[DEPRECATED] Dynamic Virtual Machine Placement Framework

This project contains the source code and results of the following research:

"Towards Elastic Virtual Machine Placement in Overbooked OpenStack Clouds under Uncertainty"

Jornadas de Cloud Computing & Big Data, 2018
Authors: F. López-Pires and B. Barán and C. Pereira and M. Velázquez and O. González
Ref.: JCC&BD-2018

Abstract

Cloud computing datacenters currently provide millions of virtual machines in highly dynamic Infrastructure as a Service (IaaS) markets. As a first step on implementing algorithms previously proposed by the authors for the Virtual Machine Placement (VMP) in a real-world IaaS middleware, this work presents an experimental comparison of these algorithms against current algorithms considered for solving VMP problems in OpenStack. Several experiments considering scenario-based simulations for uncertainty modelling demonstrate that the proposed algorithms present promising results for its implementation towards real-world operations. Next steps are also summarized.

"Machine Learning Opportunities in Cloud Computing Datacenter Management for 5G Services"

ITU Kaleidoscope, 2018
Authors: F. López-Pires and B. Barán
Ref.: ITU-2018

Abstract

Emerging paradigms associated to Cloud Computing operations are considered to serve as a basis to integrate 5G components and protocols. In the context of resource management for Cloud Computing Datacenters, several research challenges could addressed considering state-of-the-art machine learning techniques. This paper presents identified opportunities on improving critical resource management decisions, analyzing the potential on applying machine learning to solve these relevant problems, mainly in two-phase optimization schemes for Virtual Machine Placement (VMP). Future research directions are also presented.

To develop and execute the following items are required:

To develop:

1. Maven 3 or greater
2. Java 8 (JDK 1.8)
3. Java IDE (i.e. eclipse, intellij, ...)

To run:

The framework could be compile with maven.

1. Go to the project root and execute:

$ mvn clean package

2. In the target directory is the compiled framework, to execute use the following command:

$ java -jar target/DynamicVMPFramework.jar inputs/ivmp.conf inputs/vmpr.conf inputs/scenarios.con
f outputs/

Input File

• parameter: Configuration file and scenarios

Parameter File Structure

ivmp.conf:

1. APPROACH = Algorithm approach

• CENTRALIZED
• DISTRIBUTED -> This approach will automatically launch the distributed approach and you don't need to specify the following inputs: VMPr, VMPr_TRIGGERING, VMPr_RECOVERING.

2. iVMP = Algorithm for the incremental phase (iVMP).

• FF -> First Fit
• BF -> Best Fit
• WF -> Worst Fit
• FFD -> First Fit Decreasing
• BFD -> Best Fit Decreasing

vmpr.conf: 3. VMPr = Algorithm for the reconfiguration phase (VMPr).

• MEMETIC -> Memetic Algorithm
• ACO -> Ant Colony Optimization

4. VMPr_TRIGGERING = VMPr triggering strategy

• PERIODICALLY
• PREDICTION-BASED

5. VMPr_RECOVERING = VMPr recovering strategy

• CANCELLATION
• UPDATE-BASED

6. PM_CONFIG = Load CPU Configuration

• LOW -> (<10%)
• MED -> (<30%)
• HIGH -> (<80%)
• FULL -> (<95%)
• SATURATED -> (<120%)

7. DERIVE_COST = Cost per each derived VM
8. PROTECTION_FACTOR_01 = Resource1 protection factor [0;1]
9. PROTECTION_FACTOR_02 = Resource2 protection factor [0;1]
10. PROTECTION_FACTOR_03 = Resource3 protection factor [0;1]
11. PENALTY_FACTOR_01 = Resource1 penalty factor (greater than 1)
12. PENALTY_FACTOR_02 = Resource1 penalty factor (greater than 1)
13. PENALTY_FACTOR_03 = Resource1 penalty factor (greater than 1)
14. INTERVAL_EXECUTION_MEMETIC = Periodic Time of MA Execution
15. POPULATION_SIZE = Population size for MA
16. NUMBER_GENERATIONS = Generations size for MA
17. EXECUTION_DURATION = Time of Duration
18. LINK_CAPACITY = Link Capacity for Migration
19. MIGRATION_FACTOR_LOAD = Factor Load per Migration
20. HISTORICAL_DATA_SIZE = Historical Data Sieze
21. FORECAST_SIZE = Forecast Size
22. SCALARIZATION_METHOD = Scalarization Method

• ED -> Euclidean Distance
• MD -> Manhattan Distance
• CD -> Chevyshev Distance
• WS -> Weighted Sum

23. MAX_PHEROMONE = Max pheromone allowed in ACO
24. PHEROMONE_CONSTANT = Pheromone constant for ACO, range [0,1], determines how fast pheromone evaporates. Pheromones evaporates quicker as pheromone constant grows
25. N_ANTS = Number of ants used for ACO
26. ACO_ITERATIONS = Number of iterations to be performed in ACO to return a solution

scenarios.conf: 27. SCENARIOS = List of Request

Output Files

The framework generates the following file:

• *resutls: contains F1, F2, F3 values