Pub Date : 2017-12-01DOI: 10.1109/PDCAT.2017.00056
Yong Zhang, Ruonan Li, Yanan Zhang, Mei Song
Energy efficiency is an important factor to optimize the multi-hop forwarding strategy. PD (Pairing-inspired Dijkstra) multi-hop data forwarding algorithm is proposed to share cellular spectrum resources with D2D (device-to-device) users. Candidate multiplexing channel model is established in our proposal. Based on this model, PD algorithm solves the issue on channel and path selection. PD algorithm includes two parts, KM dichotomous matching algorithm and multiple iterations for Dijkstra algorithm. Under energy efficiency and QoS (Quality of Service) constraint, PD algorithm selects optimal transmission path among D2D users. Furthermore, the energy efficiency and transmission delay are evaluated in simulation section under PD, Dijkstra and CD (Closest to Destination) algorithm. Simulation results indicate that PD has better performance on energy efficiency and E2E (End to End) delay.
能量效率是优化多跳转发策略的一个重要因素。为了与D2D (device-to-device)用户共享蜂窝频谱资源，提出了PD (pair -inspired Dijkstra)多跳数据转发算法。本文建立了候选复用信道模型。基于该模型，PD算法解决了信道和路径的选择问题。PD算法包括KM二分类匹配算法和多次迭代Dijkstra算法两部分。PD算法在能量效率和QoS (Quality Service)约束下，在D2D用户之间选择最优传输路径。在仿真部分对PD、Dijkstra和CD (nearest to Destination)算法下的能量效率和传输延迟进行了评估。仿真结果表明，PD在能效和端到端延迟方面具有较好的性能。
{"title":"Data Forwarding Algorithm Based on Energy Efficiency in Multi-Hop Device to Device Network","authors":"Yong Zhang, Ruonan Li, Yanan Zhang, Mei Song","doi":"10.1109/PDCAT.2017.00056","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00056","url":null,"abstract":"Energy efficiency is an important factor to optimize the multi-hop forwarding strategy. PD (Pairing-inspired Dijkstra) multi-hop data forwarding algorithm is proposed to share cellular spectrum resources with D2D (device-to-device) users. Candidate multiplexing channel model is established in our proposal. Based on this model, PD algorithm solves the issue on channel and path selection. PD algorithm includes two parts, KM dichotomous matching algorithm and multiple iterations for Dijkstra algorithm. Under energy efficiency and QoS (Quality of Service) constraint, PD algorithm selects optimal transmission path among D2D users. Furthermore, the energy efficiency and transmission delay are evaluated in simulation section under PD, Dijkstra and CD (Closest to Destination) algorithm. Simulation results indicate that PD has better performance on energy efficiency and E2E (End to End) delay.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122737040","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00044
C. Hlaing, Sai Maung Maung Zaw
We introduce a set of statistical features and propose the SIFT texture features descriptor model on statistical image processing. The proposed feature is applied to plant disease classification with PlantVillage image dataset. The input is plant leaf image taken by phone camera whereas the output is the plant disease name. The input image is preprocessed to remove background. The SIFT features are extracted from the preprocessed image. As a main contribution, the extracted SIFT features are model by Generalized Extreme Value (GEV) Distribution to represent an image information in a small number of dimensions. We focus on the statistical feature and model-based texture features to minimize the computational time and complexity of phone image processing. The propose features aim to be significantly reduced in computational time for plant disease recognition for mobile phone. The experimental result shows that the proposed features can compare with other previous statistical features and can also distinguish between six tomato diseases, including Leaf Mold, Septoria Leaf Spot, Two Spotted Spider Mite, Late Blight, Bacterial Spot and Target Spot.
