Over the past few years, blockchains have captured the public's interest with the promise of pseudo-anonymous decentralized exchange infrastructures. However, their potential is hindered by various technical issues, such as their ability to scale with problematic storage and communication costs and a fairly low transaction throughput. In this paper, we present SplitChain, a protocol intended to support the creation of scalable account-based blockchains without undermining decentralization and security. This is achieved by using sharding, i.e. by splitting the blockchain into several lighter chains managed by their own disjoint sets of validators called shards. These shards balance the load by processing disjoint sets of transactions in parallel. SplitChain distinguishes itself from other sharded blockchains by minimizing the synchronization constraints among shards while maintaining security guarantees. Finally, the protocol is designed to dynamically adapt the number of shards to the system load, to avoid over-dimensioning issues of most of the existing sharding-based solutions where the number of shards is static.
{"title":"Student Research Abstract: SplitChain: Blockchain with fully decentralized dynamic sharding resilient to fast adaptive adversaries","authors":"Arthur Rauch","doi":"10.1145/3555776.3577207","DOIUrl":"https://doi.org/10.1145/3555776.3577207","url":null,"abstract":"Over the past few years, blockchains have captured the public's interest with the promise of pseudo-anonymous decentralized exchange infrastructures. However, their potential is hindered by various technical issues, such as their ability to scale with problematic storage and communication costs and a fairly low transaction throughput. In this paper, we present SplitChain, a protocol intended to support the creation of scalable account-based blockchains without undermining decentralization and security. This is achieved by using sharding, i.e. by splitting the blockchain into several lighter chains managed by their own disjoint sets of validators called shards. These shards balance the load by processing disjoint sets of transactions in parallel. SplitChain distinguishes itself from other sharded blockchains by minimizing the synchronization constraints among shards while maintaining security guarantees. Finally, the protocol is designed to dynamically adapt the number of shards to the system load, to avoid over-dimensioning issues of most of the existing sharding-based solutions where the number of shards is static.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"5 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89892912","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}
Heithem Sliman, I. Megdiche, Sami Yangui, Aida Drira, Ines Drira, E. Lamine
Artificial Intelligence (AI) has undergone considerable development in recent years in the field of medicine and in particular in decision support diagnostic. However, the development of such algorithms depends on the presence of a sufficiently large amount of data to provide reliable results. Unfortunately in medicine, it is not always possible to provide so much data on all pathologies. This problem is particularly true for rare diseases. In this paper we focus on uveitis, a rare disease in ophthalmology which is the third cause of blindness worldwide. This pathology is difficult to diagnose because of the disparity in prevalence of its etiologies. In order to provide physicians with a diagnostic aid system, it would be necessary to have a representative dataset of epidemiological profiles that have been studied for a long time in this domain. This work proposes a breakthrough in this field by suggesting a methodological framework for the generation of an open source dataset based on the crossing of several epidemiological profiles and using data augmentation techniques. The results of these generated synthetic data have been qualitatively validated by specialist physicians in ophthalmology. Our results are very promising and consist in a first brick to promote research in AI on Uveitis disease.
