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Experiments on cost/power and failure aware scheduling for clouds and grids. Jorge G. Barbosa , Altino M. Sampaio , Hamid Harabnejad Universidade do Porto, Faculdade de Engenharia, LIACC Porto, Portugal, jbarbosa@fe.up.pt . Outline.

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Experiments on cost/power and failure aware scheduling for clouds and grids

Jorge G. Barbosa, Altino M. Sampaio, HamidHarabnejad

Universidade do Porto, Faculdade de Engenharia, LIACC

Porto, Portugal, jbarbosa@fe.up.pt

outline
Outline
  • Dynamic Power- and Failure-aware Cloud Resources Allocation for Sets of Independent Tasks
  • A Budget Constrained Scheduling Algorithm for Workflow Applications on Heterogeneous Clusters

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

outline1
Outline
  • Dynamic Power- and Failure-aware Cloud Resources Allocation for Sets of Independent Tasks
  • A Budget Constrained Scheduling Algorithm for Workflow Applications on Heterogeneous Clusters

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

dynamic power and failure aware cloud resources allocation for sets of independent tasks
Dynamic Power- and Failure-aware Cloud Resources Allocation for Sets of Independent Tasks
  • Cloud computing paradigm
  • Dynamic provisioning of computing services.
  • Employs Virtual Machine(VM) technologies for consolidation and environment isolation purposes.
  • Node failure can occur due to hardware or software problems.
  • Image source: http://www.commputation.kit.edu/92.php

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

characteristics
Characteristics
  • Dependability of the infrastructure
    • Distributed systems continue to grow in scale and in complexity
    • Failures become norms, which can lead to violation of the negotiated SLAs
    • Mean Time Between Failures (MTBF) would be 1.25h on a petaflop system(1)
  • Energy consumption
    • The main part of energy consumption is determined by the CPU
    • Energy consumption dominates the operational costs

Task n

Task 1

Task 2

Task 3

VM

1

VM 2

VM 4

VM n

...

VMM

VMM

VMM

VMM

PM 1

PM 2

PM 3

PM m

PM – Physical Machine

  • (1) S. Fu, "Failure-aware resource management for high-availability computing clusters with distributed virtual machines," Journal of Parallel and Distributed Computing, vol. 70, April 2010, pp. 384-393, doi: 10.1016/j.jpdc.2010.01.002.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

related work
Related Work

(1) Optimistic Best-Fit (OBFIT) algorithm

- Selects the PM with minimum weighted available capacity and reliability.

(2) Pessimistic Best-Fit (PBFIT) algorithm

- Selects also unreliable PMs in order to increase the job completion rate.

- Selects the unreliable PM p with capacity Cp such that Cavg + Cp results in the minimum required capacity

  • Dynamic allocation of VMs, considering PMs’ reliability
    • Based in a failure predictor tool with 76.5% of accuracy
  • Proposed architecture for reconfigurable distributed VM (1)
  • Cavg average capacity from reliable PMs.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

approach
Approach
  • The goal
    • It is a best-effort approach, not a SLA based approach;
    • Virtual-to-physical resources mapping decisions must consider both the power-efficiency and reliability levels of compute nodes;
    • Dynamic update of virtual-to-physical configurations (CPU usage and migration).
  • Construct power- and failure-aware computing environments, in order to maximize the rate of completed jobs by their deadline

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

approach1
Approach
  • Multi-objective scheduling algorithms are addressed in three ways:
  • 1- Finding the pareto optimal solutions, and let the user select the best solution.
  • 2- Combination of the two functions in a single objective function.
  • 3- Bicriteria scheduling which the user specifies a limitation for one criterion (power or budget constraints), and the algorithm tries to optimize the other criterion under this constraint.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

approach2
Approach
  • Leverage virtualization tools
    • Xen credit scheduler
      • Dynamically update cap parameter
      • But enforcing work-conserving
    • Stop & copy migration
      • Faster VM migrations, preferable for proactive failure management

Power

consumption

CPU%

100

CPU

Increasing

0

PM3

VM

time

PM2

VM

VM

PM1

VM

VM

VM

–Failure

– Stop & copymigration

–Failurepredictionaccuracy

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

system overview
System Overview
  • Cloud architecture
    • Private cloud
    • Homogenous PMs
    • Cluster coordinator manages user’ jobs
    • VMs are created and destroyed dynamically
  • Users’ jobs
    • A job is a set of independent tasks
    • A task runs in a single VM, which CPU-intensive workload is known
    • Number of tasks per joband tasks deadlines are defined by user
  • Private cloud management architecture

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

power model
Power Model
  • Linear power model

P = p1 + p2.CPU%

  • Power Efficiency of P
  • Completion rate of users’ jobs
  • Working Efficiency
  • Example of power efficiency curve (p1 = 175w, p2 = 75w)

Measures the quantity of useful work done (i.e. completed users’ jobs) by the consumed power.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

proposed algorithms
Proposed algorithms
  • Minimum Time Task Execution (MTTE) algorithm
  • Slack time to accomplish task t
  • PM icapacity constraints
  • Selects a PM if:
    • It guarantees maximum processing power required by the VM (task);
    • It has higher reliability;
    • And if It increases CPU Power Efficiency.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

proposed algorithms1
Proposed algorithms
  • Relaxed Time Task Execution (RTTE) algorithm

100%

VM

Host CPU

0%

CAP

  • Cap set in Xen credit scheduler
  • Unlike MTTE, the RTTE algorithm always reserves to VM the minimum amount of resources necessary to accomplish the task within its deadline

