Importance of Data Profiling in Synthetic Monitoring

 Fault Management, Performance Management  Comments Off on Importance of Data Profiling in Synthetic Monitoring
Apr 092017
 

Monitoring of web services/sites via Synthetic Monitoring Systems is a common practice that is implemented by most of the companies. Cloud-based monitoring platforms give us the ability to watch our services from the Internet/ through the eyes of our customers.

However, the alarms that are emitted through these platforms are limited, most of the times, to the faults. They tell us our web service is not reachable at all, which is the worst text message a system admin would receive at 3 AM in the morning.

Can these systems be used for proactive monitoring? Yes, and this article is about this practice.

Based on your cloud-based monitoring provider capabilities, you can apply several proactive monitoring practices. But, before that, we need to elaborate on the only KPI we have for an Internet-initiated synthetic service check: Response Time.

Response Time is affected by several factors: Network delays, DNS resolution delays or Service (HTTP/FTP etc) delays.

These delays can be measured by most of the cloud-based monitoring platforms and can provide some insight to the upcoming outage that will occur soon.

How these KPIs can be used for pro-active monitoring, though, depends on the capabilities of your monitoring platform.

Monitoring platforms will usually allow you to put threshold values for several response time types that are mentioned above. Whenever the response time is above that threshold, these systems will emit alarms on several channels to alert the system administrators. Response times will rise in several occasions. These are:

· DNS issues (less likely but can happen)

· Network congestion/saturation

· HTTP Server issues (too many connections, load balancer problems, backend slowness such as RDBMS or external service provider calls)

But how will we know the threshold values? Well, we need to do some investigation for that. The easiest way is to look at the daily/weekly/monthly reports to figure out our traffic profile by ourselves. By simple calculations, we can come up with some approximate response time numbers.

The problem with the manual profiling is its tendency to lead to errors especially if your monitoring points is on the Internet. The Internet is a very dynamic environment and you have no control over it. The Internet’s this dynamic nature will result in fluctuating response times which will give us hard times to predict the thresholds. The other factor to consider is our own business. We have peak times and maintenance periods. During these times our response times may also rise and this is an expected behavior.

How can we set the correct thresholds in this very dynamic environment? There are certain steps to increase the predictability:

1-) Get Closer

If your service resides in Europe, and your customers are in Europe, there is no point of monitoring it from Hong Kong. Getting closer to your target means fewer routers to be passed along the way and reduced fluctuations in monitored response times.

2-) Create an Automated Baseline

Creating a baseline should be automatic to reflect your business. Say you take backups at 2 AM every Monday. Or your traffic goes rocket high on every Friday between 5PM-8PM. You are prepared for these events but if your monitoring system is not, you will receive false-positive alarms indicating“slowness” in your services. Your monitoring provider should provide the automatic profiling option based on hours and/or weeks. The profiling should be done at least on an hourly basis. So, if I select the weekly profiling option, the system should calculate the duration based on an algorithm (mean/median etc) for that weekday – hour. This way, the system will “capture” the event of our weekly backup event and will not emit any alarms for that event.

Following screenshot was taken from a monitoring system platform: PingTurk. This platform localizes the monitoring to the city level to increase the effectiveness of the synthetic monitoring. The table shows the calculated baseline response times for day-hour tuples per city in Turkey.

This example follows the 2 steps mentioned above (being near and baselining automatically) and will reduce the number of false-positive alarms during the operation.

The effectiveness can even be improved by introducing a local monitoring point located in the customer infrastructure (say at the DMZ). This way, we can be sure that the slowness is caused by the Internet or from our own infrastructure.

In-Flight Order Management

 Order management  Comments Off on In-Flight Order Management
Sep 092016
 

In Order Management, an in-flight order means an order that is amending or cancelling an already running order. The reasons differ. After getting the order, your customer may call you back and say he wants a new order item, or he may want to cancel the order.

Both of the operations (revise or cancel), needs some checks before accepting the order. The first and most important check is PoNR or ‘Point of No Return’ check. In Order Management, PoNR is an Order state. After that state is set, the Order cannot be cancelled or amended. Most of the cases, PoNR is the network activation or shipment and it is there to protect the provider from extra costs. For example, you may have a network activation order where 10 branch locations should be provisioned and activated. 2 of these locations does not have any connectivity at all, so a fiber cable should be laid to the customer location. This work is typically done by 3rd part supplier/partners who are contracted on an hourly basis. Also for the digging work, expensive permits may be required from regulatory bodies. In a much simpler scenario, a retailer may have shipped the goods and they may on their way. There may be no way to cancel the shipment at that moment.
PoNR is communicated with the customer at the order capturing time, so the customer is aware of it.

