Authors:
Daniel Temp
1
;
2
;
Igor Capeletti
2
;
Ariel Goes de Castro
2
;
Paulo Silas Severo de Souza
3
;
Arthur Lorenzon
4
;
Marcelo Luizelli
2
and
Fábio Rossi
1
;
2
Affiliations:
1
Federal Institute Farroupilha, Alegrete, Brazil
;
2
Federal University of Pampa, Alegrete, Brazil
;
3
Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
;
4
Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Keyword(s):
Cloud, Cost, Heuristics, Latency, Service Placement, Simulation.
Abstract:
Composite applications have the versatility of maintaining several functions and services geographically distributed but being part of the same application. This particular software architecture fits in very easily with the model of distributed regions present in most cloud players. In this way, the search for leasing applications at the lowest cost becomes a reality, given that the application services can be in different players at a lower cost, as long as the performance metrics of the application as a whole are met. Performing provisioning decisions considering the allocation cost of different providers and the latency requirements of applications is not trivial, as these requirements are often conflicting, and finding good trade-offs involves the analysis of a large-scale combinatorial problem. Accordingly, this paper presents Clover, a placement algorithm that employs score-based heuristic procedures to find the best provisioning plan for hosting composite applications in geogr
aphically distributed cloud environments. Simulated experiments using real latency traces from Amazon Web Services indicate that Clover can achieve near-optimal results, reducing latency issues and allocation cost by up to 74.47% and 21.2%, respectively, compared to baseline strategies.
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