Pub Date : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098980
Huseyn Huseynov, Kenichi Kourai, T. Saadawi, O. Igbe
Software Keyloggers are dominant class of malicious applications that surreptitiously logs all the user activity to gather confidential information. Among many other types of keyloggers, API-based keyloggers can pretend as unprivileged program running in a user-space to eavesdrop and record all the keystrokes typed by the user. In a Linux environment, defending against these types of malware means defending the kernel against being compromised and it is still an open and difficult problem. Considering how recent trend of edge computing extends cloud computing and the Internet of Things (IoT) to the edge of the network, a new types of intrusion-detection system (IDS) has been used to mitigate cybersecurity threats in edge computing. Proposed work aims to provide secure environment by constantly checking virtual machines for the presence of keyloggers using cutting edge artificial immune system (AIS) based technology. The algorithms that exist in the field of AIS exploit the immune system’s characteristics of learning and memory to solve diverse problems. We further present our approach by employing an architecture where host OS and a virtual machine (VM) layer actively collaborate to guarantee kernel integrity. This collaborative approach allows us to introspect VM by tracking events (interrupts, system calls, memory writes, network activities, etc.) and to detect anomalies by employing negative selection algorithm (NSA).
{"title":"Virtual Machine Introspection for Anomaly-Based Keylogger Detection","authors":"Huseyn Huseynov, Kenichi Kourai, T. Saadawi, O. Igbe","doi":"10.1109/HPSR48589.2020.9098980","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098980","url":null,"abstract":"Software Keyloggers are dominant class of malicious applications that surreptitiously logs all the user activity to gather confidential information. Among many other types of keyloggers, API-based keyloggers can pretend as unprivileged program running in a user-space to eavesdrop and record all the keystrokes typed by the user. In a Linux environment, defending against these types of malware means defending the kernel against being compromised and it is still an open and difficult problem. Considering how recent trend of edge computing extends cloud computing and the Internet of Things (IoT) to the edge of the network, a new types of intrusion-detection system (IDS) has been used to mitigate cybersecurity threats in edge computing. Proposed work aims to provide secure environment by constantly checking virtual machines for the presence of keyloggers using cutting edge artificial immune system (AIS) based technology. The algorithms that exist in the field of AIS exploit the immune system’s characteristics of learning and memory to solve diverse problems. We further present our approach by employing an architecture where host OS and a virtual machine (VM) layer actively collaborate to guarantee kernel integrity. This collaborative approach allows us to introspect VM by tracking events (interrupts, system calls, memory writes, network activities, etc.) and to detect anomalies by employing negative selection algorithm (NSA).","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123211529","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098996
Richard Li, K. Makhijani, Lijun Dong
The IP is a primary data plane protocol on the Internet, which has several deficiencies when addressing the needs of modern digital society involving machine-to-machine communication and a remarkably enhanced user experience. New IP is an advanced network protocol specification to modernize the network layer without changing the fundamental Internet architecture. New IP envisions a new header format with 3 functional characteristics, i.e., shipping spec, contract spec, and payload spec. Using these fundamental blocks, New IP proposes a new data plane forwarding paradigm with far more advanced capabilities, such as ManyNets addressing, high precision services and qualitative communications.
{"title":"New IP: A Data Packet Framework to Evolve the Internet : Invited Paper","authors":"Richard Li, K. Makhijani, Lijun Dong","doi":"10.1109/HPSR48589.2020.9098996","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098996","url":null,"abstract":"The IP is a primary data plane protocol on the Internet, which has several deficiencies when addressing the needs of modern digital society involving machine-to-machine communication and a remarkably enhanced user experience. New IP is an advanced network protocol specification to modernize the network layer without changing the fundamental Internet architecture. New IP envisions a new header format with 3 functional characteristics, i.e., shipping spec, contract spec, and payload spec. Using these fundamental blocks, New IP proposes a new data plane forwarding paradigm with far more advanced capabilities, such as ManyNets addressing, high precision services and qualitative communications.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124164596","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098969
Xiao Lin, Jia Zhang, Shengnan Yue, Yuanlong Tan, Xiaoyu Wang, Weiqiang Sun, M. Veeraraghavan, Weisheng Hu
The time- and space-varying nature of residual bandwidth in inter-datacenter networks (inter-DCNs) makes conventional end-to-end connections difficult to fully utilize the residual bandwidth. A promising solution is to introduce datacenter storage into data-plane paths. Delay-tolerant bulk data can be temporarily stored at intermediate sites and forwarded (SnF) at a later time when inter-DCN is less congested. However, the conventional methods attempt to involve multi-dimensional state information of the entire network in scheduling, which results in high computational complexity. In this paper, our studies reveal that there exist redundant states in scheduling, which cannot provide any performance benefit while imposing extra computational burden. Inspired by this finding, we propose a state-merging scheduling (SMS) method. By merging the state information of the pre-selected links, the SMS method naturally reduces the redundant states involved, which greatly improves the efficiency of SnF scheduling. Simulations demonstrate that the SMS method can outperform the conventional scheduling method, given a limit of the computational cost.