{"title":"A Synthetic Dataset Generation for the Uveitis Pathology Based on MedWGAN Model","authors":"Heithem Sliman, I. Megdiche, Sami Yangui, Aida Drira, Ines Drira, E. Lamine","doi":"10.1145/3555776.3577648","DOIUrl":"https://doi.org/10.1145/3555776.3577648","url":null,"abstract":"Artificial Intelligence (AI) has undergone considerable development in recent years in the field of medicine and in particular in decision support diagnostic. However, the development of such algorithms depends on the presence of a sufficiently large amount of data to provide reliable results. Unfortunately in medicine, it is not always possible to provide so much data on all pathologies. This problem is particularly true for rare diseases. In this paper we focus on uveitis, a rare disease in ophthalmology which is the third cause of blindness worldwide. This pathology is difficult to diagnose because of the disparity in prevalence of its etiologies. In order to provide physicians with a diagnostic aid system, it would be necessary to have a representative dataset of epidemiological profiles that have been studied for a long time in this domain. This work proposes a breakthrough in this field by suggesting a methodological framework for the generation of an open source dataset based on the crossing of several epidemiological profiles and using data augmentation techniques. The results of these generated synthetic data have been qualitatively validated by specialist physicians in ophthalmology. Our results are very promising and consist in a first brick to promote research in AI on Uveitis disease.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"102 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90651227","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}
Frederic A. Hayek, Mirko Koscina, P. Lafourcade, Charles Olivier-Anclin
Private permissioned blockchains are becoming gradually more sought-after. Such systems are reachable by authorized users, and tend to be completely transparent to whoever interacts with the blockchain. In this paper, we mitigate the latter. Authorized users can now stay unlinked to the transaction they propose in the blockchain while being authenticated before being allowed to interact. As a first contribution, we developed a consensus algorithm for private permissioned blockchains based on Hyperledger Fabric and the Practical Byzantine Fault Tolerance consensus. Building on this blockchain, five additional variations achieving various client-wise privacy preserving levels are proposed. These different protocols allow for different use cases and levels of privacy control and sometimes its revocation by an authority. All our protocols guarantee the unlinkability of transactions to their issuers achieving anonymity or pseudonymity. Miners can also inherit some of the above privacy preserving setting. Naturally, we maintain liveness and safety of the system and its data.
{"title":"Generic Privacy Preserving Private Permissioned Blockchains","authors":"Frederic A. Hayek, Mirko Koscina, P. Lafourcade, Charles Olivier-Anclin","doi":"10.1145/3555776.3577735","DOIUrl":"https://doi.org/10.1145/3555776.3577735","url":null,"abstract":"Private permissioned blockchains are becoming gradually more sought-after. Such systems are reachable by authorized users, and tend to be completely transparent to whoever interacts with the blockchain. In this paper, we mitigate the latter. Authorized users can now stay unlinked to the transaction they propose in the blockchain while being authenticated before being allowed to interact. As a first contribution, we developed a consensus algorithm for private permissioned blockchains based on Hyperledger Fabric and the Practical Byzantine Fault Tolerance consensus. Building on this blockchain, five additional variations achieving various client-wise privacy preserving levels are proposed. These different protocols allow for different use cases and levels of privacy control and sometimes its revocation by an authority. All our protocols guarantee the unlinkability of transactions to their issuers achieving anonymity or pseudonymity. Miners can also inherit some of the above privacy preserving setting. Naturally, we maintain liveness and safety of the system and its data.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"427 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84945426","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}
Dynamic Time Warping (DTW) is a robust method to measure the similarity between two sequences. This paper proposes a method based on DTW to analyse high-speed data streams. The central idea is to decompose the network traffic into sequences of histograms of packet sizes and then calculate the distance between pairs of such sequences using DTW with Kullback-Leibler (KL) distance. As a baseline, we also compute the Euclidean Distance between the sequences of histograms. Since our preliminary experiments indicate that the distance between two sequences falls within a different range of values for distinct types of streams, we then exploit this distance information for stream classification using a Random Forest. The approach was investigated using recent internet traffic data from a telecommunications company. To illustrate the application of our approach, we conducted a case study with encrypted Internet Protocol Television (IPTV) network traffic data. The goal was to use our DTW-based approach to detect the video codec used in the streams, as well as the IPTV channel. Results strongly suggest that the DTW distance value between the data streams is highly informative for such classification tasks.
