WEKO3
AND
アイテム
{"_buckets": {"deposit": "e75f328a-d0f8-492e-9342-211a62bc934a"}, "_deposit": {"id": "3416", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "3416"}, "status": "published"}, "_oai": {"id": "oai:repository.dl.itc.u-tokyo.ac.jp:00003416"}, "item_7_alternative_title_1": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "A Method to Apply the Cluster Point Process for Short-Time Internet Traffic Traces"}]}, "item_7_biblio_info_7": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2012-03-22", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}]}]}, "item_7_date_granted_25": {"attribute_name": "\u5b66\u4f4d\u6388\u4e0e\u5e74\u6708\u65e5", "attribute_value_mlt": [{"subitem_dategranted": "2012-03-22"}]}, "item_7_degree_name_20": {"attribute_name": "\u5b66\u4f4d\u540d", "attribute_value_mlt": [{"subitem_degreename": "\u4fee\u58eb(\u60c5\u5831\u7406\u5de5\u5b66)"}]}, "item_7_description_5": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u306e\u7279\u6027\u628a\u63e1\u3084, \u7570\u5e38\u691c\u51fa\u306a\u3069\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u305f\u3081, \u3053\u308c\u307e\u3067\u3055\u307e\u3056\u307e\u306a\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u306e\u30e2\u30c7\u30eb\u5316\u304c\u63d0\u6848\u3055\u308c\u3066\u304d\u305f. \u672c\u7814\u7a76\u3067\u306f, \u30de\u30eb\u30c1\u30b9\u30b1\u30fc\u30eb\u30ac\u30f3\u30de\u30e2\u30c7\u30eb\u3068\u3044\u3046\u30e2\u30c7\u30eb\u306b\u7740\u76ee\u3057, \u7814\u7a76\u3092\u884c\u3063\u305f. \u4e00\u65b9, \u5e83\u5e2f\u57df\u306e\u30d0\u30c3\u30af\u30dc\u30fc\u30f3\u30ea\u30f3\u30af\u3067\u306f, \u3055\u307e\u3056\u307e\u306a\u6280\u8853\u7684\u306a\u5236\u9650\u306e\u305f\u3081, \u30d1\u30b1\u30c3\u30c8\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u624b\u6cd5\u3092\u7528\u3044\u3066\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u306e\u7279\u5fb4\u628a\u63e1\u3092\u884c\u3046\u3053\u3068\u304c\u4e00\u822c\u7684\u3068\u306a\u3063\u3066\u3044\u308b. \u672c\u7814\u7a76\u3067\u306f\u307e\u305a, MAWI Traffic Trace\u3068\u3044\u3046\u65e5\u7c73\u9593\u306e\u5b9f\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u30c7\u30fc\u30bf\u3092\u7528\u3044\u3066\u30d1\u30b1\u30c3\u30c8\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306e\u5f71\u97ff\u304c\u30de\u30eb\u30c1\u30b9\u30b1\u30fc\u30eb\u30ac\u30f3\u30de\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066\u3069\u306e\u3088\u3046\u306b\u53ca\u3076\u306e\u304b\u306e\u8abf\u67fb\u3092\u884c\u3044, \u305d\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u52d5\u304d\u304c\u5927\u304d\u304f\u4e8c\u3064\u306e\u72b6\u614b\u306b\u5206\u304b\u308c\u308b\u3053\u3068\u3092\u793a\u3057\u305f. \u3057\u304b\u3057, \u5b9f\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u306b\u306f\u7d71\u8a08\u7684\u306a\u7570\u5e38\u304c\u542b\u307e\u308c\u3066\u304a\u308a, \u7d50\u679c\u306e\u4e00\u822c\u5316\u306b\u304a\u3044\u3066\u56f0\u96e3\u304c\u4f34\u3046\u3053\u3068\u304c\u660e\u3089\u304b\u3068\u306a\u3063\u305f. \u305d\u3053\u3067, \u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u306e\u518d\u751f\u6210\u306e\u624b\u6cd5\u3068\u3057\u3066\u6709\u52b9\u3067\u3042\u308b\u3068\u3055\u308c\u3066\u3044\u308bCluster Point Process\u3092\u53d6\u308a\u4e0a\u305f\u304c, \u77ed\u6642\u9593\u306e\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u306b\u5bfe\u3057\u3066Cluster Point