Performance Monitoring of .Net Framework

References:
http://msdn.microsoft.com/en-us/library/fxk122b4.aspx
http://msdn.microsoft.com/en-us/library/w8f5kw2e.aspx
http://msdn.microsoft.com/en-us/library/ms254503%28VS.80%29.aspx
http://msdn.microsoft.com/en-us/library/aa394084%28VS.85%29.aspx
http://www.dnzone.com/go?180
http://www.oracle.com/technology/products/oem/pdf/ds_net.pdf
http://technet.microsoft.com/en-us/library/bb727100.aspx
http://technet.microsoft.com/en-us/library/cc736883(WS.10).aspx

Availability Analysis and Prediction

The issue with BezVision's performance analysis is that it assumes a fault-free system. If any thing goes wrong e.g. the database goes down, the collectors will stop and no profiles would be created at all. This makes it impossible to find out the source of the blackout and to identify the workload that caused it.

The solution is to incorporate availability and reliability analysis and prediction into BezVision's toolset.

We can gather several metrics by analysing collection failures like MTTF, Probability, Up Time, Down time, Reliability etc.

We will need a heartbeat collector with timeout for DB, heartbeat queries for node (may be running out of memory), disk (may be full try checking its space if its below threshhold.), network etc.

We can then analyse to data and predict the availability of a system at various points in time.

We can also analyse the workload that caused the system (dbms/node/disk) failure.

Availability Analysis and Audits

This section draws heavily from the paper:

Stochastic Reward Nets for Reliability Prediction

http://www.cs.ucr.edu/~ciardo/pubs/1994CRMS-Reliability.pdf

Availability Prediction

This section draws heavily from the paper:

Exploiting Availability Prediction in Distributed Systems

http://www.eecs.umich.edu/~jmickens/predictors.pdf

We have to define pre-defined states of availability.

Class-1: Completely Offline

Class-16: Completely Online

Instead of queuing networks, we have to define predictor networks. But first lets briefly describe the types of predictors mentioned in the paper.

1. Right Now Predictor

Put simply. If a system is online now, it will be online for all periods in the future. And if its offline, it will be offline for all periods.

2. Saturating Counter Predictors

A saturating counter prediction uses n-bits of state and can assume 2^n states.

e.g. SatCount-2 predictor uses a 2-bit saturating counter.

Such a counter can assume four values (-2,-1,+1,and +2) which correspond to four uptime states (strongly offline, weakly offline, weakly online, and strongly online).

During each sampling period, the counter is incremented if the node is online, otherwise it is decremented; increments and decrements cannot move the counter beyond the saturated counts of -2 and +2. Predictions for all lookahead periods use the current value of the saturating counter, i.e., negative counter values produce "offline"predictions, whereas positive values result in "online" predictions.

Saturating Counter Predictiors are pretty tolerant to occasional deviations from long stretches of uniform uptime behavior. However, like the RightNow predictors, they are inaccurate for nodes with periodic uptimes.

3. State-Based Predictors History Predictor

+ve: To predict the behavior of nodes with periodic availabilities.

These predictors explicitly represent a node’s recent uptime states using a de Bruijn graph. A de Bruijn graph over k bits has a vertex for each binary number in [0, 2k-1]. Each vertex with binary label b1b2...bk has two outgoing edges, one to the node labeled b2b3...0 and the other to the node b2b3...1.

Suppose that we represent a node’s recent availability as a k-bit binary string, with bi equal to 0 if the node was offline during the ith most recent sampling period and 1 if it was online. A k-bit de Bruijn graph will represent each possible transition between availability states.

To assist uptime predictions, we attach a 2-bit saturating counter to each vertex. These counters represent the likelihood of traversing a particular outbound edge; negative counter values bias towards the 0 path, whereas positive values bias towards the 1 path. After each uptime sampling, the counter for the vertex representing the previous uptime state is incremented or decremented according to whether the new uptime sample represented an "online" edge or an "offline" edge.

To make an uptime prediction for n time steps into the future, we trace the most likely path of n edges starting from the vertex representing the current uptime state. If the last bit we shift in is a 1, we predict the node will be online in n time units, otherwise we predict that it will be offline.

4. Twiddle History Predictors

Suppose that a node has a fundamentally cyclical uptime pattern, but the pattern is “noisy.” For example, a machine might be online 80% of the time between midnight and noon and always offline at other times. If the punctuated downtime between midnight and noon is randomly scattered, the de Bruijn graph will accumulate infrequently visited vertices whose labels contain mostly 1's but differ in a small number of bit positions.

As the length of time that we observe the node grows, noisy downtime will generate increasingly more vertices whose labels are within a few bit-flips of each other. Probabilistically speaking, we should always predict that the node will be online from midnight to noon.

