi":"10.1109/ICDCS54860.2022.00075","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00075","url":null,"abstract":"Wireless rechargeable sensor networks (WRSNs), which benefit from recent breakthroughs in Wireless Power Transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods concentrate on system performance improvement while little attention has been paid to security, making them vulnerable to novel attacks. In this paper, we develop a novel Charging Spoofing Attack (CSA), in which a mobile charger (MC) is charging a node intuitively. Nevertheless, it is launching an attack based on the nonlinear superposition principle of electromagnetic waves, causing the target node to be unable to receive any energy and finally exhausted in vain. First, we explain and model the nonlinear superposition effect through experiments, which points out the potential of launching such a novel attack. Second, we formalize the attacking problem as a charging uTility optImization problem with key noDe timE window constraints (TIDE). Then, we propose an approximation algorithm termed CSA to solve the TIDE problem with a bounded performance guarantee. Theoretical analyses are presented to exploit the feature of CSA. Finally, to demonstrate the outperformed features of our scheme, extensive simulations and test-bed experiments are conducted, revealing that CSA can exhaust at least 80% of key nodes without being detected.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"74 3 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":"116469205","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}
The emergence of new storage like persistent memory (PM) and zoned namespaces SSDs (ZNS-SSDs) introduces new challenges and opportunities for distributed key-value stores. Since LSM-tree has been widely adopted in distributed key-value stores, such as RocksDB and HBase, it is necessary to revisit the LSM-tree to make it adapt to new storage. In this paper, we first analyze the challenges of adapting the LSM-tree for new storage. Then, we propose a high-level architecture for a new-storage-aware LSM-tree-based key-value store called Hybrid-LSM. We explain the key structural issues of different storage layers in Hybrid-LSM and present some preliminary design ideas.
{"title":"Design Considerations of A Novel Distributed Key-Value Store for New Storage","authors":"Ruicheng Liu, Peiquan Jin, Xiaoliang Wang, Yongping Luo, Zhaole Chu","doi":"10.1109/ICDCS54860.2022.00131","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00131","url":null,"abstract":"The emergence of new storage like persistent memory (PM) and zoned namespaces SSDs (ZNS-SSDs) introduces new challenges and opportunities for distributed key-value stores. Since LSM-tree has been widely adopted in distributed key-value stores, such as RocksDB and HBase, it is necessary to revisit the LSM-tree to make it adapt to new storage. In this paper, we first analyze the challenges of adapting the LSM-tree for new storage. Then, we propose a high-level architecture for a new-storage-aware LSM-tree-based key-value store called Hybrid-LSM. We explain the key structural issues of different storage layers in Hybrid-LSM and present some preliminary design ideas.","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":"124997794","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/ICDCS54860.2022.00092
Yan Li, Junming Ma, Donggang Cao, Hong Mei
As the security issue of cloud-offloaded Deep Learning (DL) inference is drawing increasing attention, running DL inference in Trusted Execution Environments (TEEs) has become a common practice. Latency prediction of TEE-hosted DL model inference is essential for many scenarios, such as DNN model architecture searching with a latency constraint or layer scheduling in model-parallelism inference. However, existing solutions fail to address the memory over-commitment issue in resource-constrained environments inside TEEs.This paper presents Sectum, an accurate latency predictor for DL inference inside TEE enclaves. We first perform a synthetic empirical study to analyze the relationship between inference latency and memory occupation. Sectum predicts inference latency following a two-stage design based on some critical observations. First, Sectum uses a Graph Neural Network (GNN)-based model to detect whether a given model would trigger memory over-commitment in TEEs. Then, combining operator-level latency modeling with linear regression, Sectum could predict the latency of a model. To evaluate Sectum, we design a large dataset that contains the latency information of over 6k CNN models. Our experiments demonstrate that Sectum could achieve over 85% ±10% accuracy of latency prediction. To our knowledge, Sectum is the first method to predict TEE-hosted DL inference latency accurately.
