Jorge G. Barbosa , Altino M. Sampaio , Hamid Harabnejad

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

Jorge G. Barbosa, Altino M. Sampaio, Hamid Harabnejad

Universidade do Porto, Faculdade de Engenharia, LIACCPorto, Portugal, [email protected]

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

COST IC804 – IC805 Joint meeting, Tenerife, February 7-8 2013 2

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






Dynamic Power- and Failure-aware Cloud Resources Allocation for Sets of Independent Tasks

Cloud computing paradigm

Image source: http://www.commputation.kit.edu/92.php

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.


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

(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.

VMM VMM VMMVMM

VM1 VM 4VM 2

PM 1 PM 2 PM 3 PM m

...

Task 1 Task 2 Task n

VM n

Task 3

PM – Physical Machine


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

Cavg average capacity from reliable PMs.

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)


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


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.


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

CPU

CPU% Powerconsumption

100

0

VM

VMVM

VMVM

VM

timePM3

PM2

PM1

– Failure – Stop & copy migration

Incr

easin

g

– Failure prediction accuracy


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 job and tasks deadlines are defined by user

Private cloud management architecture


Power Model Linear power model

P = p1 + p2.CPU%

Power Efficiency of P

Completion rate of users’ jobs

Working Efficiency

%( ) 1 2

1 2 %

CPUEff P p p

p p CPU

Example of power efficiency curve (p1 = 175w, p2 = 75w)

1( )

1

lJccEff J kJss

( )

11( ) ,

uEff Pf ii

s uEff Eff J u hf

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


Proposed algorithms Minimum Time Task Execution (MTTE) algorithm

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.

PM i capacity constraints 1

qr Ct it

Slack time to accomplish task t

max

Wtdt rt


Proposed algorithms Relaxed Time Task Execution (RTTE) algorithm

Unlike MTTE, the RTTE algorithm always reserves to VM the minimum amount of resources necessary to accomplish the task within its deadline

Host

CPU

100%

0%

VM

Cap set in Xen credit scheduler

CAP


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;


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)


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.


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.


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


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.







A Job is represented by a workflowA workflow is a Directed Acyclic Graph (DAG)

a node is an individual task

an edge represents the inter-job dependency

CPU1

CPU2

CPU3

Workflow schedulingMapping Tasks to ResourcesMain goal is to have a lower finish time of the exit task


IntroductionTarget 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-objective problem.

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 othercriterion under this constraint.


Proposed AlgorithmHeterogeneous 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.


Proposed AlgorithmHeterogeneous Budget Constraint Scheduling Algorithm (HBCS)

0<=k<= 1

(Objective function)

vtimevtip TimeCCostCworthiness ..cos)(

25

Experimental Result

0<=k<= 1

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

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)Lower budget (k=0) Cheapest scheduling, higher makespanHighest budget (k=1) shortest makespan (HEFT scheduling)

Performance Metric:

http://www.loria.fr/~suter/dags.html


Experimental ResultSimulation 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’5000 platform.


ResultsShopia Rennes Grenoble

HBCS Time complexity


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.

29

THANK YOU!

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

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Jorge G. Barbosa , Altino M. Sampaio , Hamid Harabnejad

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