However, the many vertices representing this time interval are infrequently visited and thus infrequently updated. Their counters may have weak saturations (-1 or +1) that poorly capture the underlying cyclic availability.

For nodes like this, we can nudge predictions towards the probabilistically favored ones by considering superpositions of multiple uptime states. Given a vertex v representing the current uptime history, we make a prediction by considering v’s counter and the counters of all observed vertices whose labels differ from v's by at most d bits.

For example, suppose that k=3 and d=1, and thateach of the 2k = 8 possible vertices corresponds to an actually observed uptime history. To make a prediction for the next time step when the current vertex has the label 111, we average the counter values belonging to vertices 111, 110, 101, and 011. If the average is greater than 0, we predict "online" otherwise we predict "offline".

The TwiddledHistory strategy will perform worse than the regular History strategy when vertices within d bits of each other correspond to truly distinct uptime patterns. In these situations, superposition amalgamates state belonging to unrelated availability behavior, reducing prediction accuracy.

5. Linear Predictor

It uses a linear combination of the last k signal points to predict future points. The k coefficients are chosen to reduce the magnitude of an error signal, which is assumed to be uncorrelated with the underlying “pure” signal. To make availability predictions for t time steps into the future, we iteratively evaluate the linear combination using the k most recent availability samples, shifting out the oldest data point and shifting in the predicted data point. Linear prediction produces good estimates for signals that are stable in the short term but oscillatory in the medium to long term.

We would expect this technique to work well with nodes having diurnal uptimes, e.g., machines that are online during the work day and offline otherwise.

6. Hybrid Predictor

A machine can transition between multiple availability patterns during its lifetime.

To dynamically select the best predictor for a given uptime pattern and lookahead interval, we employ a Hybrid predictor.

For each lookahead period of interest, the Hybrid predictor maintains tournament counters. These saturating counters determine the best predictor to use for that lookahead period.

Negative counter values select the left input, whereas positive values select the right.

Consider a Hybrid predictor making forecasts for an n-sample lookahead period. At the beginning of each time unit, the Hybrid predictor samples the current uptime state of its node. Its five sub-predictors are updated with this state, and each sub-predictor makes a prediction for n time units into the future.

The final output of the Hybrid predictor is selected via tournaments as shown in Figure 2, and the individual sub-predictions are placed in a queue and timestamped with curr time + n. If the head of the queue contains an entry whose timestamp matches the current time, the entry is dequeued and the tournament counters are updated using the dequeued predictions.

A tournament counter remains unchanged if both of the relevant dequeued predictions match the current uptime state or both do not match.

Otherwise, one prediction was right and the other was wrong, and the tournament counter is incremented or decremented appropriately. In the last stage of the update, the curr time value is incremented.

Age of Mediocres

Why is twitter such a huge success? And why Wolfram Alpha will never make it big?

The answer is simple. We are now living in the age of mediocres. Even when it comes to the tech-savvy elite.

Now I am not dissing the genious that has gone into the creation of this so-called social network. But seriously whats the science behind building a tool that simply shows what a person is doing? You can't derive any intelligence from it. You can't do any social network analysis using its relationships since these  unapproved relationships mean nothing.  Sure you are able to search real-time data which is cool but tell me is there any such thing as real-time wisdom. Isn't it like searching for Shakespeare's sonnet in the text typed by infinite monkeys?

I know I am missing the social aspect of it but tell me where is the technological innovation.

And so it seems is the technological trend these days. Making ordinary things that reach out to a million consumers. All the technology then goes into the various aspects of handling those million consumers. Gone are the days when science used to stun the humankind with its novelty. Now nobody wants to go where no one has gone before.

So whats the downside? Nothing really. Except that in all that myriad of applications, little innovative pieces can get lost very easily. I personally like the idea behind Wolfram Alpha. But will they reach out to a million people the way google did? I guess not. Its hard to find a million people in the universe who are intereseted in .. lets say mathematical equations. So despite the great innovation the site will not get the attention that Twitter seems to be getting. And thats unfortunate.

It may be that the last century of brilliant scientific discoveries has set the bar so high that its impossible to cross it by the relatively young computer sciences. Or it may be that we have again reached a point where there is nothing left to surprise the world. Or most likely we are truely living in an age of mediocres.

Feature Requests for BEZVision

I am sorry if this post appears cryptic for some users. Its my wishlist for a product I am currently working on. I have already posted details of the product here.

Some of these features need more elaboration and I will dedicate separate posts to them, updating this post with the links.

A. Workload Characterization

Workload rules for app tier

This is a feature conspicuous by its absence. We cannot create workload rules for app tier. Although we do not have UPMM in the app tier, we should still be able to create rules based on page urls, component names etc. We do save this information in component stats table so it should be easy to be able to define rules based on it.

