Pub Date : 2022-07-01DOI: 10.1109/ICDCS54860.2022.00144
K. Rajashekar, Souradyuti Paul, S. Karmakar, Subhajit Sidhanta
For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. Since the generalized assignment problem being NP-Hard, an optimal assignment of IoT devices to the edge cluster is hard. We propose the application RL based heuristics to obtain a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge devices are overloaded. We demonstrate that our algorithm outperforms the state-of-the-art.
{"title":"Topology Aware Cluster Configuration for Minimizing Communication Delay in Edge Computing","authors":"K. Rajashekar, Souradyuti Paul, S. Karmakar, Subhajit Sidhanta","doi":"10.1109/ICDCS54860.2022.00144","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00144","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. Since the generalized assignment problem being NP-Hard, an optimal assignment of IoT devices to the edge cluster is hard. We propose the application RL based heuristics to obtain a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge devices are overloaded. We demonstrate that our algorithm outperforms the state-of-the-art.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"36 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":"132036327","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/ICDCS54860.2022.00060
Tuo Shi, Zhipeng Cai, Yingshu Li
Multi-access Edge Computing is an important technique in the Internet of Things (IoT). It can help people observe the physical world by caching IoT data at an edge server and provide data query services. In this paper, we investigate how to process numerous concurrent top-k queries on an edge server. Since the computation resource of an edge server is limited and costly, processing concurrent top-k queries in the edge is totally different from that in the cloud. Researchers always focus on reducing time/space complexity of processing single top-k query in the cloud. However, how to process numerous top-k queries on an edge server in a cost-efficient manner still remains an open problem. In order to solve the problem, we propose the query recombination concept which aims at using the correlation of queries to reduce resource consumption of query processing. By adopting query recombination, we can make use of a small set of queries to answer the other queries and reduce resource consumption as well. We prove that constructing an optimal query recombination is NP-hard. Three approximate algorithms are proposed accordingly. Simulations are carried out to evaluate the performance of the proposed algorithms further, and the results show that the proposed algorithms are effective and efficient.
{"title":"Query Recombination: To Process a Large Number of Concurrent Top-k Queries towards IoT Data on an Edge Server","authors":"Tuo Shi, Zhipeng Cai, Yingshu Li","doi":"10.1109/ICDCS54860.2022.00060","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00060","url":null,"abstract":"Multi-access Edge Computing is an important technique in the Internet of Things (IoT). It can help people observe the physical world by caching IoT data at an edge server and provide data query services. In this paper, we investigate how to process numerous concurrent top-k queries on an edge server. Since the computation resource of an edge server is limited and costly, processing concurrent top-k queries in the edge is totally different from that in the cloud. Researchers always focus on reducing time/space complexity of processing single top-k query in the cloud. However, how to process numerous top-k queries on an edge server in a cost-efficient manner still remains an open problem. In order to solve the problem, we propose the query recombination concept which aims at using the correlation of queries to reduce resource consumption of query processing. By adopting query recombination, we can make use of a small set of queries to answer the other queries and reduce resource consumption as well. We prove that constructing an optimal query recombination is NP-hard. Three approximate algorithms are proposed accordingly. Simulations are carried out to evaluate the performance of the proposed algorithms further, and the results show that the proposed algorithms are effective and efficient.","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":"128255593","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/ICDCS54860.2022.00030
Rui Song, Shang Gao, Yubo Song, Bin Xiao
With the advent of the Big Data era, industry, business and academia have developed various data exchange schemes to make data more economically beneficial. Unfortunately, most of the existing systems provide only one-time data exchanges without the ability to track the provenance and transformations of datasets. In addition, existing systems encrypt the data to protect data privacy, which hinders demanders from verifying the correctness of the data and evaluating its value.To provide data traceability and privacy while ensuring fairness during data exchanges, we design and implement ZKDET, a traceable data exchange scheme based on non-fungible token and zero-knowledge, which is able to (i) track all transformations of data during their lifecycle and record them on the blockchain; (ii) provide zero-knowledge proofs to securely guarantee that all complex transformations and data contents are correct and meet specific requirements; and (iii) warrant exchange fairness and data privacy in public storage platforms. Security analysis and evaluations on ZKDET show that it can support traceable data exchange while preserving data privacy and maintaining high throughput despite large data volumes.