{"title":"Model-Based Statistical Features for Mobile Phone Image of Tomato Plant Disease Classification","authors":"C. Hlaing, Sai Maung Maung Zaw","doi":"10.1109/PDCAT.2017.00044","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00044","url":null,"abstract":"We introduce a set of statistical features and propose the SIFT texture features descriptor model on statistical image processing. The proposed feature is applied to plant disease classification with PlantVillage image dataset. The input is plant leaf image taken by phone camera whereas the output is the plant disease name. The input image is preprocessed to remove background. The SIFT features are extracted from the preprocessed image. As a main contribution, the extracted SIFT features are model by Generalized Extreme Value (GEV) Distribution to represent an image information in a small number of dimensions. We focus on the statistical feature and model-based texture features to minimize the computational time and complexity of phone image processing. The propose features aim to be significantly reduced in computational time for plant disease recognition for mobile phone. The experimental result shows that the proposed features can compare with other previous statistical features and can also distinguish between six tomato diseases, including Leaf Mold, Septoria Leaf Spot, Two Spotted Spider Mite, Late Blight, Bacterial Spot and Target Spot.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117311661","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00018
Xiaoyuan Fu, Jingyu Wang, Q. Qi, J. Liao, Tonghong Li
Cloud-based big data platforms provide physical resources for a variety of applications to analyze all forms of data. For the stream big data analytics, a participated task always needs to scale out resources when its input data increases steeply. Typically, the resource scaling out can be achieved by increasing the parallelism degree of the platform based on the experience. However, the resource scaling-out of each task produces additional cost not only from itself but also from other competitive tasks, which brings about great challenges to ensure the efficient utilization of resources. To solve this problem systematically, we consider the resource scaling-out problem as a non-cooperative game and formulate a total cost model including a risk function and a task execution time function. The total cost of resource scaling-out reflects the influence of topology structure for the benefit of a participated task. Hence, two economic classic tax-based incentive policies: Pivotal Mechanism and Externality Mechanism are applied, to stimulate the participation of tasks. We make simulations in different scenarios including node degree and different characteristics of tasks. The simulations results show that our resource scaling-out mechanism can achieve a better performance close to social optimality.
{"title":"Tax-Based Mechanisms for Resource Scaling-Out of Stream Big Data Analytics","authors":"Xiaoyuan Fu, Jingyu Wang, Q. Qi, J. Liao, Tonghong Li","doi":"10.1109/PDCAT.2017.00018","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00018","url":null,"abstract":"Cloud-based big data platforms provide physical resources for a variety of applications to analyze all forms of data. For the stream big data analytics, a participated task always needs to scale out resources when its input data increases steeply. Typically, the resource scaling out can be achieved by increasing the parallelism degree of the platform based on the experience. However, the resource scaling-out of each task produces additional cost not only from itself but also from other competitive tasks, which brings about great challenges to ensure the efficient utilization of resources. To solve this problem systematically, we consider the resource scaling-out problem as a non-cooperative game and formulate a total cost model including a risk function and a task execution time function. The total cost of resource scaling-out reflects the influence of topology structure for the benefit of a participated task. Hence, two economic classic tax-based incentive policies: Pivotal Mechanism and Externality Mechanism are applied, to stimulate the participation of tasks. We make simulations in different scenarios including node degree and different characteristics of tasks. The simulations results show that our resource scaling-out mechanism can achieve a better performance close to social optimality.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133607308","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00055
Xianfeng Li, Junxian Huang
Flying Ad hoc Network (FANET) is a special type of Mobile Ad hoc Network (MANET) consisting of a swarm of Unmanned Aerial Vehicles (UAVs). And FANET is required to allow two or more UAVs to communicate directly or via relay UAV(s). To achieve stable communication in FANET, a routing protocol targeted to fast-moving FANET nodes is necessary. Among the existing MANET routing protocols, geographic routing protocol is a promising candidate, as routing decision is made only according to position information, and there is no need to maintain a routing table. Due to fast topology change in FANET, existing geographic routing protocols need to send beacon packets (or hello packets) frequently to maintain the correctness of routing selection. However, high beacon frequency means high overhead, which will lead to collisions with data packet and transmission delay. To overcome this problem, we propose an adaptive beacon scheme called ABPP. ABPP can adjust beacon frequency dynamically and predict the future positions of UAVs. Experimental results show that the proposed ABPP scheme can effectively decrease the beacon overhead and improve the packet delivery ratio.
