Publications by Spyridon Giannakakis

This thesis tackles the problem of allocating resources for networked applications in data center topologies, which is a known NP-hard decision problem. Various proposals have been introduced in the past which either make simplifying assumptions or solve a special instance of the problem. Our approach satisfies all demands on the application side as well as resource constraints of data centers, while remaining scalable. What is more, it is a generic solution that can be further tailored to specific workloads or cloud architectures. In this report we propose and evaluate two algorithms that perform the resource allocation decision and placement for various application topologies and workloads. The first one utilizes a greedy approach of placing virtual links sequentially and back tracking when a constraint is not met. The second one clusters the application in a network optimized package, deducts a subgraph of the data center topology and makes the placement decision using a customizable heuristic. Depending on the decision, the placement takes place in the reduced subgraph. Both algorithm implementations have been evaluated using a variety of realistic workloads, different testing scenarios and data center topologies. The results show that while both algorithms perform well in all cases, depending on the testing conditions, utilizing one of the two yields better results. Finally we propose a technique to select which or a combination of the two algorithms should be used by data center operators to allocate the resources of their infraststructure more effciently.

@mastersthesis{abc,
	abstract = {This thesis tackles the problem of allocating resources for networked applications in data center topologies, which is a known NP-hard decision problem. Various proposals have been introduced in the past which either make simplifying assumptions or solve a special instance of the problem. Our approach satisfies all demands on the application side as well as resource constraints of data centers, while remaining scalable. What is more, it is a generic solution that can be further tailored to specific workloads or cloud architectures.
In this report we propose and evaluate two algorithms that perform the resource allocation decision and placement for various application topologies and workloads. The first one utilizes a greedy approach of placing virtual links sequentially and back tracking when a constraint is not met. The second one clusters the application in a network optimized package, deducts a subgraph of the data center topology and makes the placement decision using a customizable heuristic. Depending on the decision, the placement takes place in the reduced subgraph. Both algorithm implementations have been evaluated using a variety of realistic workloads, different testing scenarios and data center topologies. The results show that while both algorithms perform well in all cases, depending on the testing conditions, utilizing one of the two yields better results. Finally we propose a technique to select which or a combination of the two algorithms should be used by data center operators to allocate the resources of their infraststructure more effciently.},
	author = {Spyridon Giannakakis},
	school = {60},
	title = {Design of Traffic-Aware Placement Techniques of Applications in Modern Data Centers},
	year = {2012}
}