{"title":": A Traceable and Privacy-Preserving Data Exchange Scheme based on Non-Fungible Token and Zero-Knowledge","authors":"Rui Song, Shang Gao, Yubo Song, Bin Xiao","doi":"10.1109/ICDCS54860.2022.00030","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00030","url":null,"abstract":"With the advent of the Big Data era, industry, business and academia have developed various data exchange schemes to make data more economically beneficial. Unfortunately, most of the existing systems provide only one-time data exchanges without the ability to track the provenance and transformations of datasets. In addition, existing systems encrypt the data to protect data privacy, which hinders demanders from verifying the correctness of the data and evaluating its value.To provide data traceability and privacy while ensuring fairness during data exchanges, we design and implement ZKDET, a traceable data exchange scheme based on non-fungible token and zero-knowledge, which is able to (i) track all transformations of data during their lifecycle and record them on the blockchain; (ii) provide zero-knowledge proofs to securely guarantee that all complex transformations and data contents are correct and meet specific requirements; and (iii) warrant exchange fairness and data privacy in public storage platforms. Security analysis and evaluations on ZKDET show that it can support traceable data exchange while preserving data privacy and maintaining high throughput despite large data volumes.","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":"116613074","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/ICDCS54860.2022.00113
C. Zhang, Qingyuan Xie, Yinbin Miao, Xiaohua Jia
Key-value store is adopted by many applications due to its high performance in processing big data workloads. Recent research on secure cloud storage has shown that even if the data is encrypted, attackers can learn the sensitive information of data by launching access pattern attacks such as frequency analysis. For this issue, some schemes have been proposed to protect encrypted key-value stores against access pattern attacks. However, existing solutions protect access pattern information at the cost of large storage and bandwidth overhead, which is unacceptable for large-scale key-value stores. In this paper, we devise a K-indistinguishable frequency smoothing scheme for encrypted key-value stores, which can resist access pattern attacks launched by passive persistent adversaries with minimal storage and bandwidth overhead. Then, we propose a dynamic K-indistinguishable frequency smoothing scheme. It can efficiently adapt to the changes in access distribution while ensuring the K-indistinguishable security level and bandwidth efficiency. Finally, we formally analyze the security of our design. Extensive experiments demonstrate that our design achieves high throughput while minimizing storage and bandwidth overhead.
{"title":"K-Indistinguishable Data Access for Encrypted Key-Value Stores","authors":"C. Zhang, Qingyuan Xie, Yinbin Miao, Xiaohua Jia","doi":"10.1109/ICDCS54860.2022.00113","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00113","url":null,"abstract":"Key-value store is adopted by many applications due to its high performance in processing big data workloads. Recent research on secure cloud storage has shown that even if the data is encrypted, attackers can learn the sensitive information of data by launching access pattern attacks such as frequency analysis. For this issue, some schemes have been proposed to protect encrypted key-value stores against access pattern attacks. However, existing solutions protect access pattern information at the cost of large storage and bandwidth overhead, which is unacceptable for large-scale key-value stores. In this paper, we devise a K-indistinguishable frequency smoothing scheme for encrypted key-value stores, which can resist access pattern attacks launched by passive persistent adversaries with minimal storage and bandwidth overhead. Then, we propose a dynamic K-indistinguishable frequency smoothing scheme. It can efficiently adapt to the changes in access distribution while ensuring the K-indistinguishable security level and bandwidth efficiency. Finally, we formally analyze the security of our design. Extensive experiments demonstrate that our design achieves high throughput while minimizing storage and bandwidth overhead.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"263 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":"122380029","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/ICDCS54860.2022.00082
Yanling Bu, Linfu Xie, Jia Liu, Chuyu Wang, Ge Wang, Zenglong Wang, Sanglu Lu
In the context of Industrial Internet of Things (IIoT), RFID technologies have been widely applied to locate or track tagged objects for achieving item-level intelligence. However, prior localization work encounters two main issues. First, the phase measurement usually contains physical deviation. Existing localization work generally takes the physical center of an RFID antenna as its phase center, which is a key factor in improving localization accuracy but actually different from the physical center in practice. Second, the non-linear localization model is likely to be too complex to run on edge nodes with limited computing resources. In this paper, we present a LInear localizatiON solution, called LION, to perform the phase calibration for antennas with no need for the complex computation nor strong limitations. Specifically, we provide a novel lightweight model to pinpoint the actual antenna position quickly and accurately. Compared to previous localization methods, we reduce the intersection of circles or hyperbolas into radical lines, which greatly reduces the computation cost while guaranteeing the high accuracy. Further, to adapt to the complex environment with various ambient noise and multi-path effect, we leverage the weighted least square method to determine the optimal position. Moreover, we propose an adaptive parameter selection scheme to automatically choose optimal parameters for localization. In this way, LION is able to perform the accurate localization robustly. We implement LION using commercial RFID devices, and evaluate its performance extensively. Experimental results show the necessity of phase calibration as well as the high time efficiency of LION, e.g., the average accuracy improves by 6× and 2.1× for 2D and 3D localization, and the average time consuming is 0.02s and 1.8s for 2D and 3D cases.