{"title":"ABPP: An Adaptive Beacon Scheme for Geographic Routing in FANET","authors":"Xianfeng Li, Junxian Huang","doi":"10.1109/PDCAT.2017.00055","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00055","url":null,"abstract":"Flying Ad hoc Network (FANET) is a special type of Mobile Ad hoc Network (MANET) consisting of a swarm of Unmanned Aerial Vehicles (UAVs). And FANET is required to allow two or more UAVs to communicate directly or via relay UAV(s). To achieve stable communication in FANET, a routing protocol targeted to fast-moving FANET nodes is necessary. Among the existing MANET routing protocols, geographic routing protocol is a promising candidate, as routing decision is made only according to position information, and there is no need to maintain a routing table. Due to fast topology change in FANET, existing geographic routing protocols need to send beacon packets (or hello packets) frequently to maintain the correctness of routing selection. However, high beacon frequency means high overhead, which will lead to collisions with data packet and transmission delay. To overcome this problem, we propose an adaptive beacon scheme called ABPP. ABPP can adjust beacon frequency dynamically and predict the future positions of UAVs. Experimental results show that the proposed ABPP scheme can effectively decrease the beacon overhead and improve the packet delivery ratio.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125113897","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00074
Lei Wang, G. Ding, Yulong Zhao, Dingzeyu Wu, Chengrui He
LevelDB uses the merger mechanism for data integration during the writing process. During this process, the value will move together with the key, causing a lot of unnecessary data rewriting. This paper presents a structure that the key stored separately from the value. And the value is stored in a separate file (we call it Value File), with value offset in the file and length information stored in LevelDB. Test results show that the optimized LevelDBs sequential write performance is reduced by about 40%. But random write and overwrite performances improve more than 200%. And with the increase of the number of tests records, the improvement becomes more and more obvious. The amount of rewriting data and the number of merging files, depending on the length of every different record, reduce about 80% averagely, which significantly improves the performance of the original program.
{"title":"Optimization of LevelDB by Separating Key and Value","authors":"Lei Wang, G. Ding, Yulong Zhao, Dingzeyu Wu, Chengrui He","doi":"10.1109/PDCAT.2017.00074","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00074","url":null,"abstract":"LevelDB uses the merger mechanism for data integration during the writing process. During this process, the value will move together with the key, causing a lot of unnecessary data rewriting. This paper presents a structure that the key stored separately from the value. And the value is stored in a separate file (we call it Value File), with value offset in the file and length information stored in LevelDB. Test results show that the optimized LevelDBs sequential write performance is reduced by about 40%. But random write and overwrite performances improve more than 200%. And with the increase of the number of tests records, the improvement becomes more and more obvious. The amount of rewriting data and the number of merging files, depending on the length of every different record, reduce about 80% averagely, which significantly improves the performance of the original program.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116790305","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00047
Xiangyuan Zhu, Bing Li, Kenli Li, Ping Shao, Yi Pan
Pairwise sequence alignment is a common and fundamental task in Computational Biology, which constitutes the basis for many Bioinformatics applications. In the post-genomic era, there is an increasing demand to align long DNA sequences to discover their functions. In this paper, we propose a parallel pairwise alignment algorithm for large genomic sequences by recursively dividing the whole genomic sequences into small pieces, with an effective pruning strategy to reduce search and computation space. We implemented rigorous tests on a 4-core computer using real genomic sequences and artificially generated sequences. The results show that our implementation can achieve speedup 10.64 with 99.75% accuracy compared to the sequential algorithm. As far as we know, this is the first time that MBP (mega base-pairs) sequences are globally aligned with an affine gap penalty.