Workload Clustering

The problem with aggregating data on the basis of UPMM is that very different requests (in terms of cpu and io) can be grouped together. If the requests have widely different response times, their average response time will not be a true measure. So we basically need to group requests together that are close to each other in response time, cpu time and/or io. This can be done by k-means clustering of raw data. We will have to store a percentile field with each request and characterize on the basis of that. Its somewhat already done in model service where requests are distributed in percentiles.

Workload Recommendation

This is in part inspired from Teradata Workload Analyzer. We should be able to analyze collected data, find patterns in them by using k-means clustering and then suggest a confingurable number of workload rules to the user.

More refined definition of workload rules. (Classifier function DSL)

SQL Server has a nice way of defining workload rules by creating a classifier function. We should have a similar way of definiing a lassifier function in a custom DSL that looks something like

if (stats.logicalReads > 1000) then place in 'High IO Workload'

else if (stats.cpuTime > 3600) then place in 'High CPU Workload'

Workloads based on types of requests. (INSERT, UPDATE)

This is self explanatory. We should be able to define workload on the basis of requests types as the demands/characteristics of select, insert, update and delete statements are all different.

Calculate Cost of each workload (a function of CPU and IO)

All RDBMS (oracle, sql server, teradata) have an internal cost algorithm that is used to compute the cost of a query. We should be able to have some customizable algorithm to compute the cost of requests so tham we are able to characterize requests on the basis of that.

Workload Priority while data collection

Again self-explanatory. We do have a priority field in workload stats, but its not populated while data collection (its only set while creating plans for a prediction). We need a way to get the workload priority while collecting data from database

Importing workload rules from monitored database

Teradata has a workload manager, SQL Server has a resource governor and Oracle has a resource manager that allow the user to define workload rules. We need a way to import those workload rules into BEZVision.

Asynchronous workload data collection

Asynchronous workload is the workload not associated with any request. We need a standard way to create asynchronous workload while data collection and in model service.

Workload Type (Batch, DSS, OLTP)

I wonder why we do not define the workload type as the queuing network for batch and OLTP workloads are different from each other.

B. Prediction Change Plans

In App- tier : Implement SSL Plan

Implementing SSL increases the response time of the page. This should allow the user to select an encryption algorithm and other SSL parameters and foresee the impact of implementing SSL.

Change DBMS instance type change plan

This plan is conspicuous by its absence, as we have node type change plan and JVM configuration change plan. We should also have a DBMS instance type change plan. This will allow the user to test various configurations beforehand. Will also be able to predict the impact of a db upgrade.

Cost of Planning Scenario. Total Cost of Ownership. Relative Cost

C. Analysis & Prediction

Network data collection to use in the alignment of interconnect messages

Besides databases and jvms, the appllication should also be able to analyse and predict network configurations. We should atleast collect some network stats to use in the alignement of interconnect stats.

Collect and analyse memory data

Again memory data is never collected or analysed. We do not need it in prediction engine, but it may be useful to have some sort of anaysis available for memory as well such as that available for storage.

Data collection and Performance Analysis of .Net Framework

The microsoft .net framework is the logical extension after the support for SQL Server. Currently BEZVision only supports Java application servers in the app tier. Supporting a multitier system with .net framework and sql server would be useful. For performance data collection the framework provides a rich set of performance counters that can be read by WMI or the typeperf command.

Availability Analysis and Prediction

We can gather several metrics by analysing collection failures like MTTF, Probability, Up Time, Down time. We will need a heartbeat query with timeout for DB, heartbeat queries for node (may be running out of memory), disk (may be full try checking its space if its below threshhold.), network etc. We can then analyse to data and predict the availability of a system at various points in time. We can also analyse the workload that caused the system (dbms/node/disk) failure. Again this will not involve queuing theory but will be a done in the way described here.

Predict usage patterns (workload/activity prediction): queries. page accesses (collective intelligence)

This is not a related feature but since we are gathering so much user data, we should be able to derive some intelligence from it. e.g. predict db/app usage patterns etc.

Save an analyse query plans

Every database (Oracle, SQL Server and Teradata atleast) save the query plans associated with a query that conatns several useful information. We need a way to extract and anlyse that information.

D. Advice

App tier advice

We can have an app tier advice similar to (http://code.google.com/speed/page-speed/). We can also have an app tier catalog snapshot which will include the list of components (jsp pages, ejbs etc) in the app tier.

Database Configuration Advisor

Need a database configuration advisor that will suggest changes in db configuration based on the workload. It may be extended to a general purpose advisor that runs various predictions with different set of configurations and then advises on the basis of that (its already done as the PE generates recommendations in case of a contention point but it needs to be generalized and automated).

E. Audits

1. Custom Alert Rules.
2. Audit Exception Advice.
3. Custom audits. Define a baseline for every metric. Alert for deviations. Automatic corrective actions.

F. User Interface

1. Improve data presentation in the UI. Gantt and Kiviat charts.