{"title":"Pinpoint Achilles’ Heel in RFID Localization: Phase Calibration of RFID Antenna based on Linear Localization Model","authors":"Yanling Bu, Linfu Xie, Jia Liu, Chuyu Wang, Ge Wang, Zenglong Wang, Sanglu Lu","doi":"10.1109/ICDCS54860.2022.00082","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00082","url":null,"abstract":"In the context of Industrial Internet of Things (IIoT), RFID technologies have been widely applied to locate or track tagged objects for achieving item-level intelligence. However, prior localization work encounters two main issues. First, the phase measurement usually contains physical deviation. Existing localization work generally takes the physical center of an RFID antenna as its phase center, which is a key factor in improving localization accuracy but actually different from the physical center in practice. Second, the non-linear localization model is likely to be too complex to run on edge nodes with limited computing resources. In this paper, we present a LInear localizatiON solution, called LION, to perform the phase calibration for antennas with no need for the complex computation nor strong limitations. Specifically, we provide a novel lightweight model to pinpoint the actual antenna position quickly and accurately. Compared to previous localization methods, we reduce the intersection of circles or hyperbolas into radical lines, which greatly reduces the computation cost while guaranteeing the high accuracy. Further, to adapt to the complex environment with various ambient noise and multi-path effect, we leverage the weighted least square method to determine the optimal position. Moreover, we propose an adaptive parameter selection scheme to automatically choose optimal parameters for localization. In this way, LION is able to perform the accurate localization robustly. We implement LION using commercial RFID devices, and evaluate its performance extensively. Experimental results show the necessity of phase calibration as well as the high time efficiency of LION, e.g., the average accuracy improves by 6× and 2.1× for 2D and 3D localization, and the average time consuming is 0.02s and 1.8s for 2D and 3D cases.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"19 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":"130046286","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/ICDCS54860.2022.00051
Qiushi Li, Ju Ren, Xinglin Pan, Yuezhi Zhou, Yaoxue Zhang
Time-efficient artificial intelligence (AI) service has recently witnessed increasing interest from academia and industry due to the urgent needs in massive smart applications such as self-driving cars, virtual reality, high-resolution video streaming, etc. Existing solutions to reduce AI latency, like edge computing and heterogeneous neural-network accelerators (NNAs), face high risk of privacy leakage. To achieve both low-latency and privacy-preserving purposes on edge servers (e.g., NNAs), this paper proposes ENIGMA that can exploit the trusted execution environment (TEE) and heterogeneous NNAs of edge servers for edge inference. The low-latency is supported by a new ahead-of-time analysis framework for analyzing the linearity of multilayer neural networks, which automatically slices forward-graph and assigns sub-graphs to TEE or NNA. To avoid privacy leakage issue, we then introduce a pre-forwarded cipher generation (PFCG) scheme for computing linear sub-forward-graphs on NNA. The input data is encrypted to ciphertext that can be computed directly by linear sub-graphs, and the output can be decrypted to obtain the correct output. To enable non-linear computation of sub-graphs on TEE, we use ring-cache and automatic vectorization optimization to address the memory limitation of TEE. Qualitative analysis and quantitative experiments on GPU, NPU and TPU demonstrate that ENIGMA is not only compatible with heterogeneous NNAs, but also can avoid leakages of private features with latency as low as 50-milliseconds.