{"title":"A Parallel Pairwise Alignment with Pruning for Large Genomic Sequences","authors":"Xiangyuan Zhu, Bing Li, Kenli Li, Ping Shao, Yi Pan","doi":"10.1109/PDCAT.2017.00047","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00047","url":null,"abstract":"Pairwise sequence alignment is a common and fundamental task in Computational Biology, which constitutes the basis for many Bioinformatics applications. In the post-genomic era, there is an increasing demand to align long DNA sequences to discover their functions. In this paper, we propose a parallel pairwise alignment algorithm for large genomic sequences by recursively dividing the whole genomic sequences into small pieces, with an effective pruning strategy to reduce search and computation space. We implemented rigorous tests on a 4-core computer using real genomic sequences and artificially generated sequences. The results show that our implementation can achieve speedup 10.64 with 99.75% accuracy compared to the sequential algorithm. As far as we know, this is the first time that MBP (mega base-pairs) sequences are globally aligned with an affine gap penalty.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129279182","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00057
S. Fujita
This paper proposes a method to improve the routing performance of hierarchical Delaunay networks. Delaunay network is a network topology for peer-to-peer systems which is based on the Delaunay triangulation of point set associated with the set of peers. It has a favorable property as a topology for peer-to-peer systems such that a greedy routing scheme always delivers a given message to its destination. The key idea of the proposed method is to apply a hash function to the address of participant peers. More concretely, by applying a hash function to the coordinate point of the peers, we could realize an overlay so that the number of hops to the destination in the original network could be effectively reduced.
{"title":"Hierarchical Delaunay Network for Peer-to-Peer Overlay with Address Hashing","authors":"S. Fujita","doi":"10.1109/PDCAT.2017.00057","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00057","url":null,"abstract":"This paper proposes a method to improve the routing performance of hierarchical Delaunay networks. Delaunay network is a network topology for peer-to-peer systems which is based on the Delaunay triangulation of point set associated with the set of peers. It has a favorable property as a topology for peer-to-peer systems such that a greedy routing scheme always delivers a given message to its destination. The key idea of the proposed method is to apply a hash function to the address of participant peers. More concretely, by applying a hash function to the coordinate point of the peers, we could realize an overlay so that the number of hops to the destination in the original network could be effectively reduced.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128449544","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00054
Lingjing Kong, Guowei Huang, Keke Wu
Network traffic identification has been a hot topic in network security area. The identification of abnormal traffic can detect attack traffic and helps network manager enforce corresponding security policies to prevent attacks. Support Vector Machines (SVMs) are one of the most promising supervised machine learning (ML) algorithms that can be applied to the identification of traffic in IP networks as well as detection of abnormal traffic. SVM shows better performance because it can avoid local optimization problems existed in many supervised learning algorithms. However, as a binary classification approach, SVM needs more research in multiclass classification. In this paper, we proposed an abnormal traffic identification system(ATIS) that can classify and identify multiple attack traffic applications. Each component of ATIS is introduced in detail and experiments are carried out based on ATIS. Through the test of KDD CUP dataset, SVM shows good performance. Furthermore, the comparison of experiments reveals that scaling and parameters has a vital impact on SVM training results.
