Open Access

Nico Kruber

• One of the biggest impacts on the performance of a Distributed Hash Table (DHT), once established, is its ability to balance load among its nodes. DHTs supporting range queries for example suffer from a potentially huge skew in the distribution of their items since techniques such as consistent hashing can not be applied. Thus explicit load balancing schemes need to be deployed. Several such schemes have been developed and are part of recent research, most of them using only information locally available in order to scale to arbitrary systems. Gossiping techniques however allow the retrieval of fairly good estimates of global information with low overhead. Such information can then be added to existing load balancing algorithms that can use the additional knowledge to improve their performance. Within this thesis several schemes are developed that use global information like the average load and the standard deviation of the load among the nodes to primarily reduce the number of items an algorithm moves to achieve a certain balance. Two novel load balancing algorithms have then been equipped with implementations of those schemes and have been simulated on several scenarios. Most of these variants show better balance results and move far less items than the algorithms they are based on. The best of the developed algorithms achieves a 15-30% better balance and moves only about 50-70% of the number of items its underlying algorithm moves. This variation is also very robust to erroneous estimates and scales linearly with the system size and system load. Further experiments with self-tuning algorithms that set an algorithm’s parameter according to the system’s state show that even more improvements can be gained if additionally applied. Such a variant based on the algorithm described by Karger and Ruhl shows the same balance improvements of 15-30% as the variant above but reduces the number of item movements further to 40-65%.