![](bg7.png)
Amazing strategy has a higher cost than our strat-
egy because Amazing cannot guarantee the deadline.
Therefore in most of the cases with tighter deadlines,
Amazing will switch quickly to on-demand instance.
Depends on the buffer setting of Amazing. It could
perform the best, but also could be worst. Neverthe-
less, Amazing has much larger variance than other
compared strategies because it bids on the highest
spot price. It shows Amazing is a more risky bid-
ding strategy and will rely more often on on-demand
instances to complete the job.
Static has similar or even lower cost than Amaz-
ing and Dynamic. It shows that dynamically chang-
ing the price is not necessary, and could cause addi-
tional computation and running overhead as explained
in section 2.1.
5 RELATED WORK
Cloud economies has drawn a tremendous attention
over the past decade (Regev and Nisan, 1998; Stokely
et al., 2009b; Chaisiri et al., 2011; Genaud and Gossa,
2011; Chard et al., 2015; Oh et al., 2022). Part of
this is attributable to the dynamic nature of cloud and
the vast pool of underutilized resources in data cen-
ters (Stokely et al., 2009a). In auction-based cloud
economies such as Spot Instances (SI), availability
and cost saving are difficult tradeoff to make (Andrze-
jak et al., 2010b; Chaisiri et al., 2011; Genaud and
Gossa, 2011) since it involves understanding the mar-
ket fluctuations and the resource model including the
availability. Nevertheless, spot instances still provide
cheaper resources (Lu et al., 2013). It is therefore im-
portant to design a bidding strategy to benefit from
this resource model.
6 CONCLUSION
In this paper, we proposed a Static Bidding strategy to
efficiently decide the bid price for executing deadline-
constraint jobs on Spot Instances. The inputs of our
problem are the estimated job execution time and the
deadline to complete the job. With these information,
we use the historical market data to map the price fluc-
tuation in to random variable based on the Markov
chain decision process. Furthermore, to guarantee the
completion of the job within the constrained deadline,
on-demand instances are used when the deadline is
close. We have modeled and included these consider-
ations in the optimal solution. We then pointed out the
recursive behavior of solution. Next, we prove that by
a means of a Dynamic Programming algorithm, the
optimal bidding price can be derived with the check-
point and restart overhead. Our evaluations conducted
with real historical traces from Amazon Spot markets
show that Static Bidding strategy can significantly re-
duce the execution cost on Spot Instance, guarantee
the deadline requirement and effectively cope with the
checkpoint/restart overheads. As compared to the dy-
namic bidding strategy, our method is more practical
and suitable to the current spot markets. Moreover,
we can achieve similar or even better result than the
existing strategies.
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