Process\u3092\u305d\u306e\u307e\u307e\u9069\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u306a\u3044\u3053\u3068\u3092\u767a\u898b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u305f. \u305d\u3053\u3067, \u3053\u306e\u539f\u56e0\u3092\u8abf\u67fb\u3057, \u554f\u984c\u304c\u8d77\u3053\u308b\u539f\u56e0\u306e\u7279\u5b9a\u3068, \u305d\u308c\u3092\u56de\u907f\u3059\u308b\u305f\u3081\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u624b\u6cd5\u3092\u63d0\u6848\u3057, \u305d\u306e\u8a55\u4fa1\u3092\u884c\u3063\u305f. \u305d\u306e\u7d50\u679c, \u63d0\u6848\u3057\u305f3\u3064\u306e\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u624b\u6cd5\u306e\u3046\u3061\u6700\u3082\u3088\u3044\u624b\u6cd5\u3092\u540c\u5b9a\u3057, \u5143\u306e\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u624b\u6cd5\u3088\u308a\u3082\u3088\u3044\u7279\u6027\u3092\u6301\u3064\u3053\u3068\u3092\u793a\u3057\u305f.", "subitem_description_type": "Abstract"}, {"subitem_description": "Wide variety of Internet traffic model has been proposed to understand characteristics of traffic and is applied to various applications(e.g., anomaly detection methods). This paper focuses on multi-scale gamma model as a traffic model. Since packet sampling is common in current high speed backbone network, the evaluation on the effects of the each sampling method is important so as to adopt the particular traffic model for the given sampling method. This paper challenges this issue by using actual academic traffic captured between Japan and the United States and finds that parameter dynamics can be divided to two parts, depending on each sampling methods. To generalize this result, we employ the Cluster Point Process to generate synthetic traffic, and obtain clearer view of the parameter dependency of the model. However synthetic traffic generated by the Cluster Point Process fails to have same characteristics of the original traffic. We point out reasons of the failure and propose a solution that is a new method of parameter tuning of the Cluster Point Process. We evaluate this method by using the Logscale Diagram, and choose the best method to fit it.", "subitem_description_type": "Abstract"}]}, "item_7_full_name_3": {"attribute_name": "\u8457\u8005\u5225\u540d", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "8207", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Motodate, Takuya"}]}]}, "item_7_select_21": {"attribute_name": "\u5b66\u4f4d", "attribute_value_mlt": [{"subitem_select_item": "master"}]}, "item_7_subject_13": {"attribute_name": "\u65e5\u672c\u5341\u9032\u5206\u985e\u6cd5", "attribute_value_mlt": [{"subitem_subject": "547", "subitem_subject_scheme": "NDC"}]}, "item_7_text_24": {"attribute_name": "\u7814\u7a76\u79d1\u30fb\u5c02\u653b", "attribute_value_mlt": [{"subitem_text_value": "\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u96fb\u5b50\u60c5\u5831\u5b66\u5c02\u653b"}]}, "item_7_text_36": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_text_value": "Thesis"}]}, "item_7_text_4": {"attribute_name": "\u8457\u8005\u6240\u5c5e", "attribute_value_mlt": [{"subitem_text_value": "\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u96fb\u5b50\u60c5\u5831\u5b66\u5c02\u653b"}, {"subitem_text_value": "Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "\u672c\u8218, \u62d3\u4e5f"}], "nameIdentifiers": [{"nameIdentifier": "8206", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2017-05-31"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "48106435.pdf", "filesize": [{"value": "1.4 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1400000.0, "url": {"label": "48106435.pdf", "url": "https://repository.dl.itc.u-tokyo.ac.jp/record/3416/files/48106435.pdf"}, "version_id": "f9d57505-f9dc-4216-aa81-7581f04371f3"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "\u5b9f\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u30c7\u30fc\u30bf\u3078\u306eCPP\u624b\u6cd5\u306e\u9069\u7528\u3068\u305d\u306e\u6539\u5584\u306b\u95a2\u3059\u308b\u7814\u7a76", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "\u5b9f\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u30c7\u30fc\u30bf\u3078\u306eCPP\u624