{"title":"Sectum: Accurate Latency Prediction for TEE-hosted Deep Learning Inference","authors":"Yan Li, Junming Ma, Donggang Cao, Hong Mei","doi":"10.1109/ICDCS54860.2022.00092","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00092","url":null,"abstract":"As the security issue of cloud-offloaded Deep Learning (DL) inference is drawing increasing attention, running DL inference in Trusted Execution Environments (TEEs) has become a common practice. Latency prediction of TEE-hosted DL model inference is essential for many scenarios, such as DNN model architecture searching with a latency constraint or layer scheduling in model-parallelism inference. However, existing solutions fail to address the memory over-commitment issue in resource-constrained environments inside TEEs.This paper presents Sectum, an accurate latency predictor for DL inference inside TEE enclaves. We first perform a synthetic empirical study to analyze the relationship between inference latency and memory occupation. Sectum predicts inference latency following a two-stage design based on some critical observations. First, Sectum uses a Graph Neural Network (GNN)-based model to detect whether a given model would trigger memory over-commitment in TEEs. Then, combining operator-level latency modeling with linear regression, Sectum could predict the latency of a model. To evaluate Sectum, we design a large dataset that contains the latency information of over 6k CNN models. Our experiments demonstrate that Sectum could achieve over 85% ±10% accuracy of latency prediction. To our knowledge, Sectum is the first method to predict TEE-hosted DL inference latency accurately.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"114 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":"127354828","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/ICDCS54860.2022.00031
Linpeng Jia, Keyuan Wang, Xin Wang, Lei Yu, Zhongcheng Li, Yi Sun
Consensus algorithm is the core component of consortium blockchains. Equality, Unpredictability and Scalability are three important demands for the consensus algorithms of consortium blockchain. Existing deterministic consensus algorithms (e.g. PBFT) can ensure Equality, but cannot meanwhile meet Unpredictability and Scalability; probabilistic consensus algorithms (e.g. PoW) can achieve Scalability and guarantee a decent Unpredictability, but cannot meet the Equality requirement. In this paper, we propose a new consensus algorithm, namely Themis, which takes the three properties into account. Themis independently adjusts the block-producing difficulty of each node through a self-adaptive node election mechanism, effectively reducing the correlation between the block-producing frequency and the invested computing power of each node. Besides, a GEOST main chain consensus rule is proposed to handle forks and further improve the performance of the algorithm. If a fork occurs, consensus nodes will choose the sub-chain with the highest Equality to join the main chain. Evaluations show that Themis achieves outstanding performance in Equality and Unpredictability while ensuring Scalability, compared with the existing algorithms.
{"title":"Themis: An Equal, Unpredictable, and Scalable Consensus for Consortium Blockchain","authors":"Linpeng Jia, Keyuan Wang, Xin Wang, Lei Yu, Zhongcheng Li, Yi Sun","doi":"10.1109/ICDCS54860.2022.00031","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00031","url":null,"abstract":"Consensus algorithm is the core component of consortium blockchains. Equality, Unpredictability and Scalability are three important demands for the consensus algorithms of consortium blockchain. Existing deterministic consensus algorithms (e.g. PBFT) can ensure Equality, but cannot meanwhile meet Unpredictability and Scalability; probabilistic consensus algorithms (e.g. PoW) can achieve Scalability and guarantee a decent Unpredictability, but cannot meet the Equality requirement. In this paper, we propose a new consensus algorithm, namely Themis, which takes the three properties into account. Themis independently adjusts the block-producing difficulty of each node through a self-adaptive node election mechanism, effectively reducing the correlation between the block-producing frequency and the invested computing power of each node. Besides, a GEOST main chain consensus rule is proposed to handle forks and further improve the performance of the algorithm. If a fork occurs, consensus nodes will choose the sub-chain with the highest Equality to join the main chain. Evaluations show that Themis achieves outstanding performance in Equality and Unpredictability while ensuring Scalability, compared with the existing algorithms.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"193 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":"114233217","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/ICDCS54860.2022.00019
Ming Chen, Renxiang Zhou, Hanhua Chen, Hai Jin
Graph stream, which represents an evolving graph updating as an infinite edge stream, is a special emerging graph data model widely adopted in big data analysis applications. Entirely storing the continuously produced and tremendously large-scale datasets is impractical. Therefore, graph stream summarization structures which support approximate graph stream storage and management attract much recent attention. Existing designs commonly leverage a compressive matrix and use hash-based schemes to map each edge to a bucket of the matrix. Accordingly, they store the edges associated with the same node in the same row or column of the matrix. We show that existing designs suffer from unacceptable query latency and precision in the presence of node degree skewness in graph streams.We argue that the key to efficient graph stream summarization is to identify the high-degree nodes and leverage a differentiated strategy for the associated edges. However, it is not trivial to estimate the degree of a node in real-time graph streams due to the rigorous requirements of space and time efficiency. Moreover, the existence of duplicate edges makes high-degree nodes identification difficult. To solve the problem, we propose Scube, an efficient summarization structure for skewed graph streams. Two factors contribute to the efficiency of Scube. First, Scube proposes a space and computation efficient probabilistic counting scheme to identify high-degree nodes in a graph stream. Second, Scube differentiates the storage strategy for the edges associated with high-degree nodes by dynamically allocating multiple rows or columns. We conduct comprehensive experiments to evaluate the performance of Scube on large-scale real-world datasets. The results show that Scube significantly reduces the query latency over a graph stream by 48%-99%, as well as achieving acceptable query accuracy compared to the state-of-the-art designs.
