Crunching Numbers Comfortably with IPython Notebook and Pandas

A big part of building distributed computer systems is delivering proof they actually work. Besides a live demo with shiny front-end and a polished slide deck, raw numbers are ultimately necessary to show that promises of robust availability, high throughput and low latency are kept during real-world use. And sometimes you may need numbers for debugging as well.

My clear personal favorite for data analysis and visualization (and light programming) is Python, and in extension IPython Notebook, matplotlib, seaborn and the time-series analysis framework Pandas. Their integration has become seamless over the past years and they are very well suited for pretty much any task from quickly visualizing application logs to in-depth looks at time series and performing statistical inference. As examples for successful use of these tools I can offer our recent work on validated simulation of IaaS clouds and SLAs for spot instances. When looking closely at these publications the astute reader will find the giveaways of graphs generated with these tools.

If you haven’t used IPython notebook yet, I highly recommend you invest 1-2 hours in getting familiar with the basics. Personally, it took some time to overcome my internal inertia and finally spend the time necessary – and I haven’t looked back since. It makes life quite a bit easier. I also had the opportunity earlier this year to talk to Brian Granger – one of the master minds behind IPython – and heard about the plans for expanding IPython’s scope with project Jupyter. I’m excited to see what’s coming down the pipeline in terms of high-performance analytics for those lengthy production log files we have sitting around.

Pandas had a steep learning curve for me as well, but it took some time to get my head around some of the intermediate indexing and slicing techniques. As I figured this out, however, productivity shot through the roof. Importing text, csv, json and xml? No problem. Join three different data sets on different columns and get aggregate statistics a la SQL? Check. Plot intermediate results to debug heavy scipy use? Quick and easy. Things that took hours before get done in minutes now. It was well worth spending an afternoon to get familiar with it.

Despite all this greatness, there’s a caveat. For presentation slides I still find myself falling back on Microsoft Excel for most visualization. Yes, I know my coolness factor just took a hit. The WYSIWYG (“what-you-see-is-what-you-get”) formatting capabilities are still more time-efficient than figuring out the various corner-cases of matplotlib calls. That being said, I usually prepare the data plotted with Excel using the aforementioned Python tools.

Probably the easiest way to get it all set up is a pre-configured Python distribution such as Continuum Anaconda. An installation from scratch with pip and co is possible as well, but depending on your platform you will end up dealing with version conflicts manually. In case you run into any roadblocks there’s a solid user base for all these tools. This means that stackexchange.com is an invaluable resource for troubleshooting in addition to the official IPython and Pandas docs.

Creating Validated Simulation Models of IaaS Clouds

In a recent effort to empirically evaluate a newly-proposed power-aware scheduler for private IaaS clouds we ran into problems obtaining accurate simulation results for two cloud testbeds we were working with. This prompted us to investigate an approach for creating validated, yet light-weight simulation models using an approach inspired by Perturbation theory. The approach augments a simple cloud model with measurements taken from a small subset of an actual production system to produce highly accurate predictions at scale.

The “power manager” we investigate is designed to learn from and validate against production traces with multi-month time frames. The sheer duration of these traces makes it necessary to use faster-than-realtime simulation. Furthermore, we want to make predictions about the performance of the scheduler at larger scale than we can observe. Since we have to luxury of having access to two production-quality testbeds, we are also required to deliver a fully functional scheduler that handles workloads replayed on the real-world clouds flawlessly. This article is meant to be an accessible high-level How-To of our work on “Using Trustworthy Simulation to Engineer Cloud Schedulers” published at IC2E 2015.

Our primary goal is to build light-weight simulation models for our two specific private IaaS clouds to evaluate the power manager. These “clouds” have with 5 and 8 nodes, respectively, and are very small compared to commercial cloud installations. Due to their small size we have the opportunity to get away with a simple model while still achieving high accuracy. Having a production system at hand, we chose an approach inspired by Perturbation theory and start with a parsimonious model (“solvable”, e.g. simple model derived from the architecture overview diagram) of our cloud. We then iteratively perturb (“refine”) the model until the desired level of accuracy is achieved.