{"title":"ENIGMA: Low-Latency and Privacy-Preserving Edge Inference on Heterogeneous Neural Network Accelerators","authors":"Qiushi Li, Ju Ren, Xinglin Pan, Yuezhi Zhou, Yaoxue Zhang","doi":"10.1109/ICDCS54860.2022.00051","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00051","url":null,"abstract":"Time-efficient artificial intelligence (AI) service has recently witnessed increasing interest from academia and industry due to the urgent needs in massive smart applications such as self-driving cars, virtual reality, high-resolution video streaming, etc. Existing solutions to reduce AI latency, like edge computing and heterogeneous neural-network accelerators (NNAs), face high risk of privacy leakage. To achieve both low-latency and privacy-preserving purposes on edge servers (e.g., NNAs), this paper proposes ENIGMA that can exploit the trusted execution environment (TEE) and heterogeneous NNAs of edge servers for edge inference. The low-latency is supported by a new ahead-of-time analysis framework for analyzing the linearity of multilayer neural networks, which automatically slices forward-graph and assigns sub-graphs to TEE or NNA. To avoid privacy leakage issue, we then introduce a pre-forwarded cipher generation (PFCG) scheme for computing linear sub-forward-graphs on NNA. The input data is encrypted to ciphertext that can be computed directly by linear sub-graphs, and the output can be decrypted to obtain the correct output. To enable non-linear computation of sub-graphs on TEE, we use ring-cache and automatic vectorization optimization to address the memory limitation of TEE. Qualitative analysis and quantitative experiments on GPU, NPU and TPU demonstrate that ENIGMA is not only compatible with heterogeneous NNAs, but also can avoid leakages of private features with latency as low as 50-milliseconds.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"84 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":"126208392","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/ICDCS54860.2022.00072
Yi Zhao, Zheng Yang, Xiaowu He, Jiahang Wu, Hao Cao, Liang Dong, Fan Dang, Yunhao Liu
Time-Sensitive Networking (TSN) is the most promising network technology for Industry 4.0. A series of IEEE standards on TSN introduce deterministic transmission into standard Ethernet. Under the current paradigm, TSN can only schedule the deterministic transmission of time-triggered critical traffic (TCT), neglecting the other type of traffic in industrial cyber physical systems, i.e., event-triggered critical traffic (ECT). So in this work, we propose a new paradigm for TSN scheduling named E-TSN, which can provide deterministic transmission for both TCT and ECT. The three techniques of E-TSN, i.e., probabilistic stream, prioritized slot sharing, and prudent reservation, enable the deterministic transmission of ECT in TSN, and at the same time, protect TCT from the impacts of ECT. We also develop and make public a TSN evaluation toolkit to fill the gap in TSN study between algorithm design and experimental validation. The experiments show that E-TSN can reduce the latency and jitter of ECT by at least an order of magnitude compared to state-of-the-art methods. By enabling reliable and timely delivery of ECT in TSN for the first time, E-TSN can broaden the application scope of TSN in industry.
{"title":"E-TSN: Enabling Event-triggered Critical Traffic in Time-Sensitive Networking for Industrial Applications","authors":"Yi Zhao, Zheng Yang, Xiaowu He, Jiahang Wu, Hao Cao, Liang Dong, Fan Dang, Yunhao Liu","doi":"10.1109/ICDCS54860.2022.00072","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00072","url":null,"abstract":"Time-Sensitive Networking (TSN) is the most promising network technology for Industry 4.0. A series of IEEE standards on TSN introduce deterministic transmission into standard Ethernet. Under the current paradigm, TSN can only schedule the deterministic transmission of time-triggered critical traffic (TCT), neglecting the other type of traffic in industrial cyber physical systems, i.e., event-triggered critical traffic (ECT). So in this work, we propose a new paradigm for TSN scheduling named E-TSN, which can provide deterministic transmission for both TCT and ECT. The three techniques of E-TSN, i.e., probabilistic stream, prioritized slot sharing, and prudent reservation, enable the deterministic transmission of ECT in TSN, and at the same time, protect TCT from the impacts of ECT. We also develop and make public a TSN evaluation toolkit to fill the gap in TSN study between algorithm design and experimental validation. The experiments show that E-TSN can reduce the latency and jitter of ECT by at least an order of magnitude compared to state-of-the-art methods. By enabling reliable and timely delivery of ECT in TSN for the first time, E-TSN can broaden the application scope of TSN in industry.