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

performance analysis
Performance Analysis
  • Simulation setup
    • 50 PMs, each modeled with one CPU core with the performance equivalent to 800 MFLOPS;
    • VMs stop & copy migration overhead takes 12 secs;
    • 30 synthetic jobs, each being constituted of 5 CPU-intensive workload tasks;
    • Failed PMs stay unavailable during 60 secs;
    • Predicted occurrence time of failure precedes the actual occurrence time;
    • Failures instants, jobs arriving time, and tasks workload sizes follow an uniform distribution;

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

performance analysis1
Performance Analysis
  • Implementation considerations
    • Stabilization to avoid multiple migrations
    • Concurrence among cluster coordinators
  • Algorithms compared to ours
    • Common Best-Fit (CBFIT)
      • Selects the PM with the maximum power-efficiency and do not consider resources reliability
    • Optimistic Best-Fit (OBFIT)
    • Pessimistic Best-Fit (PBFIT)

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

performance analysis2
Performance Analysis
  • Migrations occurring due to proactive failure management only:
    • Failure predictor tool has 76.5% of accuracy; RTTE algorithm presents the best results;
    • Working efficiency, as well as the jobs completion rate, decreases with failure prediction inaccuracy.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

performance analysis3
Performance Analysis
  • Migrations occurring due to proactive failure management and power efficiency:
    • Sliding window of 36 seconds, with threshold of 65% (a migration starts if CPU usage below 65%);
    • RTTE returns the best results for 76.5% failure prediction accuracy;
    • Comparing to earlier results, the rate of completed jobs diminishes, since the number of VMs migrations increases.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

performance analysis4
Performance Analysis
  • Number of migrations occurring due to failure management and power efficiency
    • RTTE and MTTE have stable number of migrations and respawns along failure accuracy variation
  • Migrations occurring due to proactive failure management only (75% accuracy)
    • RTTE and MTTE return the best working efficiency as the number of failures in the cloud infrastructure rises

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

conclusions 1
Conclusions (1)
  • Conclusion remarks:
    • Power- and failure-aware dynamic allocations improve the jobs completion rate;
    • Dynamically adjusting cap parameter of Xen credit scheduler prove to be capable of obtaining better jobs completion rate (RTTE);
    • Excessive number of VM migrations to optimizing power efficiency reduces job completion rate.
  • Future directions:
    • Dynamic allocation considering workload characteristics;
    • Data locality;
    • Scalability;
    • Compare/integrate DVFS feature;
    • Improve PM consolidation (why 65% threshold?);
    • Heterogeneous CPUs.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

outline2
Outline
  • Dynamic Power- and Failure-aware Cloud Resources Allocation for Sets of Independent Tasks
  • A Budget Constrained Scheduling Algorithm for Workflow Applications on Heterogeneous Clusters

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

a budget constrained scheduling algorithm for workflow applications on heterogeneous clusters
A Budget Constrained Scheduling Algorithm for Workflow Applications on Heterogeneous Clusters
  • A Job is represented by a workflow
  • A workflow is a Directed Acyclic Graph (DAG)

a node is an individual task

CPU1

CPU2

CPU3

an edge represents the inter-job dependency

  • Workflow scheduling
  • Mapping Tasks to Resources
  • Main goal is to have a lower finish time of the exit task

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

introduction
Introduction

Target platform:

- Utility Grids that are maintained and managed by a service provider.

- Based on user requirements, the provider finds a scheduling that meets user constrains.

In utility Grids, other QoS attributes than execution time, like economical cost or deadline, may be considered. It is a multi-objectiveproblem.

Multi-objective scheduling algorithms are addressed in three ways:

1- Finding the pareto optimal solutions, and let the user select the best solution;

2- Combination of the two functions in a single objective function;

3- Bicriteria scheduling which the user specifies a limitation for one criterion (power or budget constraints), and the algorithm tries to optimize the other

criterion under this constraint.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

proposed algorithm
Proposed Algorithm

Heterogeneous Budget Constraint Scheduling Algorithm (HBCS)

  • HBCS has two phases:
  • Task Selection Phase :
      • We use Upward rank to assign the priority to tasks in the DAG
  • Processor Selection Phase :
      • We combine both objective functions (cost and time) in a single function; the processor that maximizes that function for the current task is selected.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

proposed algorithm1
Proposed Algorithm

Heterogeneous Budget Constraint Scheduling Algorithm (HBCS)

0<=k<= 1

(Objective function)

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

experimental result
Experimental Result
  • Workflow Structure:
  • Synthetic DAG generation
    • (www.loria.fr/~suter/dags.html)
  • Applications have between 30 and 50 tasks, generated randomly.
  • Total number of DAGs in our simulation is 1000.
  • Workflow Budget: BUDGET = C cheapest + k (CHEFT – Ccheapest)

0<=k<= 1

Lower budget (k=0)  Cheapest scheduling, higher makespan

Highest budget (k=1)  shortest makespan (HEFT scheduling)

Performance Metric:

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

experimental result1
Experimental Result

Simulation Platform :

  • We use SIMGRID that allows a realistic description of the infrastructure parameters.
  • We consider a bandwidth sharing policy; only one processor can send data over one network link at a time.
  • We consider nodes of clusters from the GRID’5000platform.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

results
Results

Shopia

Rennes

Grenoble

HBCS Time complexity

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

conclusions 2
Conclusions (2)
  • Conclusion remarks
    • We considered a realistic model of the infrastructure;
    • The HBCS algorithm achieves better performances, in particular for lower budget values (makespan and time complexity);
  • Future directions
    • Compare other combinations of cost and time factors in the objective function;
    • Data locality;
    • Multiple DAG scheduling.

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013

slide29

Thank you!

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013