{"title":"Identification of Abnormal Network Traffic Using Support Vector Machine","authors":"Lingjing Kong, Guowei Huang, Keke Wu","doi":"10.1109/PDCAT.2017.00054","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00054","url":null,"abstract":"Network traffic identification has been a hot topic in network security area. The identification of abnormal traffic can detect attack traffic and helps network manager enforce corresponding security policies to prevent attacks. Support Vector Machines (SVMs) are one of the most promising supervised machine learning (ML) algorithms that can be applied to the identification of traffic in IP networks as well as detection of abnormal traffic. SVM shows better performance because it can avoid local optimization problems existed in many supervised learning algorithms. However, as a binary classification approach, SVM needs more research in multiclass classification. In this paper, we proposed an abnormal traffic identification system(ATIS) that can classify and identify multiple attack traffic applications. Each component of ATIS is introduced in detail and experiments are carried out based on ATIS. Through the test of KDD CUP dataset, SVM shows good performance. Furthermore, the comparison of experiments reveals that scaling and parameters has a vital impact on SVM training results.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133916874","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00015
Yeh-Cheng Chen, C. Chu, Hung-Min Sun, Jyh-haw Yeh, Ruey-Shun Chen, C. Koong
Abstract-The equipment lock has been an important tool for the power company to protect the electricity metering equipment. However, the conventional equipment lock has two potential problems: vandalism and counterfeiting. To fulfill the control and track the potential illegal behavior, the human labor and paper are required to proceed with related operations, resulting in the consumption of a large amount of human resources and maintenance costs. This study focused on the design of RFID technology applied to the traditional equipment lock, which, through the mobile and electronic technology, strengthens the management/operating convenience of the lock and provides the solutions for anti-counterfeiting and spoilage detection so that the national energy can be properly protected and fairly distributed.
{"title":"Development of an Intelligent Equipment Lock Management System with RFID Technology","authors":"Yeh-Cheng Chen, C. Chu, Hung-Min Sun, Jyh-haw Yeh, Ruey-Shun Chen, C. Koong","doi":"10.1109/PDCAT.2017.00015","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00015","url":null,"abstract":"Abstract-The equipment lock has been an important tool for the power company to protect the electricity metering equipment. However, the conventional equipment lock has two potential problems: vandalism and counterfeiting. To fulfill the control and track the potential illegal behavior, the human labor and paper are required to proceed with related operations, resulting in the consumption of a large amount of human resources and maintenance costs. This study focused on the design of RFID technology applied to the traditional equipment lock, which, through the mobile and electronic technology, strengthens the management/operating convenience of the lock and provides the solutions for anti-counterfeiting and spoilage detection so that the national energy can be properly protected and fairly distributed.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124053128","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00019
Yongqiang Gao, Hengchao Wei
With the rising demand on internet online services, more and more individuals and organizations migrate their data and services from local to geographically distributed internet data centers for reliability, management, and cost benefits. From internet data center operators’ perspective, profit is one of the most significant factors which are to be taken into account and it is mainly determined by the quality of service and the electricity cost. In this work, we jointly take the diversity of time-varying electricity prices, the quality of service and the power usage effectiveness of data centers into consideration and propose a profit optimization framework that includes three important decisions: requests dispatching, the number of active servers and frequency adjustment. An efficient heuristic algorithm is then developed to provide internet data center operators with the advice on these decisions to achieve profit maximization for geographically distributed data centers. Through extensive trace-driven simulations, we demonstrate the effectiveness and superiority of our solution.
{"title":"Profit-Aware Workload Management for Geo-Distributed Data Centers","authors":"Yongqiang Gao, Hengchao Wei","doi":"10.1109/PDCAT.2017.00019","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00019","url":null,"abstract":"With the rising demand on internet online services, more and more individuals and organizations migrate their data and services from local to geographically distributed internet data centers for reliability, management, and cost benefits. From internet data center operators’ perspective, profit is one of the most significant factors which are to be taken into account and it is mainly determined by the quality of service and the electricity cost. In this work, we jointly take the diversity of time-varying electricity prices, the quality of service and the power usage effectiveness of data centers into consideration and propose a profit optimization framework that includes three important decisions: requests dispatching, the number of active servers and frequency adjustment. An efficient heuristic algorithm is then developed to provide internet data center operators with the advice on these decisions to achieve profit maximization for geographically distributed data centers. Through extensive trace-driven simulations, we demonstrate the effectiveness and superiority of our solution.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122243812","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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