{"title":"A State-Merging Scheduling Method for Bulk Transfers with Store-and-Forward over Inter-DC Optical Networks","authors":"Xiao Lin, Jia Zhang, Shengnan Yue, Yuanlong Tan, Xiaoyu Wang, Weiqiang Sun, M. Veeraraghavan, Weisheng Hu","doi":"10.1109/HPSR48589.2020.9098969","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098969","url":null,"abstract":"The time- and space-varying nature of residual bandwidth in inter-datacenter networks (inter-DCNs) makes conventional end-to-end connections difficult to fully utilize the residual bandwidth. A promising solution is to introduce datacenter storage into data-plane paths. Delay-tolerant bulk data can be temporarily stored at intermediate sites and forwarded (SnF) at a later time when inter-DCN is less congested. However, the conventional methods attempt to involve multi-dimensional state information of the entire network in scheduling, which results in high computational complexity. In this paper, our studies reveal that there exist redundant states in scheduling, which cannot provide any performance benefit while imposing extra computational burden. Inspired by this finding, we propose a state-merging scheduling (SMS) method. By merging the state information of the pre-selected links, the SMS method naturally reduces the redundant states involved, which greatly improves the efficiency of SnF scheduling. Simulations demonstrate that the SMS method can outperform the conventional scheduling method, given a limit of the computational cost.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443660","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098971
Risa Fujita, Fujun He, E. Oki
This paper presents two strategies to obtain an assignment of backup servers to network functions of middleboxes when each backup server can recover a half of the functions which it protects at the same time. In the previous work, there are approaches to obtain an assignment only when each backup server protects two functions and recovers one of them at the same time. Therefore, we present two strategies to expand the cases where an assignment can be obtained by utilizing the previous approaches. The basic ideas of our two strategies are dividing each server into a set of small servers that protects two functions and recovers one of them at the same time, obtaining an assignment with them, and combining them. In the process of obtaining an assignment with our presented two strategies, there is a constraint to avoid impairing the capabilities of backup servers. Our two strategies incorporate this constraint before and after obtaining an assignment with the divided small servers, respectively. We define six survival probabilities regarding our two strategies and analyze their relationships. Then, we derive two theorems to consider when our strategy can obtain an assignment that satisfies the constraint. Based on the theorems, we analyze properties of our strategies and the relationship between the different survival probabilities. Numerical results show that one of our strategies provides the higher survival probability than the other one for all the settings that we examine.