{"title":"Scube: Efficient Summarization for Skewed Graph Streams","authors":"Ming Chen, Renxiang Zhou, Hanhua Chen, Hai Jin","doi":"10.1109/ICDCS54860.2022.00019","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00019","url":null,"abstract":"Graph stream, which represents an evolving graph updating as an infinite edge stream, is a special emerging graph data model widely adopted in big data analysis applications. Entirely storing the continuously produced and tremendously large-scale datasets is impractical. Therefore, graph stream summarization structures which support approximate graph stream storage and management attract much recent attention. Existing designs commonly leverage a compressive matrix and use hash-based schemes to map each edge to a bucket of the matrix. Accordingly, they store the edges associated with the same node in the same row or column of the matrix. We show that existing designs suffer from unacceptable query latency and precision in the presence of node degree skewness in graph streams.We argue that the key to efficient graph stream summarization is to identify the high-degree nodes and leverage a differentiated strategy for the associated edges. However, it is not trivial to estimate the degree of a node in real-time graph streams due to the rigorous requirements of space and time efficiency. Moreover, the existence of duplicate edges makes high-degree nodes identification difficult. To solve the problem, we propose Scube, an efficient summarization structure for skewed graph streams. Two factors contribute to the efficiency of Scube. First, Scube proposes a space and computation efficient probabilistic counting scheme to identify high-degree nodes in a graph stream. Second, Scube differentiates the storage strategy for the edges associated with high-degree nodes by dynamically allocating multiple rows or columns. We conduct comprehensive experiments to evaluate the performance of Scube on large-scale real-world datasets. The results show that Scube significantly reduces the query latency over a graph stream by 48%-99%, as well as achieving acceptable query accuracy compared to the state-of-the-art designs.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"11 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":"129350334","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/ICDCS54860.2022.00091
Leijie Wu, Song Guo, Yi Liu, Zicong Hong, Yufeng Zhan, Wenchao Xu
Federated learning (FL) clients may be reluctant to participate in the energy-consuming FL unless they are incentivized. Existing incentive mechanisms seldom consider the economic properties, e.g., social welfare, individual rationality and incentive compatibility, which significantly limits the sustainability of FL to attract more clients. The Vickrey–Clarke–Groves (VCG) auction is an ideal mechanism for simultaneously guaranteeing all crucial economic properties to maximize social welfare. However, VCG auction cannot be applied directly to FL scenarios due to the following challenges: 1) It requires precise analytical derivation of the optimal strategy, which is unavailable due to the inherent model-unknown and privacy-sensitive characteristics of FL. 2) Current auction modeling decomposes the entire process into multiple independent rounds and solves them one-by-one, which breaks the successive correlation between rounds in the long-term training process of FL. To overcome these challenges, this paper presents a long-term online VCG auction mechanism for FL that employs an experience-driven deep reinforcement learning algorithm to obtain the optimal strategy. Besides, we extend long-term forms of the crucial economic properties for the successive FL process. Furthermore, knowledge transfer is applied to reduce the excessive training overhead arising from the VCG payment rules. By exploiting the environmental similarity among sub-auctions, we develop the strategy sharing to significantly cut the training time by half. Finally, we theoretically prove the extended economic properties and conduct extensive experiments on multiple real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL increases by 36% with a 37% reduction in payment.
