This thesis investigates the efficient synchronization of concurrent operations on AVL trees in multicore environments. While AVL trees offer logarithmic-time guarantees, concurrent access requires synchronization, which often leads to contention and scalability bottlenecks. Delegation-based synchronization methods, such as Flat Combining, address contention by batching operations but introduce a new bottleneck due to their reliance on a single combiner thread. To overcome this limitation, this thesis proposes a Multi Combining approach that alleviates the bottleneck by partitioning operation batches across multiple combiners. The design leverages the structural properties of AVL trees by partitioning the workload based on the root node, dedicating separate combiners for operations on the left and right subtrees. To minimize the synchronization overhead that is typically required for frequent root updates, the system utilizes a relaxed AVL tree design, which defers rebalancing until a specific threshold is reached. Extensive evaluation shows that Multi Combining consistently outperforms both traditional locking and Flat Combining approaches. Insertion- and removal-heavy workloads achieve up to double the throughput compared to competing methods, while read-heavy workloads benefit from parallel execution of lookups. Overall, the results demonstrate that Multi Combining effectively balances batching and parallelism, providing a scalable synchronization strategy for concurrent balanced trees.