{"title":"Shared Backup Resource Assignment for Middleboxes Considering Server Capability","authors":"Risa Fujita, Fujun He, E. Oki","doi":"10.1109/HPSR48589.2020.9098971","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098971","url":null,"abstract":"This paper presents two strategies to obtain an assignment of backup servers to network functions of middleboxes when each backup server can recover a half of the functions which it protects at the same time. In the previous work, there are approaches to obtain an assignment only when each backup server protects two functions and recovers one of them at the same time. Therefore, we present two strategies to expand the cases where an assignment can be obtained by utilizing the previous approaches. The basic ideas of our two strategies are dividing each server into a set of small servers that protects two functions and recovers one of them at the same time, obtaining an assignment with them, and combining them. In the process of obtaining an assignment with our presented two strategies, there is a constraint to avoid impairing the capabilities of backup servers. Our two strategies incorporate this constraint before and after obtaining an assignment with the divided small servers, respectively. We define six survival probabilities regarding our two strategies and analyze their relationships. Then, we derive two theorems to consider when our strategy can obtain an assignment that satisfies the constraint. Based on the theorems, we analyze properties of our strategies and the relationship between the different survival probabilities. Numerical results show that one of our strategies provides the higher survival probability than the other one for all the settings that we examine.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133090840","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 : 2020-05-01DOI: 10.1109/hpsr48589.2020.9098984
{"title":"HPSR 2020 Author Index","authors":"","doi":"10.1109/hpsr48589.2020.9098984","DOIUrl":"https://doi.org/10.1109/hpsr48589.2020.9098984","url":null,"abstract":"","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126866929","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098982
Kazuki Hara, K. Shiomoto, Chin Lam Eng, Sebastian Backstad
It is crucial to identify the cause immeditely when a failure occurs at the base station called eNodeB in LTE networks. However, a huge amount of log data generated from the eNodeB prevents the human operator to quickly identify the cause of failure. In order to improve the network operation efficiency, machine learning technique is used to analyze Key Performance Indicator (KPI) data generated from eNodeB and classify the operational status of the eNodeB. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate raw performance metric data. To address this issue, we propose a method that employs Adversarial Autoencoder (AAE), which is a semi-supervised learning method. We evaluate the proposed method using eNodeB log data collected from a service provider LTE network. We confirm that our approach achieves on average 94% accuracy and yields high accuracy even for the class with a small amount of labeled data.
{"title":"Automatic eNodeB state management in LTE networks using Semi-Supervised Learning with Adversarial Autoencoder","authors":"Kazuki Hara, K. Shiomoto, Chin Lam Eng, Sebastian Backstad","doi":"10.1109/HPSR48589.2020.9098982","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098982","url":null,"abstract":"It is crucial to identify the cause immeditely when a failure occurs at the base station called eNodeB in LTE networks. However, a huge amount of log data generated from the eNodeB prevents the human operator to quickly identify the cause of failure. In order to improve the network operation efficiency, machine learning technique is used to analyze Key Performance Indicator (KPI) data generated from eNodeB and classify the operational status of the eNodeB. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate raw performance metric data. To address this issue, we propose a method that employs Adversarial Autoencoder (AAE), which is a semi-supervised learning method. We evaluate the proposed method using eNodeB log data collected from a service provider LTE network. We confirm that our approach achieves on average 94% accuracy and yields high accuracy even for the class with a small amount of labeled data.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129292594","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098974
Hasibul Jamil, N. Weng
High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine and its performance evaluation. By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EBs) to extract a large number of candidate rules with just a few of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be full-matched to improve the online system performance. Moreover, our EBs learning-based selection method is independent of the ruleset, which can be applied to varying rulesets. Our multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs.
{"title":"Multibit Tries Packet Classification with Deep Reinforcement Learning","authors":"Hasibul Jamil, N. Weng","doi":"10.1109/HPSR48589.2020.9098974","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098974","url":null,"abstract":"High performance packet classification is a key component to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable and high performance packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine and its performance evaluation. By exploiting the sparsity of ruleset, our algorithm uses a few effective bits (EBs) to extract a large number of candidate rules with just a few of memory access. These effective bits are learned with deep reinforcement learning and they are used to create a bitmap to filter out the majority of rules which do not need to be full-matched to improve the online system performance. Moreover, our EBs learning-based selection method is independent of the ruleset, which can be applied to varying rulesets. Our multibit tries classification engine outperforms lookup time both in worst and average case by 55% and reduce memory footprint, compared to traditional decision tree without EBs.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127746550","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098994
Zili Ning, Ning Wang, R. Tafazolli
With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.
