From mboxrd@z Thu Jan 1 00:00:00 1970 Return-Path: X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on aws-us-west-2-korg-lkml-1.web.codeaurora.org X-Spam-Level: X-Spam-Status: No, score=-0.5 required=3.0 tests=DKIM_ADSP_CUSTOM_MED, DKIM_INVALID,DKIM_SIGNED,FREEMAIL_FORGED_FROMDOMAIN,FREEMAIL_FROM, HEADER_FROM_DIFFERENT_DOMAINS,HTML_MESSAGE,MAILING_LIST_MULTI,SPF_HELO_NONE, SPF_PASS autolearn=no autolearn_force=no version=3.4.0 Received: from mail.kernel.org (mail.kernel.org [198.145.29.99]) by smtp.lore.kernel.org (Postfix) with ESMTP id 625FFC47404 for ; Wed, 9 Oct 2019 08:25:25 +0000 (UTC) Received: from shelob.surriel.com (shelob.surriel.com [96.67.55.147]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail.kernel.org (Postfix) with ESMTPS id 207092133F for ; Wed, 9 Oct 2019 08:25:24 +0000 (UTC) Authentication-Results: mail.kernel.org; dkim=fail reason="signature verification failed" (2048-bit key) header.d=gmail.com header.i=@gmail.com header.b="MlE70JFf" DMARC-Filter: OpenDMARC Filter v1.3.2 mail.kernel.org 207092133F Authentication-Results: mail.kernel.org; dmarc=fail (p=none dis=none) header.from=gmail.com Authentication-Results: mail.kernel.org; spf=fail smtp.mailfrom=kernelnewbies-bounces@kernelnewbies.org Received: from localhost ([::1] helo=shelob.surriel.com) by shelob.surriel.com with esmtp (Exim 4.92.3) (envelope-from ) id 1iI7H4-0002P8-G1; Wed, 09 Oct 2019 04:24:46 -0400 Received: from mail-qt1-x834.google.com ([2607:f8b0:4864:20::834]) by shelob.surriel.com with esmtps (TLSv1.2:ECDHE-RSA-AES128-GCM-SHA256:128) (Exim 4.92.3) (envelope-from ) id 1iI7H1-0002Oz-LE for kernelnewbies@kernelnewbies.org; Wed, 09 Oct 2019 04:24:43 -0400 Received: by mail-qt1-x834.google.com with SMTP id o12so2197136qtf.3 for ; Wed, 09 Oct 2019 01:24:42 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:from:date:message-id:subject:to; bh=3vZ76lxl0lFjH70ywjvrDMoOgM2kGj0dXotUOK9nG3g=; b=MlE70JFfUdKMOVxLWRuaTvP5pI02JgfX5UdLVFkvbsju5V7NqFPvw3+5H2Rg4pY3JO H6YM6a7B+i9enTBqZbq1DW6KsTEu3QH7Cw58xoKZ/UkWDuLbtTbyWXZnzxkFxlxb+mp8 Dd4NZTnqbll35IiSCrmCEqnNhfOaXn8f63cariXK5+dSxa50ROaCuZRA66H4X0DjCX0N Z56D+5JJkke998Li8rBJiEDFkiM/bQoxdQ4JvGCMWYygfgffyoIwmIgr+CN2X3QVrnTD FgVccYt13WcQasq3k+vdJClX83SjXTlwz6s+39Guxl98xL85IrQBmnZElJX9XhobIgJn 9rDA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:from:date:message-id:subject:to; bh=3vZ76lxl0lFjH70ywjvrDMoOgM2kGj0dXotUOK9nG3g=; b=gmVq50Vf6yBkSwT5fJaO+aD4Sb9pUEf+F2/ttSdsbqiz5/SWf5/qsFZBUdZ8Jc2/JV 2tXtR4Yk0TL/LIuGywXzf6oslgw0BosPNgXqLFZz82mVBg9YaHm0NlZ3rBCloripDOhy TmGzqaDfxWnUJHdv1kiN0R3qplBFOvxAPA9cQtc1xPT9UNMIjg/mFQPdbesphL+eGema 3GD18SGpC6GIU7kXAMAlSLhI/ESOx1ZhCshF2O8tfJzNxUCjCHmV3JTnUWsgPJjmic9d APacACeAQobPbhPtBWXZf/gQysmK/em4ViWD3FQN6iIQjiLyx9cu1/kbefI6k/zeJkMR mDkw== X-Gm-Message-State: APjAAAXdE9UduftS6xN1QixUD4/3nhHAkE+0sByH3fpbVDHglasPMV8N r0hXgeSs61Gaw4UIyy78lQqfg6PcDPLWPfqnIq7b92mA X-Google-Smtp-Source: APXvYqwdUW8aWp2o7S4QTc+P82faF3ZZLoONKqsbQUTlWFnXBHuDRwl8fLOjA6aFP2XCg7CYMYFAYnm1oiBygb6OacM= X-Received: by 2002:a0c:8884:: with SMTP id 4mr2454355qvn.248.1570609420401; Wed, 09 Oct 2019 01:23:40 -0700 (PDT) MIME-Version: 1.0 From: prathamesh naik Date: Wed, 9 Oct 2019 01:23:28 -0700 Message-ID: Subject: Predicting Process crash / Memory utlization using machine learning To: kernelnewbies@kernelnewbies.org X-BeenThere: kernelnewbies@kernelnewbies.org X-Mailman-Version: 2.1.15 Precedence: list List-Id: Learn about the Linux kernel List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Content-Type: multipart/mixed; boundary="===============0279421871517029687==" Errors-To: kernelnewbies-bounces@kernelnewbies.org --===============0279421871517029687== Content-Type: multipart/alternative; boundary="000000000000957e26059476007d" --000000000000957e26059476007d Content-Type: text/plain; charset="UTF-8" Hi all, I want to work on project which can predict kernel process crash or even user space process crash (or memory usage spikes) using machine learning algorithms. Can someone point me what all data can be useful for tuning my algorithm ? is there already paper on this (could not find much articles on this) ? Thanks, Prathamesh --000000000000957e26059476007d Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable
Hi all,
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 I wa= nt to work on project which can predict kernel process crash or even user s= pace process crash (or memory usage spikes) using machine learning algorith= ms. Can someone point me what all data can be useful for tuning my algorith= m ? is there already paper on this (could not find much articles on this) ?=

Thanks,
Prathamesh=C2=A0
--000000000000957e26059476007d-- --===============0279421871517029687== Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit Content-Disposition: inline _______________________________________________ Kernelnewbies mailing list Kernelnewbies@kernelnewbies.org https://lists.kernelnewbies.org/mailman/listinfo/kernelnewbies --===============0279421871517029687==--