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"60 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":"115238943","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 innovation and evolution of file and storage systems have been influenced by workload analysis. Though cloud storage systems have been widely deployed and used, real-world and large-scale cloud storage workload studies are rare. Previous large-scale distributed storage systems can meet versatility, stability, and reliability requirements. Furthermore, modern cloud storage systems need to meet additional challenges, such as coping with surges in peak loads and rapid expansion of requests. These changes may lead to different characteristics.In this work, we propose DiTing data tracing system and collect workloads with over 242,000 billion requests from the Alibaba cloud. By comparing the normal days and the Single’s Day (the world’s largest online shopping festival), we analyze characteristics such as I/O scale, latency, locality, and load distribution. Our analysis reveals four key observations as follows. First, the virtual layer is the performance bottleneck of modern cloud storage systems during extreme peak periods. Second, the write operations dominate the data access because the application and operating system buffers absorb reads better than writes. Third, the workload is heavily skewed toward a small percentage of virtual cloud disks, with 20% of cloud disks accounting for 80% of I/O requests. Finally, data access shows poor temporal and spatial locality, and the I/O requests are mostly small-scaled. Based on these observations, we propose several suggestions for cloud storage systems, including separating I/O processing from the virtual layer to the proxy layer, deploying heavy and light workload applications on the same node, and adopting a write-friendly cloud disk design for write-skewed requests, etc. In summary, these workload characteristics and suggestions are useful for designing and implementing next-generation cloud storage systems.
{"title":"Dissecting the Workload of Cloud Storage System","authors":"Yaodanjun Ren, Xiaoyi Sun, Kai Li, Jiale Lin, Shuzhi Feng, Zhenyu Ren, Jian Yin, Zhengwei Qi","doi":"10.1109/ICDCS54860.2022.00068","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00068","url":null,"abstract":"The innovation and evolution of file and storage systems have been influenced by workload analysis. Though cloud storage systems have been widely deployed and used, real-world and large-scale cloud storage workload studies are rare. Previous large-scale distributed storage systems can meet versatility, stability, and reliability requirements. Furthermore, modern cloud storage systems need to meet additional challenges, such as coping with surges in peak loads and rapid expansion of requests. These changes may lead to different characteristics.In this work, we propose DiTing data tracing system and collect workloads with over 242,000 billion requests from the Alibaba cloud. By comparing the normal days and the Single’s Day (the world’s largest online shopping festival), we analyze characteristics such as I/O scale, latency, locality, and load distribution. Our analysis reveals four key observations as follows. First, the virtual layer is the performance bottleneck of modern cloud storage systems during extreme peak periods. Second, the write operations dominate the data access because the application and operating system buffers absorb reads better than writes. Third, the workload is heavily skewed toward a small percentage of virtual cloud disks, with 20% of cloud disks accounting for 80% of I/O requests. Finally, data access shows poor temporal and spatial locality, and the I/O requests are mostly small-scaled. Based on these observations, we propose several suggestions for cloud storage systems, including separating I/O processing from the virtual layer to the proxy layer, deploying heavy and light workload applications on the same node, and adopting a write-friendly cloud disk design for write-skewed requests, etc. In summary, these workload characteristics and suggestions are useful for designing and implementing next-generation cloud storage systems.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"24 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":"132658206","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}
Blind area has plagued drivers’ safety ever since the dawn of automobiles. Thanks to the fast-growing vision-based perception technologies, autonomous driving systems can monitor the driving circumstance through a 360-degree view, and hence most blind areas can be avoided. However, in the left turn scenario at an intersection, the opposite road may be blocked by another vehicle parking at the same intersection (see Fig. 1), and in this case, the blind area cannot be observed by the onboard perception module of the autonomous vehicle. A potential fatal collision may occur if the autonomous vehicle turns left while a vehicle is running through the blind area. In this paper, we propose Safecross, a framework that oversees an intersection and delivers blind area warnings to the left-turn vehicles at the intersection if running vehicles are detected in the blind area. In order to provide accurate and reliable real-time warnings in all possible weather conditions, the architecture of Safecross has four major components: video pre-processing (VP) module, video classification (VC) module, few-shot learning (FL) module, and model switching (MS) module. Especially, the VP and VC modules will train a basic model to identify the blind area when a blocking vehicle appears at the intersection. Since the range of the blind area varies in different weather conditions, the FL and MS modules can adapt the basic model to the new condition in real-time to make the blind area identification more accurate. Intuitively, if the blind area is identified timely and accurately, the left-turn throughput of the intersection can be maximized. We have conducted extensive experiments to evaluate our proposed framework. The experiments are performed on a total of 2855 video segments with a time span of 180 days, including sunny, rainy, and snowy weather conditions. Experimental results show how Safecross can guarantee the vehicle’s safety while increasing the left-turn traffic throughput by 50%.