{"title":"Deep Reinforcement Learning for NFV-based Service Function Chaining in Multi-Service Networks : Invited Paper","authors":"Zili Ning, Ning Wang, R. Tafazolli","doi":"10.1109/HPSR48589.2020.9098994","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098994","url":null,"abstract":"With the advent of Network Function Virtualization (NFV) techniques, a subset of the Internet traffic will be treated by a chain of virtual network functions (VNFs) during their journeys while the rest of the background traffic will still be carried based on traditional routing protocols. Under such a multi-service network environment, we consider the co-existence of heterogeneous traffic control mechanisms, including flexible, dynamic service function chaining (SFC) traffic control and static, dummy IP routing for the aforementioned two types of traffic that share common network resources. Depending on the traffic patterns of the background traffic which is statically routed through the traditional IP routing platform, we aim to perform dynamic service function chaining for the foreground traffic requiring VNF treatments, so that both the end-to-end SFC performance and the overall network resource utilization can be optimized. Towards this end, we propose a deep reinforcement learning based scheme to enable intelligent SFC routing decision-making in dynamic network conditions. The proposed scheme is ready to be deployed on both hybrid SDN/IP platforms and future advanced IP environments. Based on the real GEANT network topology and its one-week traffic traces, our experiments show that the proposed scheme is able to significantly improve from the traditional routing paradigm and achieve close-to-optimal performances very fast while satisfying the end-to-end SFC requirements.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280954","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 : 2020-05-01DOI: 10.1109/HPSR48589.2020.9098977
Jorge Medina, Zhengqi Jiang, R. Rojas-Cessa
In this paper we propose three modified versions of the Greedy SmAlleSt-cost Path first (GRASP) algorithm for minimizing the total transmission cost in a digital microgrid (DMG). The Simple Dynamic GRASP (SDG) is a cached-version of GRASP that dynamically updates the available capacity of links. The total path Transmission cost Dynamic GRASP (TDG) is a cached version of GRASP that uses the path’s transmission cost as the criteria to select the smallest-cost paths. The Brute Force TDG (BFT) is a cached version of GRASP that explores all paths between loads and sources and selects the smallest-cost paths by evaluating the path’s transmission costs. Our results show that SDG achieves the smallest total transmission cost and the fewest unsatisfied loads. Although TDG and BFT may achieve similar total transmission costs to those of SDG and GRASP, our results show that using the path’s transmission costs in the selection of the smallest-cost path is not as effective as using the path’s link costs. However, in more complex power networks, we show that the cached TDG and BFT yield fewer unsatisfied loads than that of a GRASP implementation without a cache.
在本文中，我们提出了贪心最小代价路径优先(GRASP)算法的三个改进版本，用于最小化数字微电网(DMG)中的总传输成本。简单动态抓取(SDG)是抓取的缓存版本，它动态更新链接的可用容量。动态抓取(TDG)是抓取的缓存版本，它使用路径的传输代价作为选择代价最小的路径的标准。Brute Force TDG (BFT)是GRASP的缓存版本，它探索负载和源之间的所有路径，并通过评估路径的传输成本来选择成本最小的路径。结果表明，SDG的总输电成本最小，未满足负荷最小。尽管TDG和BFT可以实现与SDG和GRASP相似的总传输成本，但我们的研究结果表明，在选择成本最小的路径时，使用路径的传输成本不如使用路径的链路成本有效。然而，在更复杂的电力网络中，我们表明，与没有缓存的GRASP实现相比，缓存的TDG和BFT产生的未满足负载更少。
{"title":"Effectiveness of Many-to-Many GRASP-Based Routing Algorithms for Power Distribution","authors":"Jorge Medina, Zhengqi Jiang, R. Rojas-Cessa","doi":"10.1109/HPSR48589.2020.9098977","DOIUrl":"https://doi.org/10.1109/HPSR48589.2020.9098977","url":null,"abstract":"In this paper we propose three modified versions of the Greedy SmAlleSt-cost Path first (GRASP) algorithm for minimizing the total transmission cost in a digital microgrid (DMG). The Simple Dynamic GRASP (SDG) is a cached-version of GRASP that dynamically updates the available capacity of links. The total path Transmission cost Dynamic GRASP (TDG) is a cached version of GRASP that uses the path’s transmission cost as the criteria to select the smallest-cost paths. The Brute Force TDG (BFT) is a cached version of GRASP that explores all paths between loads and sources and selects the smallest-cost paths by evaluating the path’s transmission costs. Our results show that SDG achieves the smallest total transmission cost and the fewest unsatisfied loads. Although TDG and BFT may achieve similar total transmission costs to those of SDG and GRASP, our results show that using the path’s transmission costs in the selection of the smallest-cost path is not as effective as using the path’s link costs. However, in more complex power networks, we show that the cached TDG and BFT yield fewer unsatisfied loads than that of a GRASP implementation without a cache.","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122309164","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 : 2020-05-01DOI: 10.1109/hpsr48589.2020.9098973
{"title":"[Copyright notice]","authors":"","doi":"10.1109/hpsr48589.2020.9098973","DOIUrl":"https://doi.org/10.1109/hpsr48589.2020.9098973","url":null,"abstract":"","PeriodicalId":163393,"journal":{"name":"2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129685135","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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