{"title":"A DTW Approach for Complex Data A Case Study with Network Data Streams","authors":"Paula Raissa Silva, João Vinagre, J. Gama","doi":"10.1145/3555776.3577638","DOIUrl":"https://doi.org/10.1145/3555776.3577638","url":null,"abstract":"Dynamic Time Warping (DTW) is a robust method to measure the similarity between two sequences. This paper proposes a method based on DTW to analyse high-speed data streams. The central idea is to decompose the network traffic into sequences of histograms of packet sizes and then calculate the distance between pairs of such sequences using DTW with Kullback-Leibler (KL) distance. As a baseline, we also compute the Euclidean Distance between the sequences of histograms. Since our preliminary experiments indicate that the distance between two sequences falls within a different range of values for distinct types of streams, we then exploit this distance information for stream classification using a Random Forest. The approach was investigated using recent internet traffic data from a telecommunications company. To illustrate the application of our approach, we conducted a case study with encrypted Internet Protocol Television (IPTV) network traffic data. The goal was to use our DTW-based approach to detect the video codec used in the streams, as well as the IPTV channel. Results strongly suggest that the DTW distance value between the data streams is highly informative for such classification tasks.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"12 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77968707","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 the aging population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we develop and evaluate a method to forecast activities of daily living (ADL) and detect anomalous behaviour using motion sensor data from smart homes. We build a predictive Multivariate long short term memory (LSTM) model for forecasting activities and evaluate it using data from six real-world smart homes. Further, we use Mahalanobis distance to identify anomalies in user behaviors based on predictions and actual values. In all of the datasets used for forecasting both duration of stay and level of activities using duration of activeness/stillness features, the max NMAE error was about 6%, the values show that the performance of LSTM for predicting the direct next activity versus the seven coming activities are close.
{"title":"Unsupervised Forecasting and Anomaly Detection of ADLs in single-resident elderly smart homes","authors":"Zahraa Khais Shahid, S. Saguna, C. Åhlund","doi":"10.1145/3555776.3577822","DOIUrl":"https://doi.org/10.1145/3555776.3577822","url":null,"abstract":"As the aging population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we develop and evaluate a method to forecast activities of daily living (ADL) and detect anomalous behaviour using motion sensor data from smart homes. We build a predictive Multivariate long short term memory (LSTM) model for forecasting activities and evaluate it using data from six real-world smart homes. Further, we use Mahalanobis distance to identify anomalies in user behaviors based on predictions and actual values. In all of the datasets used for forecasting both duration of stay and level of activities using duration of activeness/stillness features, the max NMAE error was about 6%, the values show that the performance of LSTM for predicting the direct next activity versus the seven coming activities are close.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"51 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81874960","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}
Many privilege escalation exploits on Linux abuse vulnerable system calls to threaten the system's security. Therefore, various static and dynamic analysis based seccomp policy generation frameworks emerged. Yet, they either focus on a subset of the available binaries or are constrained by the inherent properties of dynamic, testing-based analysis, which are prone to false negatives. In this paper, we present Jesse, a static-analysis-based framework for generating seccomp policies for ELF binaries. We design and implement an abstract-interpretation-based constant propagation that helps the analyst identify vital system calls for arbitrary, non-obfuscated binaries. Using the extracted results, Jesse allows producing effective seccomp policies, reducing the system's attack vector. To assess Jesse's effectiveness and accuracy, we have applied our system to over 1,000 ELF binaries for Debian 10, and show that---contrary to existing solutions---Jesse produces accurate and safely approximated results, without relying on any properties of the target binaries. In addition, we conduct a case study in which we combine Jesse's constant propagation strategy with container debloating techniques to produce seccomp policies that restrict up to five times more system calls than the Docker's default seccomp policy on average.
{"title":"Free Willy: Prune System Calls to Enhance Software Security","authors":"Charlie Groh, Sergej Proskurin, Apostolis Zarras","doi":"10.1145/3555776.3577593","DOIUrl":"https://doi.org/10.1145/3555776.3577593","url":null,"abstract":"Many privilege escalation exploits on Linux abuse vulnerable system calls to threaten the system's security. Therefore, various static and dynamic analysis based seccomp policy generation frameworks emerged. Yet, they either focus on a subset of the available binaries or are constrained by the inherent properties of dynamic, testing-based analysis, which are prone to false negatives. In this paper, we present Jesse, a static-analysis-based framework for generating seccomp policies for ELF binaries. We design and implement an abstract-interpretation-based constant propagation that helps the analyst identify vital system calls for arbitrary, non-obfuscated binaries. Using the extracted results, Jesse allows producing effective seccomp policies, reducing the system's attack vector. To assess Jesse's effectiveness and accuracy, we have applied our system to over 1,000 ELF binaries for Debian 10, and show that---contrary to existing solutions---Jesse produces accurate and safely approximated results, without relying on any properties of the target binaries. In addition, we conduct a case study in which we combine Jesse's constant propagation strategy with container debloating techniques to produce seccomp policies that restrict up to five times more system calls than the Docker's default seccomp policy on average.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"4 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90065825","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}
ZNS SSDs (Zoned Namespace SSD) are block devices that provide stable performance and low price by forcing sequential writes, however their users have to pay the price to guarantee strong write order. In addition, to get the best performance from small zoned ZNS SSDs that give the users control over device-internal parallel elements, the users need to utilize the SSDs in detail. Due to these overheads, Ceph, one of the distributed storage systems, has up to 69% lower performance when using ZNS SSDs compared to using legacy SSD. In this paper, we present zCeph which solves the problems that occur when using small zoned ZNS SSD in storage systems. We implemented zCeph based on legacy Ceph and evaluated it using synthesized and real-world workloads, showing that the performance improved by up to 4.1x and 7x, respectively, compared to the legacy Ceph using ZNS SSD.