We lay out a minimal model with the end goal of evaluating the power manager in mind. The power manager draws on information on node occupancy and modifies the power states of individual nodes in our cloud. Hence, our simulation should accurately reproduce node utilization, occupancy, and power states and should allow for a plug-able scheduling algorithm and faster-than-realtime execution. Anything more than this is optional. Hence, we start with a simple model in which instance requests (and timer events) arrive at a scheduler which places instances on individual nodes. These instances then execute on the node for a fixed duration until they complete. Furthermore, the scheduler observes the system state in fixed intervals (epochs) and may hibernate and wake-up nodes as needed.

From experience working with clouds we also decide to run the simulation in a Monte-Carlo-style fashion which allows for non-determinism when it comes to state-transition times and failures in the system. This is necessary since multiple runs of the same workload – on the same cloud – are still subject to concurrency issues and tend to create similar but not necessarily equal results.

Our Perturbation approach then demands that we validate our model’s predictions at small scale – a single node in our case – against measurements of the real system. As expected, the prediction of the initial model diverge by a 15 percent margin and we enter our first round of iterative “perturbing” of the model. Looking at the logs of the production system versus the simulation, three types of unaccounted overheads stand out: instance setup, instance teardown and node power state changes. We extract these three overheads as variables for our model by storing their empirical distributions and later sampling them during the simulation. “Perturbing” our model by introducing these three variables indeed produces simulation results within 1 percent of the real 1-node cluster. But do these predictions hold when scaling up the simulation without collecting additional samples?

For the avid reader, the unsurprising answer to this question is “yes”. Even predictions made for our cloud with all 8-nodes validate against out-of-sample (i.e. later) measurements taken from the real-world system, with an error in the 1-2 percent range. Interestingly, this “cloud-specific” model for our 8-node cluster is trivially portable to the 5-node cluster and produces equally accurate results by swapping out the three empirical variables with measurements from a single node of the other cluster.

While these results are encouraging, a number of qualifications in order. Most importantly, scaling up a simple model cannot be expected to remain accurate for large systems. Shared resources such as network bandwidth or storage contention will put a cap on linear scaling sooner rather than later. Another aspect worth addressing is the assumption that nodes are homogeneous. Without this assumption, different nodes require different empirical distributions which increases model complexity and the required sample count. This may go to the extreme where each node is represented by it’s own samples, which defies the purpose of the approach. On the other hand, from a methodological perspective there are no restriction on “perturbing” the model further to account for these issues.

A practical takeaway of our Perturbation experiment is that it is indeed possible to build a simulation model that scales with the size of an implementation effort. The parsimonious simulation model is manually designed by the developer, which allows qualitative considerations, and augmented with empirical measurements, which adds quantitative information. The subsequent real-world testing of a component developed with the help of this perturbation model then generates new insights and empirical measurements at scale, which in turn can be fed back into the model by iterative “perturbing”.

Cloud Simulators for Research and Development

My personal interest lies in the area of scheduling and resource allocation in IaaS clouds. Evaluating the effectiveness of a new scheduling algorithm is often only visible in over a long period of time, with heavy load on the system. When working with production traces spanning multiple months, empirical evaluation in real-time becomes infeasible. The academic community has picked up on this issue and produced a large variety of simulators that allow evaluation of schedulers in faster-than-realtime. For a taxonomy of evaluation methods for large scale systems, I highly recommend you to have a look at a Gustedt et al. survey from 2009.

Looking specifically at the simulation approach, system evaluation is typically performed from a specific perspective – from the application or the infrastructure provider – and deliver accordingly tailored results. A subset of these simulators is presented below. Another, complimentary summary of existing work by Oujani can be found online as well.