{"title":"Sustainable Federated Learning with Long-term Online VCG Auction Mechanism","authors":"Leijie Wu, Song Guo, Yi Liu, Zicong Hong, Yufeng Zhan, Wenchao Xu","doi":"10.1109/ICDCS54860.2022.00091","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00091","url":null,"abstract":"Federated learning (FL) clients may be reluctant to participate in the energy-consuming FL unless they are incentivized. Existing incentive mechanisms seldom consider the economic properties, e.g., social welfare, individual rationality and incentive compatibility, which significantly limits the sustainability of FL to attract more clients. The Vickrey–Clarke–Groves (VCG) auction is an ideal mechanism for simultaneously guaranteeing all crucial economic properties to maximize social welfare. However, VCG auction cannot be applied directly to FL scenarios due to the following challenges: 1) It requires precise analytical derivation of the optimal strategy, which is unavailable due to the inherent model-unknown and privacy-sensitive characteristics of FL. 2) Current auction modeling decomposes the entire process into multiple independent rounds and solves them one-by-one, which breaks the successive correlation between rounds in the long-term training process of FL. To overcome these challenges, this paper presents a long-term online VCG auction mechanism for FL that employs an experience-driven deep reinforcement learning algorithm to obtain the optimal strategy. Besides, we extend long-term forms of the crucial economic properties for the successive FL process. Furthermore, knowledge transfer is applied to reduce the excessive training overhead arising from the VCG payment rules. By exploiting the environmental similarity among sub-auctions, we develop the strategy sharing to significantly cut the training time by half. Finally, we theoretically prove the extended economic properties and conduct extensive experiments on multiple real-world datasets. Compared with state-of-the-art approaches, the long-term social welfare of FL increases by 36% with a 37% reduction in payment.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"1 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":"130191334","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}
Bitcoin P2P networking is especially vulnerable to networking threats because it is permissionless and does not have the security protections based on the trust in identities, which enables the attackers to manipulate the identities for Sybil and spoofing attacks. The Bitcoin node keeps track of its peer’s networking misbehaviors through ban scores. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS (BM-DoS) attacks but also vulnerable to the Defamation attack as the network adversary can exploit the ban score to defame innocent peers. To defend against these threats, we design an anomaly detection approach that is effective, lightweight, and tailored to the networking threats exploiting Bitcoin’s ban-score mechanism. We prototype our threat discoveries against a real-world Bitcoin node connected to the Bitcoin Mainnet and conduct experiments based on the prototype implementation. The experimental results show that the attacks have devastating impacts on the targeted victim while being cost-effective on the attacker side. For example, an attacker can ban a peer in two milliseconds and reduce the victim’s mining rate by hundreds of thousands of hash computations per second. Furthermore, to counter the threats, we empirically validate our detection countermeasure’s effectiveness and performances against the BM-DoS and Defamation attacks.
{"title":"The Security Investigation of Ban Score and Misbehavior Tracking in Bitcoin Network","authors":"Wenjun Fan, Simeon Wuthier, Hsiang-Jen Hong, Xiaobo Zhou, Yan Bai, Sang-Yoon Chang","doi":"10.1109/ICDCS54860.2022.00027","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00027","url":null,"abstract":"Bitcoin P2P networking is especially vulnerable to networking threats because it is permissionless and does not have the security protections based on the trust in identities, which enables the attackers to manipulate the identities for Sybil and spoofing attacks. The Bitcoin node keeps track of its peer’s networking misbehaviors through ban scores. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS (BM-DoS) attacks but also vulnerable to the Defamation attack as the network adversary can exploit the ban score to defame innocent peers. To defend against these threats, we design an anomaly detection approach that is effective, lightweight, and tailored to the networking threats exploiting Bitcoin’s ban-score mechanism. We prototype our threat discoveries against a real-world Bitcoin node connected to the Bitcoin Mainnet and conduct experiments based on the prototype implementation. The experimental results show that the attacks have devastating impacts on the targeted victim while being cost-effective on the attacker side. For example, an attacker can ban a peer in two milliseconds and reduce the victim’s mining rate by hundreds of thousands of hash computations per second. Furthermore, to counter the threats, we empirically validate our detection countermeasure’s effectiveness and performances against the BM-DoS and Defamation attacks.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"38 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":"130457261","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/ICDCS54860.2022.00063
Xinwen Wang, R. V. Renesse
Mobile Ad Hoc Networks are important today for scenarios in which centralized cloud infrastructure is missing, has broken down, or imposes censure or undesirable monitoring of storage and communication. Unfortunately, existing peer-to-peer storage systems such as a Geographic Hash Table (GHT) can consume a significant amount of network bandwidth just to maintain a certain required number of replicas of the data due to the churn present in the network. The replicas have to continuously exchange heartbeat messages in order to detect failures of replicas. If heartbeat messages get lost, an unnecessary but expensive recovery protocol ends up wasting significant bandwidth. To avoid this, replicas are placed close to one another, but that makes them vulnerable to dependent failures.Based on Mergeable Bloom Filters, a new data structure proposed for peer-to-peer distributed systems, we build BloomBox, a failure detection protocol for a GHT. Our simulations show that BloomBox can significantly reduce bandwidth usage needed for regenerated blocks compared to heartbeat-based failure detection. Moreover, BloomBox can provide significantly better availability than protocols based on heartbeats by placing replicas in geographically diverse locations.