{"title":"zCeph: Achieving High Performance On Storage System Using Small Zoned ZNS SSD","authors":"Jin-Yong Ha, H. Yeom","doi":"10.1145/3555776.3577758","DOIUrl":"https://doi.org/10.1145/3555776.3577758","url":null,"abstract":"ZNS SSDs (Zoned Namespace SSD) are block devices that provide stable performance and low price by forcing sequential writes, however their users have to pay the price to guarantee strong write order. In addition, to get the best performance from small zoned ZNS SSDs that give the users control over device-internal parallel elements, the users need to utilize the SSDs in detail. Due to these overheads, Ceph, one of the distributed storage systems, has up to 69% lower performance when using ZNS SSDs compared to using legacy SSD. In this paper, we present zCeph which solves the problems that occur when using small zoned ZNS SSD in storage systems. We implemented zCeph based on legacy Ceph and evaluated it using synthesized and real-world workloads, showing that the performance improved by up to 4.1x and 7x, respectively, compared to the legacy Ceph using ZNS SSD.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"5 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89732334","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}
Kengo Watanabe, F. Machida, E. Andrade, R. Pietrantuono, Domenico Cotroneo
Real-time object detection systems are rapidly adopted in many edge computing systems for IoT applications. Since the computational resources on edge devices are often limited, continuous real-time object detection may suffer from the degradation of performance and reliability due to software aging. To provide a reliable IoT applications, it is crucial to understand how software aging can manifest in object detection systems under resource-constrained environment. In this paper, we investigate the software aging issue in a real-time object detection system using YOLOv5 running on a Raspberry Pi-based edge server. By performing statistical analysis on the measurement data, we detected a suspicious trend of software aging in the memory usage, which is induced by real-time object detection workloads. We also observe that a system monitoring process is halted due to the shortage of free storage space as a result of YOLOv5's resource dissipation. The monitoring process fails after 24.11, 44.56, and 115.36 hours (on average), when we set the sizes of input images to 160px, 320px, and 640px, respectively, in our system. Our experimental results can be used to plan countermeasures such as software rejuvenation and task offloading.
{"title":"Software Aging in a Real-Time Object Detection System on an Edge Server","authors":"Kengo Watanabe, F. Machida, E. Andrade, R. Pietrantuono, Domenico Cotroneo","doi":"10.1145/3555776.3577717","DOIUrl":"https://doi.org/10.1145/3555776.3577717","url":null,"abstract":"Real-time object detection systems are rapidly adopted in many edge computing systems for IoT applications. Since the computational resources on edge devices are often limited, continuous real-time object detection may suffer from the degradation of performance and reliability due to software aging. To provide a reliable IoT applications, it is crucial to understand how software aging can manifest in object detection systems under resource-constrained environment. In this paper, we investigate the software aging issue in a real-time object detection system using YOLOv5 running on a Raspberry Pi-based edge server. By performing statistical analysis on the measurement data, we detected a suspicious trend of software aging in the memory usage, which is induced by real-time object detection workloads. We also observe that a system monitoring process is halted due to the shortage of free storage space as a result of YOLOv5's resource dissipation. The monitoring process fails after 24.11, 44.56, and 115.36 hours (on average), when we set the sizes of input images to 160px, 320px, and 640px, respectively, in our system. Our experimental results can be used to plan countermeasures such as software rejuvenation and task offloading.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89101926","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}
Recently, research using knowledge distillation in artificial intelligence (AI) has been actively conducted. In particular, data-efficient image transformer (DeiT) is a representative transformer model using knowledge distillation in image classification. However, DeiT's safety against the patch unit's adversarial attacks was not verified. Furthermore, existing DeiT research did not prove security robustness against adversarial attacks. In order to verify the vulnerability of adversarial attacks, we conducted an attack using the fast gradient sign method (FGSM) targeting the DeiT model based on knowledge distillation. As a result of the experiment, an accuracy of 93.99% was shown in DeiT verification based on Normal data (Cifar-10). In contrast, when verified with abnormal data based on FGSM (adversarial examples), the accuracy decreased by 83.49% to 10.50%. By analyzing the vulnerability pattern related to adversarial attacks, we confirmed that FGSM showed successful attack performance through weight control of DeiT. Moreover, we verified that DeiT has security limitations for practical application.