{"title":"To Turn or Not To Turn, SafeCross is the Answer","authors":"Baofu Wu, Yuankai He, Zheng Dong, Jian Wan, Jilin Zhang, Weisong Shi","doi":"10.1109/ICDCS54860.2022.00047","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00047","url":null,"abstract":"Blind area has plagued drivers’ safety ever since the dawn of automobiles. Thanks to the fast-growing vision-based perception technologies, autonomous driving systems can monitor the driving circumstance through a 360-degree view, and hence most blind areas can be avoided. However, in the left turn scenario at an intersection, the opposite road may be blocked by another vehicle parking at the same intersection (see Fig. 1), and in this case, the blind area cannot be observed by the onboard perception module of the autonomous vehicle. A potential fatal collision may occur if the autonomous vehicle turns left while a vehicle is running through the blind area. In this paper, we propose Safecross, a framework that oversees an intersection and delivers blind area warnings to the left-turn vehicles at the intersection if running vehicles are detected in the blind area. In order to provide accurate and reliable real-time warnings in all possible weather conditions, the architecture of Safecross has four major components: video pre-processing (VP) module, video classification (VC) module, few-shot learning (FL) module, and model switching (MS) module. Especially, the VP and VC modules will train a basic model to identify the blind area when a blocking vehicle appears at the intersection. Since the range of the blind area varies in different weather conditions, the FL and MS modules can adapt the basic model to the new condition in real-time to make the blind area identification more accurate. Intuitively, if the blind area is identified timely and accurately, the left-turn throughput of the intersection can be maximized. We have conducted extensive experiments to evaluate our proposed framework. The experiments are performed on a total of 2855 video segments with a time span of 180 days, including sunny, rainy, and snowy weather conditions. Experimental results show how Safecross can guarantee the vehicle’s safety while increasing the left-turn traffic throughput by 50%.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"40 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":"114751248","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/ICDCS54860.2022.00149
Yigui Yuan, Peiquan Jin, Shouhong Wan
Cloud services such as multi-tenant content delivery networks (CDN) have become a trend because they can offer personalized business support for users. However, as the business modes of different users are usually different, it is not appropriate to use a single cache strategy on cloud servers. First, a single cache cannot adapt to various access patterns of tenants. Second, a single cache will also affect the isolation among different users. In this paper, we propose MyCache, a new framework to deliver personalized cache management for multi-tenant cloud services. We first discuss the impact of access patterns, which motivates MyCache. Then, we briefly introduce the architecture of MyCache, and finally, we present preliminary experimental results to show the feasibility and superiority of our proposal.
{"title":"Personalized Cache Management for Multi-Tenant Cloud Services","authors":"Yigui Yuan, Peiquan Jin, Shouhong Wan","doi":"10.1109/ICDCS54860.2022.00149","DOIUrl":"https://doi.org/10.1109/ICDCS54860.2022.00149","url":null,"abstract":"Cloud services such as multi-tenant content delivery networks (CDN) have become a trend because they can offer personalized business support for users. However, as the business modes of different users are usually different, it is not appropriate to use a single cache strategy on cloud servers. First, a single cache cannot adapt to various access patterns of tenants. Second, a single cache will also affect the isolation among different users. In this paper, we propose MyCache, a new framework to deliver personalized cache management for multi-tenant cloud services. We first discuss the impact of access patterns, which motivates MyCache. Then, we briefly introduce the architecture of MyCache, and finally, we present preliminary experimental results to show the feasibility and superiority of our proposal.","PeriodicalId":225883,"journal":{"name":"2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)","volume":"18 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":"124344557","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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