Infrastructure simulators:

CloudSim. One of the primary frameworks used for simulating clouds in academic research. It is the brain-child of the developers of GridSim and has been used in a number of studies as it is highly customizable. Extensions to CloudSim include CloudAnalyst and NetworkCloudSim, which add a GUI and facilities for simulating geo-distributed applications, among others.

GreenCloud. Built on NS2 it’s primary focus lies on exploring the impact of network layouts on cloud performance and energy consumption.

iCanCloud. Focuses on predicting application performance, energy-consumption and cost with different hardware platforms and resource allocation schemes.

MDCSim. A commercial entrant in the area, relying on detailed models of individual hardware components to produce predictions about a clouds performance at scale. The original publication targets 3-tier web applications instead of generic IaaS cloud infrastructures.

DCSim. Simulates IaaS clouds with a specific focus on dynamic power- and SLA-optimization via VM migration. Its authors use tiered scale-out workloads and evaluate the advantage of VM migration and replication strategies over static provisioning.

GDCSim. Primarily concerned with the thermal aspects of power-management in data centers by integrating existing modeling tools. Specifically investigates the interaction of workloads intensity and resource management policies with heat dissipation and fluid dynamics of different physical data center layouts.

Application-perspective simulators:

PICS. A recent entrant in the cloud simulation field, with a focus on accurate reproduction of job execution times and cost on public clouds from traces.

EMUSim. Uses emulation of Bag-of-Task applications to extract performance properties and simulate their behavior at larger scale more accurately. An evaluation step ensures that emulation and simulation agree at observable scales.

These simulator are typically based on discrete-event simulation, using compound models from smaller sub-models. This approach makes them highly customizable, but creates a significant problem calibrating and validating them against real-world measurements. Notably, while application-perspective simulator are published with results to validate their accuracy against measurements taken from real-world execution, this is step appears to be missing for most infrastructure simulators. They are thus mostly applicable to exploratory research and design studies rather than exact performance prediction.

Visualizing Cloud Traces

We commonly need to debug an implementation of a new cloud component. While a torrent of error messages in the system logs communicates the fact that the experiment did not work the way it was intended to, it is harder to determine exactly why this is the case. This becomes especially bad when the cause isn’t a simple programming flaw, but an issue with the scheduling logic or algorithm itself.

A recent example for this was a scheduler which uses machine learning techniques to enforce SLAs, i.e. guarantees to the user of the instances, on the termination rates of evictable (“pre-emptible”) spot instances in IaaS clouds. In our model “spot instance” requests arrive at a cloud from “outside” and execute on spare capacity on the nodes. If the cloud receives high-priority “internal” instance requests which cause resource demand to spike, the spot instances are terminated to make room for more important ones. While I feel the itch to go on about the the scheduler in detail, I’ll rather focus on the problem at hand:

The scheduler performs smoothly for two thirds of a trace we chose for testing and then fails spectacularly, just to return to back to normal towards the end of the experiment. Of course, the primary goal now is to understand what is going on (and going wrong) in the cloud. In addition to the classic “CDF-everything” approach, I find that I frequently come back to two visualization methods for the dynamic behavior of cloud workloads – the utilization graph and the lifetime graph. Representing the inner workings of a cloud in a visual format helps me immensely to interpret the information quickly.

The following graphs are generated from the Eucalyptus private IaaS traces published recently, more specifically the “constant” workload data sets DS5 and DS6. These cloud workload traces are insofar interesting as they are recorded from commercial production systems and cover continuous multi-month time frames. Using the aforementioned types of graphs we will be able to dig up interesting details about the traces and solve the mystery of the failing SLA enforcement.

(A) The utilization graph

trace_utilization

The utilization graph I’m using here is a time-series graph with time on the x-axis and three series plotted on the normalized y-axis: utilization of CPU cores by IaaS instances in red, occupancy of physical hosts by instances in green, and the (here irrelevant) power state of the physical hosts in blue.