{"title":"Security Verification Software Platform of Data-efficient Image Transformer Based on Fast Gradient Sign Method","authors":"In-pyo Hong, Gyu-ho Choi, Pan-koo Kim, Chang Choi","doi":"10.1145/3555776.3577731","DOIUrl":"https://doi.org/10.1145/3555776.3577731","url":null,"abstract":"Recently, research using knowledge distillation in artificial intelligence (AI) has been actively conducted. In particular, data-efficient image transformer (DeiT) is a representative transformer model using knowledge distillation in image classification. However, DeiT's safety against the patch unit's adversarial attacks was not verified. Furthermore, existing DeiT research did not prove security robustness against adversarial attacks. In order to verify the vulnerability of adversarial attacks, we conducted an attack using the fast gradient sign method (FGSM) targeting the DeiT model based on knowledge distillation. As a result of the experiment, an accuracy of 93.99% was shown in DeiT verification based on Normal data (Cifar-10). In contrast, when verified with abnormal data based on FGSM (adversarial examples), the accuracy decreased by 83.49% to 10.50%. By analyzing the vulnerability pattern related to adversarial attacks, we confirmed that FGSM showed successful attack performance through weight control of DeiT. Moreover, we verified that DeiT has security limitations for practical application.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"137 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89146499","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}
Edge servers must routinely undergo maintenance to ensure the environment's performance and security. During maintenance, applications hosted by outdated servers must be relocated to alternative servers to avoid downtime. In distributed edges with servers spread across large regions, ensuring that applications are not migrated to servers too far away from their users to avoid high latency hardens the maintenance planning. In addition, the limited power supply of edge sites restricts the list of suitable alternative hosts for the applications even further. Past work has focused on optimizing maintenance or increasing the power efficiency of edge computing infrastructures. However, no work addresses both objectives together. This paper presents Emma, a maintenance strategy that reduces power consumption during edge server maintenance without excessively extending maintenance time or increasing application latency. Experiments show that Emma can minimize power consumption during maintenance by up to 26.48% compared to strategies from the literature.
{"title":"Reducing Power Consumption during Server Maintenance on Edge Computing Infrastructures","authors":"Felipe Rubin, Paulo S. Souza, T. Ferreto","doi":"10.1145/3555776.3577739","DOIUrl":"https://doi.org/10.1145/3555776.3577739","url":null,"abstract":"Edge servers must routinely undergo maintenance to ensure the environment's performance and security. During maintenance, applications hosted by outdated servers must be relocated to alternative servers to avoid downtime. In distributed edges with servers spread across large regions, ensuring that applications are not migrated to servers too far away from their users to avoid high latency hardens the maintenance planning. In addition, the limited power supply of edge sites restricts the list of suitable alternative hosts for the applications even further. Past work has focused on optimizing maintenance or increasing the power efficiency of edge computing infrastructures. However, no work addresses both objectives together. This paper presents Emma, a maintenance strategy that reduces power consumption during edge server maintenance without excessively extending maintenance time or increasing application latency. Experiments show that Emma can minimize power consumption during maintenance by up to 26.48% compared to strategies from the literature.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":"1 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89677045","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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