After the PoNR is checked, cancel and revise operations can be validated. For cancel, no validation is necessary. Typically, the customer will contact the contact center and ask for cancellation. The order id that is acquired at the order submission time will be used to cancel the in-flight order. After the ‘Cancel Order’ is received by the OM platform, the running workflow is signaled and if implemented, all the rollback scripts are run before setting the order state to ‘Cancelled’.

Revise operation is a little bit trickier as it requires a catalog validation step. The amendment order may be breaking some catalog rules so before submitting it from the order capturing platform (or after the submission, by order manager), the validation rules should be executed. After that point, the in-flight order is cancelled and a new order is created with the same order id as the previous one. The changing property is the revision number of the order. The revision is incremented by 1, indicating that the Order had some changes in the past. It should be noted that different OM vendors may be implementing different strategies here. The preferable way is to keep the order id same for consistent customer experience, however, some platforms may not allow doing so. It is better to check the implementation details with the OM vendors before designing your end to end flows.

Aug 302016
 

In OSS, we use the polling concept often to pull statistics and configuration data from the devices. If the devices we are dealing with are implementing the pull based protocols such as SNMP or FTP, we cannot get rid of this.

All types of polling processes come with a polling period. If I have 100 routers and a polling period of 5 minutes, each and every 5 minutes I will have to connect each device and pull the necessary KPIs to be injected into my DataMart.

If you look at the CPU and Memory utilization of a performance management server (poller) during the process, you will see high peaks at the start of the polling periods. If we follow the 5 minutes polling example above, we will see the peaks at the minutes, for example, 0,5,10,15,20,25,30,35,40,45,50,55. If your polling period is 5 minutes, you have 5 minutes to finish your job. If it exceeds that period, you will fall into data consistency issues. As the node and KPI count increase, you have to throw more hardware to finish soon. (For each device connection, we will most probably want to open up a separate thread until we hit the point of diminishing returns)

Considering the whole collection process does not occupy the whole 5 minutes’ period, the remaining period will be wasted in the waiting state for the server. Since the hardware configuration was designed for the peak times, our server will remain to be “expensive”.

Assigning a polling time to a specific node is the key to this problem. In this approach, we divide the polling period to sub-periods. So, if the polling period is 5 minutes, we can divide it like:

10 nodes Zeroth second of First Minute, 10 nodes Thirtieth second of First Minute, 10 nodes Zeroth second of Second Minute, 10 nodes Thirtieth second of Second Minute…

Here we put 10 nodes into each 30 seconds timeframe, to finalize polling of 100 nodes in 5 minutes.

We also need to consider the speed of these nodes. Some nodes will suffer performance problems due to weak hardware configuration or high load. The response time of those may exceed the 30 seconds timeframe.

In order to cope with this problem, we should also consider putting the slowest responding nodes to the earliest sub-frames. This way, a node’s polling can “extend” to the next subframe and can still be finalized in the given 5 minutes. This, of course, requires you to maintain a continuous baseline of node response times at the server side.

Splitting the polling period and distributing the nodes wisely to the sub-periods will help you to reduce your hardware costs.

Aug 152016
 

Today’s topic is about the Network Sweeping and how it can be optimized. As you may know from the previous topics, sweeping means searching a subnet by attempting to connect to each and every possible IP addresses it has.  Usually, the initial protocol is ICMP due to its’ low overhead. (In that case, the sweep is called Ping Sweep). SNMP and even HTTP interfaces are also used as sweep protocols.

Sweeping is used in different domains, such as;

  • Security
  • Inventory Management
  • Performance Management
  • Configuration Management

Sweeping could be time and resource consuming (both for sender and receiver side). That’s why, for most enterprise customers, it is normally done daily.

For large networks, it may take hours to complete a sweeping process. Consider the scenario of sweeping a class C IP subnet. (It will have at least 254 IP addresses.). Also, suppose that only 10 devices exist in that subnet. I am supposing I will be using ICMP for discovery. That is the simple ping request and at least I need to send 2 ICMP packets to be sure that there is a device there. (50% packet loss still means the remote side is up)

For the reachable devices, the round-trip ping time should not exceed 5ms. Considering we have 2 ICMP packets, it would be 10ms per check. We have 10 devices and it would take around 100ms which is well below 1 sec. That’s a great performance if you just consider pinging the “up” devices. But what about the remaining 244 down ones?

ICMP timeout kicks in when dealing with the dead devices or vacant IP addresses. ICMP timeout is the duration in milliseconds for the ping software will wait until an ICMP echo reply package arrives. If the packet does not arrive within that period, it will report it as “down”. The default timeout for ICMP in Cisco routers is 2 seconds. So, using the defaults, if you use 2 seconds as the timeout, for 2 packets in the test, you will have to wait 4 seconds per test. If we do the math, the total wait time for the class C subnet on hand would be 976 seconds, roughly 16 minutes. Organizations that rely on sweeping normally have much bigger subnets with thousands of possible IP addresses. The sweeping process would take hours in such kind of networks.

