Dynamic Placement of Virtual Machines for Managing SLA Violations NORMAN BOBROFF, ANDRZEJ KOCHUT, KIRK BEATY SOME SLIDE CONTENT ADAPTED FROM ALEXANDER NUS PRESENTED BY JON LOGAN

Motivation  Virtual machines are becoming more and more popular throughout our datacenters  Servers use electricity  Electricity can be expensive!  How do we minimize the number of utilized machines, while meeting our SLA obligations?  Usage patterns of machines are NOT static, and generally change dynamically

Goals  Maximize utilization of active machines  Minimize Service Level Agreement (SLA) violations  Minimize number of active machines  Power off unused machines to conserve cost (electricity)  Essentially, minimize cost while meeting SLA guarantees

Static Allocation  All machines are taken offline, and historical usage is used to determine ideal placement  Happens very infrequently (~weeks or months)  Must interrupt service to relocate  Utilization is not consistent in many cases! Demand may vary significantly within the period between allocations

Dynamic Allocation  VMs are seamlessly migrated between machines based on predicted demand  Is done rather frequently (~minutes, hours)  Live migration  Minimal (~ms) service disruptions during migration  Allows for allocations to more closely follow demand

Live Migration  Moves a VM image between machines without service interruption  The paper cites a ~45 second transition time  VM must be serialized and transferred over the network  Artificially limits our reallocation period  Can’t reallocate faster than we can migrate!

Service Level Agreement  Essentially is a contract between the provider and the customer that states that resources R will be available X% of the time  Violations cost money!  X is usually high (ex. 95%)  VMs do not necessarily use this entire resource allocation at all times, but it must be available should they choose to use it  Ex. VM may be doing batch processing, and only do substantial work between 12:00AM and 1:00AM

Static vs Dynamic Usages  Workloads are not static!  Try to predict the usage of the VM in a time T  Reallocate machines to be able to meet that predicted usage  Need to be within a certain percentile to meet SLA requirements  Capacity savings is simply  Static Allocation - (Predicted Usage + Error Factor)  Repeat this process every time T

What Workloads Are Best For Dynamic Allocation?  Not all Workloads are created equal  Some tend to be better than others  Constant workloads = bad!  A workload is an ideal candidate for dynamic allocation if  It has strong variability AND  It has strong autocorrelation combined with periodic behavior  Essentially, you need to have a decent degree of variability, and be able to reasonably predict its usage

Workload 3a  Strongly variable – good  Autocorrelation ~0.8 – good  Weak periodic behavior – bad  Verdict – Good  Large variability offers significant potential for optimization  Strong autocorrelation makes it possible to obtain a low-error predication

Workload 3b  Weakly variable - bad  Decaying autocorrelation - bad  Weak periodic behavior – bad  Verdict – Bad  Low variability makes potential gain low  Weak autocorrelation and no periodic component make it difficult to predict demand

Workload 3c  Strongly variable – good  Strong Autocorrelation– good  Strong periodic behavior – good  Verdict – Very Good  An ideal case for dynamic allocation

Potential Gain

Demand forecast algorithm  Determine the periods in demand using ‘common sense’ aided by periodogram (e.g.time-of-day,day of week,…)  Decompose the process into deterministic periodic and residual components D i + r i  Estimate the deterministic part using averaging of multiple smoothed historical periods  Fit Auto Regressive Moving Average (ARMA) model to the residual process  Use the combined components for demand prediction U i = D i + r i

Management Algorithm  Goal is to minimize time averaged number of active servers without violating the SLA agreement  Machines that are not utilized to handle VMs are powered off or put in a low power state  Will be reactivated if/when required (minimally, the next period)  The time to power on & migrate must be less than the period T  Responsible for actual migrations of machines  Placing of VMs is essentially a version of the bin packing problem  NP hard!  We use an approximation, using first-fit

Management Algorithm  Measure – Measure usage  Forecast – Predict usage for the next window  Remap – Relocate machines if necessary  Preform this (MFR) at regular intervals  Designed to try to predict the “best we can do”

Management Algorithm Overview

Key Terms  N – virtual machines  M – physical machines  C m – Maximum capacity of physical machine  f n i, k – forcast value for resource demand of VM n at interval i+k  R – migration interval  C p (u, o 2 ) – (1-p)-percentile of Gaussian distribution with mean u and variance o 2

Management Algorithm

Management Algorithm (2)

Management Algorithm (3)

Management Algorithm (4)

Simulations  Simulated using traces gathered from hundreds of production servers using various applications  Traces contain CPU, memory, storage, and network  We are only focusing on CPU usage  Samples were collected every 15 minutes  The simulated study  Verifies that the MFR meets SLA targets  Quantifies the reduction of SLA violations  Quantifies the number of saved machines  Explores the relationship between the remapping interval and the gain from dynamic management  Performs measurements to determine properties of a practical infrastructure with respect to migration of VMs

Overflows vs Number of PMs

Number of Machines vs Overflow Desired Significantly reduces number of machines active

Performance degrades as the migration interval increases Essentially, the prediction is the max usage predicted within the range

Limitations  The paper only looks at one resource utilization  In this case, CPU utilization  In the real world, you have numerous resources to handle allocations for  Memory, CPU, IO, Network, etc.  Assumes bandwidth between machines is free & unrestricted  Relocating some VMs in some cases may not be worth the cost of relocating the image  Their study size is small  Only 6 physical machines  What if different VMs have different SLA requirements?  What if your PMs had differing hardware?

Conclusion  Based on the simulated data, it significantly reduces cost to execute virtual machines  Relies on an ideal case of VMs  Predictable and volatile usage  Algorithm could be optimized to reduce the number of VM relocations, or to more optimally schedule  Simulation is too small  The paper claims a 44% average savings in the number of active PMs