b\u6cd5\u306e\u9069\u7528\u3068\u305d\u306e\u6539\u5584\u306b\u95a2\u3059\u308b\u7814\u7a76"}]}, "item_type_id": "7", "owner": "1", "path": ["9/233/234", "34/105/262"], "permalink_uri": "http://hdl.handle.net/2261/51740", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2012-05-29"}, "publish_date": "2012-05-29", "publish_status": "0", "recid": "3416", "relation": {}, "relation_version_is_last": true, "title": ["\u5b9f\u30a4\u30f3\u30bf\u30fc\u30cd\u30c3\u30c8\u30c8\u30e9\u30d5\u30a3\u30c3\u30af\u30c7\u30fc\u30bf\u3078\u306eCPP\u624b\u6cd5\u306e\u9069\u7528\u3068\u305d\u306e\u6539\u5584\u306b\u95a2\u3059\u308b\u7814\u7a76"], "weko_shared_id": null}
実インターネットトラフィックデータへのCPP手法の適用とその改善に関する研究
http://hdl.handle.net/2261/51740
c080fbe4-63a0-4431-ab4b-9174af6ca9a5
| 名前 / ファイル | ライセンス | アクション | |
|---|---|---|---|
48106435.pdf (1.4 MB)
|
|
| Item type | 学位論文 / Thesis or Dissertation(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2012-05-29 | |||||
| タイトル | ||||||
| タイトル | 実インターネットトラフィックデータへのCPP手法の適用とその改善に関する研究 | |||||
| 言語 | ||||||
| 言語 | jpn | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_46ec | |||||
| タイプ | thesis | |||||
| その他のタイトル | ||||||
| その他のタイトル | A Method to Apply the Cluster Point Process for Short-Time Internet Traffic Traces | |||||
| 著者 |
本舘, 拓也
× 本舘, 拓也 |
|||||
| 著者別名 | ||||||
| 識別子 | ||||||
| 識別子 | 8207 | |||||
| 識別子Scheme | WEKO | |||||
| 姓名 | ||||||
| 姓名 | Motodate, Takuya | |||||
| 著者所属 | ||||||
| 東京大学大学院情報理工学系研究科電子情報学専攻 | ||||||
| 著者所属 | ||||||
| Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo | ||||||
| Abstract | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | インターネットトラフィックの特性把握や, 異常検出などのアプリケーションのため, これまでさまざまなトラフィックのモデル化が提案されてきた. 本研究では, マルチスケールガンマモデルというモデルに着目し, 研究を行った. 一方, 広帯域のバックボーンリンクでは, さまざまな技術的な制限のため, パケットサンプリングの手法を用いてトラフィックの特徴把握を行うことが一般的となっている. 本研究ではまず, MAWI Traffic Traceという日米間の実トラフィックデータを用いてパケットサンプリングの影響がマルチスケールガンマモデルに対してどのように及ぶのかの調査を行い, そのパラメータの動きが大きく二つの状態に分かれることを示した. しかし, 実トラフィックには統計的な異常が含まれており, 結果の一般化において困難が伴うことが明らかとなった. そこで, トラフィックの再生成の手法として有効であるとされているCluster Point Processを取り上たが, 短時間のトラフィックに対してCluster Point Processをそのまま適用することができないことを発見することができた. そこで, この原因を調査し, 問題が起こる原因の特定と, それを回避するためのパラメータチューニング手法を提案し, その評価を行った. その結果, 提案した3つのチューニング手法のうち最もよい手法を同定し, 元のチューニング手法よりもよい特性を持つことを示した. | |||||
| Abstract | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Wide variety of Internet traffic model has been proposed to understand characteristics of traffic and is applied to various applications(e.g., anomaly detection methods). This paper focuses on multi-scale gamma model as a traffic model. Since packet sampling is common in current high speed backbone network, the evaluation on the effects of the each sampling method is important so as to adopt the particular traffic model for the given sampling method. This paper challenges this issue by using actual academic traffic captured between Japan and the United States and finds that parameter dynamics can be divided to two parts, depending on each sampling methods. To generalize this result, we employ the Cluster Point Process to generate synthetic traffic, and obtain clearer view of the parameter dependency of the model. However synthetic traffic generated by the Cluster Point Process fails to have same characteristics of the original traffic. We point out reasons of the failure and propose a solution that is a new method of parameter tuning of the Cluster Point Process. We evaluate this method by using the Logscale Diagram, and choose the best method to fit it. | |||||
| 書誌情報 | 発行日 2012-03-22 | |||||
| 日本十進分類法 | ||||||
| 主題 | 547 | |||||
| 主題Scheme | NDC | |||||
| 学位名 | ||||||
| 学位名 | 修士(情報理工学) | |||||
| 学位 | ||||||
| 値 | master | |||||
| 研究科・専攻 | ||||||
| 情報理工学系研究科電子情報学専攻 | ||||||
| 学位授与年月日 | ||||||
| 学位授与年月日 | 2012-03-22 | |||||