{"title":"BloomBox: Improving Availability and Efficiency in Geographic Hash Tables","authors":"Xinwen Wang, R. V. Renesse","doi":"10.1109/ICDCS54860.2022.00063","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00063","url":null,"abstract":"Mobile Ad Hoc Networks are important today for scenarios in which centralized cloud infrastructure is missing, has broken down, or imposes censure or undesirable monitoring of storage and communication. Unfortunately, existing peer-to-peer storage systems such as a Geographic Hash Table (GHT) can consume a significant amount of network bandwidth just to maintain a certain required number of replicas of the data due to the churn present in the network. The replicas have to continuously exchange heartbeat messages in order to detect failures of replicas. If heartbeat messages get lost, an unnecessary but expensive recovery protocol ends up wasting significant bandwidth. To avoid this, replicas are placed close to one another, but that makes them vulnerable to dependent failures.Based on Mergeable Bloom Filters, a new data structure proposed for peer-to-peer distributed systems, we build BloomBox, a failure detection protocol for a GHT. Our simulations show that BloomBox can significantly reduce bandwidth usage needed for regenerated blocks compared to heartbeat-based failure detection. Moreover, BloomBox can provide significantly better availability than protocols based on heartbeats by placing replicas in geographically diverse locations.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"10 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":"133953467","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/ICDCS54860.2022.00128
K. Rajashekar
For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT devices and the edge devices, we need a sophisticated approach for assignment IoT devices to the edge devices. Most of the heuristics solutions previously used to tackle the problem faced issues being solution stuck at local optima and high computational over head. To that end, researchers used reinforcement learning (RL) algorithms to explore the search space to get near optimal solutions. For our work, we consider RL based algorithms and show the preliminary results.
{"title":"Reinforcement Learning for Minimizing Communication Delay in Edge Computing","authors":"K. Rajashekar","doi":"10.1109/ICDCS54860.2022.00128","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00128","url":null,"abstract":"For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. In order to minimize the communication delay between the IoT devices and the edge devices, we need a sophisticated approach for assignment IoT devices to the edge devices. Most of the heuristics solutions previously used to tackle the problem faced issues being solution stuck at local optima and high computational over head. To that end, researchers used reinforcement learning (RL) algorithms to explore the search space to get near optimal solutions. For our work, we consider RL based algorithms and show the preliminary results.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"9 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":"134461705","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/ICDCS54860.2022.00085
Shuxin Zhong, Wenjun Lyu, Desheng Zhang, Yu Yang
Given the recent global development of bike-sharing systems, numerous methods have been proposed to predict their user demand. These methods work fine for single-step prediction (i.e., 10 mins) but are limited to predicting in a multi-step prediction (i.e., more than 60 mins), which is essential for applications such as bike re-balancing that requires long operation time. To address this limitation, we leverage the fact that the demand for upstream transportation, e.g., subways, can assist the future demand prediction of downstream transportation, e.g., bikes. Specifically, we design a deep spatial-temporal capsule network called BikeCAP with three components: (1) a historical capsule that learns the demand characteristics for both the upstream (i.e., subways) and downstream (i.e., bikes) transportation systems, where a pyramid convolutional layer explores the simultaneous spatial-temporal correlations; (2) a future capsule that actively captures the dynamic spatial-temporal propagation correlations from the upstream to the downstream system, in which a spatial-temporal routing technique benefits to reduce the accumulated prediction errors; (3) a 3D-deconvolution decoder that constructs future bike demand considering the similar downstream demand patterns in neighboring grids and adjacent time slots. Experimentally, we conduct comprehensive experiments on the data of 30, 000 bikes and 7 subway lines collected in Shenzhen City, China, The results show that BikeCAP outperforms several state-of-the-art methods, significantly increasing the performance by 38.6% in terms of accuracy in multi-step prediction. We also conduct ablation studies to show the significance of BikeCAP’s different designed components.
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