[{"data":1,"prerenderedAt":79},["ShallowReactive",2],{"blog-improve-mean-time-to-resolution-using-nmis-automated-base-lining":3},{"id":4,"title":5,"author":6,"body":7,"categories":61,"category":62,"date":63,"description":64,"extension":65,"featured":66,"fields":61,"image":20,"meta":67,"modified":61,"navigation":68,"path":69,"seo":70,"slug":71,"stem":72,"tags":73,"__hash__":78},"blog\u002Fblog\u002Fimprove-mean-time-to-resolution-using-nmis-automated-base-lining.md","Improve Mean Time to Resolution Using NMIS Automated Baselining","Luke Richardson",{"type":8,"value":9,"toc":58},"minimark",[10,14,21,24,27,32,35,40,43,46],[11,12,13],"p",{},"Managing a large complex environment with ever-changing operational states is challenging. Several of our engineers who previously managed shifts in large 24-hour Network Operation Centres described how they used Automated Live Baselining when starting a shift and during shift handovers to immediately understand a network's current health and recent history. NMIS Live Baselining was able to provide them with a fast synopsis of current network stability and an ability to quickly drill into the most relevant occurrences.",[11,15,16],{},[17,18],"img",{"alt":19,"src":20},"","\u002Fimages\u002Fblog\u002FNMIS-Metrics-Health-700.png",[11,22,23],{},"Live baselining is achieved by automatically calculating a single \"network wide\" health metric, reachability metric and availability metric as seen in this graph.",[11,25,26],{},"The secret to showing engineers where to look for deteriorating conditions is that the current state (metrics) of your network are then continuously compared against a rolling period (configurable, last 8 hours by default) to see whether performance is deteriorating or improving. This is done not only on the network as a whole but also on subsections (groups) of your network.",[11,28,29],{},[17,30],{"alt":19,"src":31},"\u002Fimages\u002Fblog\u002FNMIS-Baseline-700.png",[11,33,34],{},"Status trends are reported visually using arrows to show if health is improving or declining. You can then drill down into more detailed KPI data for an individual device.",[11,36,37],{},[17,38],{"alt":19,"src":39},"\u002Fimages\u002Fblog\u002FNMIS-KPIs-500.png",[11,41,42],{},"The visual alerts, along with the ability to very quickly access detailed information relating to performance deterioration, allow an organisation to identify issues and make corrective decisions faster -- vastly improving mean time to resolution (MTTR).",[44,45],"hr",{},[11,47,48,52,53],{},[49,50,51],"strong",{},"Related reading:"," ",[54,55,57],"a",{"href":56},"\u002Fblog\u002Fnmis-9-network-automation\u002F","NMIS 9 Network Automation",{"title":19,"searchDepth":59,"depth":59,"links":60},2,[],null,"Network Management","2016-02-18","How NMIS live baselining gives NOC engineers immediate visibility into network health trends and helps resolve issues faster.","md",false,{},true,"\u002Fblog\u002Fimprove-mean-time-to-resolution-using-nmis-automated-base-lining",{"title":5,"description":64},"improve-mean-time-to-resolution-using-nmis-automated-base-lining","blog\u002Fimprove-mean-time-to-resolution-using-nmis-automated-base-lining",[74,75,76,77],"nmis","baselining","mttr","network operations","XQySbTM74fQWheF_W3SbdHh2UQ_y20r-8D6M2r3_Nrs",1782795857595]