Luckily, we can tweak this process so it will take less time.

1: Use of Parallel Measurements:

This is the first thing we need to do. Opening multiple threads of ICMP operation at the same time. How about opening up 1000 threads? It will be finished in 4 seconds. Isn’t it great? Not really, it has some consequences.

  • Increased LAN traffic: Sending 1000 ICMP packets at the same second will generate lots of traffic in your LAN/WAN. (around 70 bytes per packet * 1000 threads = 70000 bytes/sec =560000 bits/sec = 560Kbps one-way traffic. Considering there would be replies to these requests, the total bandwidth consumption can easily reach 1Mbps.
  • CPU Cycles: Each thread will consume CPU and Memory resources. Source machine should be able to cope with this. 

This is just the sweeping part of it. In the real world scenarios, no inventory or security tool will stop there after it discovered a live IP address. It will go ahead and try to fetch more information. So these two parameters can boost if you open up too many threads.

2: Optimize your ICMP Packet Timeout

I told that the default ICMP timeout is 2 seconds. Luckily this is configurable. Go ahead and send some pings to those destination IP addresses. For the “live” ones, capture the round trip time. This is the network delay (plus the processing delay of the remote NIC). That delay will not change much on LAN links, may slightly change on WAN links. Baseline this. So if it is 100msec you can easily put a timeout of 300 msec. This is 3 times more than the baseline but still well below 2 seconds default.

Keep in mind that ICMP is one of the protocols which has the lowest overhead. Layer 7 protocols like SNMP and HTTP will have much more overhead, so above suggestions may bring greater value.

Long sweep times can also result in inconsistencies between the sweep periods. Suppose you started with 10.1.1.1 /24 and found out that 10.1.1.1 is vacant. You continue your sweeping and 10 seconds later 10.1.1.1 became up. If you sweep every day, your inventory (and other dependent OSS systems) will not know this device until the next day. (If you don’t have a change process in place for this device) That’s why there should be a mechanism to listen for new IP address activity during the sweep time. DHCP logs could be a good alternative for the networks that utilize DHCP for IP addressing. A costlier solution could be listening for Syslog events or switch span ports.

Order Capturing

 Order management  Comments Off on Order Capturing
Oct 222015
 

Today we are going to talk about Order Capturing from the Order Management domain.

What is an Order?

When you ask this question to the customer, he/she will reply, it is the collection of goods and services I am demanding from the provider.

However, from the Provider (and also Order Management) perspective, it is more than that. Order is a complex data structure which includes goods/services that are demanded plus any additional information such as shipping, payment, service location etc. that will help with the fulfillment of the order.

At the end of the day, the primary responsibility of Order Management is to run the necessary orchestration and fulfill an order. It does so by interfacing the infrastructure (network, IT, OSS/BSS), functional groups in the organization, customer and other enabling organizations (shipment providers, payment providers etc.)

Most of the time, the Order’s primary components are the goods/services. These goods and services should be presented in the Product Catalogs, so that the customers can browse and pick the ones they need. At this “selection” stage, we tend not to call these selected items as Order yet, rather we tend to call it Cart. (We also see the terms Shopping Cart and Basket which refers to the same concept)

So, the customer-selected products are placed to a Cart object.  After the customer is done with his selection, this cart should be freeze. This stage is called “Checkout” phase. Most of us know this from our online shopping experiences. At some moment we are asked to hit the “Checkout” button. After the checkout stage, we cannot (or supposed not to) change what is inside the cart. (Why is that? Because Cart belongs to another domain: Catalog Management. The items, the rules between the items, the checks that are done against these items are all reside at the catalog side and we will be using its’ interfaces to construct our Cart object. We cannot change anything in this goods/services list before consulting the Catalog Manager. If we do so, we will break the integrity. We checkout, to leave the Catalog Management domain and enter to another domain: Order Management.)

Can this checked-out cart object be sent to the order management right away? No, because we do not know how to fulfill it yet. We can instantiate the products for that customer but how their rates will be collected? If there are physical goods in the list, where they will be shipped to? Without this additional information, the order cannot be fulfilled. That’s why, the Order Capture platform, will also attempt to collect these details. Some basic validations can also be done at this stage (other than the Catalog Validations, which I will write in another blog post)

The Order, prepared at the Order Capture platform (which includes Cart Information + Additional Information + Customer Information) can now be sent to the Order Management for fulfillment. The OM platform will return an identifier to the Order Capture platform to be used for future queries regarding that specific order. Most of the times, the Order Capture platform is also triggered asynchronously about the key status changes of the order at the OM side (Pending, Error, Cancelled, Success etc.)

Order Capturing, is mostly achieved by CRM systems and Enterprise Portals which allow customizations. In the absence of such system, some add-on tools offered by most of the OM providers can be used. Regardless of the tool type, we should be ready for heavy customizations as each provider’s way of handling the orders differ from the others.