Most helpful to our investigation is the red core utilization series. The trace indeed appears constant for most of the trace with a load spike plus a dip in the beginning, a short burst in the middle and another dip followed by a spike towards the end. The beginning of the trace is used as warmup-period for the machine learning scheduler and we’ll ignore it for now. The spike in the middle doesn’t throw off the scheduler, but the dip-spike formation towards the end matches with the timestamps of the observed SLA violation.

In more detail, both node utilization and node occupancy show an extreme outlier within the valley to the right. While our look at the utilization graph confirms that “something” is going on here, it is not immediately clear why this causes the scheduler to trip. After all, the load drops for a moment just to return back to normal levels.

(B) The lifetime graph

trace_lifetimes

The lifetime graph integrates a large amount of information, so please bear with me for a moment. Time is plotted again on the x-axis while the y-axis represents the instance index (details below). Each horizontal row (“life-line”) represents the lifetime of a single IaaS instance, broken down into three phases: setup, execution, and tear down. Finally, the color-coding is connected to the requesting user, but not relevant for our investigation at hand.

Intuitively, the vertical ordering of rows can be interpreted as their launch order, i.e. instances closer to the bottom of the graph launched before instances towards the top. The horizontal indent of each row visualizes the absolute delay of the instance launch from the very beginning of the trace.

Even with the bare eye you can typically identify clusters of instances with similar start- and stop-times, as they tend to group together in the graph. Examples for this are the long green cluster and the long red cluster in the bottom half of the graph. If a number of these request clusters start or terminate at the same time stamp, such as right after time stamp 4.000, this is a strong indication for a change-point in system (or user) behavior. We can already observe that a number of long-running instances stop at the same time around the 7.000 mark, just when our SLA enforcement fails.

The final hint comes from the line created by newly added instance life-lines. For two thirds of the trace the “slope” generated by new lines is relatively even, indicating a constant arrival rate of requests at the could. This is what we would expect to see from the “constant” workload DS5 and DS6. At the 7.000 mark, however, the slope increases to almost vertical. This is the fingerprint of a massive burst of requests arriving at the cloud at once. Most of the launched instances only execute for a very short period of time and requests end up being issues quickly. This incident has the appearance of a runaway script that is supposed to replace some of the terminated long-running instances, but enters a rapid-fire request loop when failing to launch instances repeatedly.

The initial drop in load causes the scheduler to launch additional spot instances in the systems. While the runaway script ramps up its request rates, more and more spot instances are launched, just to be immediately terminated by a flood of high-priority requests. Armed with this knowledge we are able to solve the mystery of our failing scheduler. We modify it to detect drastic surges and stay at the side-lines until the situation normalizes. More importantly however, I hope that the visualization methods presented above will help you with your quest for building the perfect cloud or, at least, catastrophe-free schedulers. Happy hacking.

Cloud Traces and Production Workloads for Your Research

(Only interested in the raw trace data? Skip to the end.)
(EDIT 2015-09-15: added Yahoo cluster traces. Hat tip Dachuan Huang)

Whenever there’s a new idea for a cloud scheduler, my first step is a quick draft of the algorithm in an IaaS cloud simulation framework – punching out every idea on a production system simply isn’t feasible. The simulator then needs to be fed with platform configuration about system hardware and some type of utilization trace. The easiest type of workload trace to look at is generated from synthetic distributions, but this has some limitations. The traces we work with at minimum contain (a) job start times, (b) a type of job size such as duration or amount of data to process, and (c) a job type such as the instance type other form of constraint. When I speak of workload traces in this article, I am specifically referring to traces of batch jobs with fixed units of work. As an example, for one of our recent papers about SLA-enforcement for IaaS spot instances this means in detail:

  • request timestamp
  • instance life-time
  • instance core count
  • any additional data …

Generating realistic cloud workloads synthetically has spawned an entire branch of research. My focus in this article is rather a practical description of the steps I personally take for developing and evaluating a new cloud scheduler.

