Heuristic Scheduling Algorithms for Allocation of Virtualized
Resources Allocation Algorithms for Distributed and ... · Introduction •Resource co-allocation...
Transcript of Resources Allocation Algorithms for Distributed and ... · Introduction •Resource co-allocation...
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Dependable and Coordinated Resources Allocation Algorithms for Distributed
Computing
Victor Toporkov and Dmitry Yemelyanov
National Research University “Moscow Power Engineering Institute”
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Introduction
• Resource co-allocation and scheduling of complex sets of parallel jobs is an essential issue in distributed environments with non-dedicated resources
• Economic models are used to solve resource sharing and scheduling problems in a transparent and an efficient way in cloud computing, utility Grids, and multi-agent systems
• The main aspect of job-flow scheduling is its efficiency in terms of QoS and resources utilization
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Job Flow Scheduling Problem
Job 1 Job 2 Job N-1
Job N
Start Time Runtime Cost Data
Storage
. . .
Nodes
Reserved
Reserved
Local task
Reserved Reserved
1
2
3
4
5 Time
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Job Flow Scheduling: V. Toporkov et al. 2017. Cyclic Anticipation Scheduling in Grid VOs with Stakeholders Preferences. PaCT 2017, LNCS 10421, pp. 372–383.
R E S O U R C E S
Cycle i-1 Cycle i
Job Batch Job Batch Job Flow
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Job Resource Request
The resource requirements are arranged into a resource request containing:
n - number of simultaneously reserved computational nodes
p - minimal performance requirement for each computational node
V – computational volume for a single node task
C - maximum total job execution cost (budget)
z – preferred job optimization criterion
1
2
n
.
.
t = V/pmin
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Window Search Problem
Nodes
Time
Reserved
Reserved
Local task
Reserved Reserved
1
2
3
4
5
Slot: • Node • Performance • Cost • Start Time • Finish Time
Allocate a window of four nodes for a time T, with requirements on nodes performance and total cost. Minimize window start time:
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Deadline and Scheduling Horizon
Reserved
Reserved
Local task
Reserved Reserved
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2
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5 Time
Job 1 Job 2
There’s a practical limit on the slots availability: • deadline • backfilling • scheduling horizon
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General Window Search Scheme
All available time-slots are ordered by the start time;
for each pi in (pmin; pmax) {
while there is at least one slot available {
Add next available slot to the window list;
Check all slots in the window considering required length t = V/pmin
and remove the slots being late;
Select n-slot window best by the given user criterion Z; }
}
return the best of the found interim windows;
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Window Search Scheme Visualized
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Increasing Start Time
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2
3
4
3
2
1
6
4
3
7
6
4
3
Slots
3
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7
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Optimal Slots Subset Allocation
c1, z1
c2, z2
cm, zm
…
1
2
m
1
2
n
n < m
ci, zi
cj, zj
ck, zk
Maximize:
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Optimization Scheme 0-1 Knapsack Problem with O(m∗C)
k=1 c=3 z=3
c=2 z=1
c=4 z=6
0 - - -
1 - - -
2 - 1 1
3 3 3 3
4 3 3 6
5 3 3 6
6 3 3 6
k=2 c=3 z=3
c=2 z=1
c=4 z=6
0 - - -
1 - - -
2 - - -
3 - - -
4 - - -
5 - 4 4
6 - 4 7
Cj
i
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n << m
n << C
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Simulation Study https://github.com/dmieter/mimapr Intel Core i3-4130,16GB RAM
Simulation environment: • heterogeneous resource domain model
N =100 computing nodes
L = 1200 scheduling horizon
• non-linear probabilistic model for the computing environment parameters generation: cost, performance, initial load
• a space-shared resources allocation policy
Job request:
n = 7 nodes
V = 800 computational volume
C = 644 maximum budget
qi in [0;10] randomly generated for
each node to compare algorithms against Q (sum of qi, i = 1,…,n)
q=5.6
q=1.2
q=9.4
q=4.3
q=3.9
q=2.0
q=7.1
q=5.2
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Resource Domain Utilization Example
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Algorithms Comparison
• First Fit returns first suitable and affordable window found
• Multiple Best returns the best window over a set of execution alternatives found
• General Window Search Scheme:
Min Finish Time
Min Runtime
Min Cost
• Max Q additionally implements an optimal slots subset allocation
Kovalenko et al., 2007 Buyya et al., 2007
Ernemann et al., 2002
Toporkov et al., 2012
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Toporkov, Yemelyanov, 2018
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Simulation Study: Time I (7 nodes out of 100)
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Simulation Study: Time II (7 nodes out of 40)
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Simulation Study: Execution Cost
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Simulation Study: Custom Criterion Q = sum of qi, i = 1,…,n, qi in [0;10], Q in [0;70]
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Running Time and Complexity
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Running Time Depending on the Scheduling Interval Length L
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Running Time Depending on a Number n of Nodes Required for a Job Execution
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Conclusions and Further Work
The general square window search algorithm with the special slots subset allocation procedure is proposed for the resources co-allocation problem
Simulation study proved 44% advantage over the First Fit algorithm and 18% over the MultipleBest optimization heuristic
As a drawback, the general case algorithm requires 600 times longer realization time compared to FirstFit
In our further work, we will be focused on a practical resources allocation tasks implementation based on the proposed general case approach
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Acknowledgments
This work was partially supported by:
Ministry on Education and Science of the Russian Federation (project no. 2.9606.2017/8.9)
Council on Grants of the President of the Russian Federation for State Support of Young Scientists (grant YPhD-2297.2017.9)
RFBR (grants 18-07-00456 and 18-07-00534)
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References https://github.com/dmieter/mimapr СloudOfferFinder SquareWindowFinder https://srmpds.github.io/
International Workshop on SRMPDS (Scheduling and Resource Management for Parallel and Distributed Systems) 2018, University of Oregon, USA
https://www.youtube.com/watch?v=h6KfBKdVHOU Victor Toporkov and Dmitry Yemelyanov. Resources Co-Allocation
Optimization Algorithms for Distributed Computing Environments
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Thank You!
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