I usually start with a synthetic trace with job inter-arrival times and durations generated from an exponential distribution, with uniform core size – in our example a core count of 1 – for all requests. If the new scheduler doesn’t provide satisfactory results with this, it’s back to the drawing board. The next stage uses a log-normal distribution for arrival and duration, as this better models the long-tail properties of jobs encountered in real-world traces. A last extension for the synthetic traces then is the introduction of a non-homogeneous mix of instance sizes – which has been the demise of quite a few ideas. While the synthetic approach is a useful basic for testing, it does not re-create the kind of challenges that production traces pose, such as change-points in user-behavior, time-varying auto-correlation, and seasonality in the workload.

When a scheduler prototype enters serious consideration, I am a strong proponent of using traces recorded from production systems for evaluation. Unfortunately, this is where evaluation becomes difficult. Besides handling the technological complexity of the scheduler, a logistical problem comes up: the scarcity of publicly available production traces. This can be a big challenge for the aspiring cloud researcher. I’ve listed a number of notable exception below, but generally companies in the cloud space either do not record utilization traces over the long-term or they heavily guard these traces and rarely allow the interested researcher a glimpse. If researchers do get access, they often cannot name the source of the traces and cannot re-distribute the raw data used as foundation for their work. This in turn creates problems with the reproducibility of results and slows down the overall innovation process. The desire to protect a company’s competitive edge is understandable, and yet the availability of anonymized traces would spark innovation and drastically support academic research.

Fortunately, there are exceptions to this rule of scarcity. Here is a selection of public traces that we have found valuable in testing the real-world suitability of cloud schedulers:

Google cluster workload. Published by Google in an effort to support large-scale scheduling research, these traces from a Google data center cell have attracted analysis efforts from a number of researchers in the meantime, e.g. an analysis by Sharma et al.. The trace covers a 1-month time frame and 12.000 machines an includes anonymized job constraint tags.

Facebook Hadoop workload. A number of 1-hour segments from Facebook’s Hadoop traces published as part of UC Berkeley AMP Lab’s SWIM project. Some segments contain arrival times and duration, whereas others provide the amounts of data processed.

OpenCloud Hadoop workload. Taken from a Hadoop cluster managed by CMU’s Parallel Data Lab, these traces provide very detailed insights in the workload of a cluster used for scientific workloads for a 20-month period. Includes timestamps, slot counts, and more.

Eucalyptus IaaS cloud workload. Anonymized traces scraped from the log files of multiple production systems running Eucalyptus private IaaS clouds. Published as part of a study by Wolski and Brevik. The traces contain start- an stop times for instances, their size and the node allocation as decided by the native scheduler.

Yahoo cluster traces. A number of data sets from Yahoo’s production systems. Most notably contains system utilization metrics from PNUTS/Sherpa and HDFS access logs for a larger Hadoop cluster. Additionally provides data sets with file access statistics and time-series for testing anomaly detection algorithms.

There are also a number of papers that investigate Hadoop workloads of specific production clusters. While these studies do not provide raw trace data with start times, etc. they contain summary statistics about the workload, typically after applying clustering methods. Some of them are:

OpenCloud Hadoop workload. An analysis of CMU’s OpenCloud Hadoop workload as well as two other traces of data-mining and web-scraping workloads taken from production systems of a large internet company.

Cloudera Hadoop workload. Similar to the above with data from production systems of anonymous Cloudera customers and Facebook and analyzed by researchers from UC Berkeley.

Notably, most of these traces stem from Hadoop clusters and are limited to data-mining applications. More generic IaaS-type workloads can be found in the Eucalyptus traces and, potentially, the Google trace. I want to emphasize that these are very different types of batch workloads that can offer interesting insights in the behavior of a cloud system under varying conditions. I hope this short reference provides a jump-off point for both researchers and engineers to get their hands on a broader variety of production traces.