Abstract: Embodiments monitor statistics from groups of devices and generate an alarm upon detecting a utilization imbalance that is beyond a threshold. Particular balance statistics are periodically sampled, over a timeframe, for a group of devices configured to have balanced utilization. The devices are ranked at every data collection timestamp based on the gathered device statistics. The numbers of times each device appears within each rank over the timeframe are tallied. The device/rank summations are collectively used as a probability distribution representing the probability of each device being ranked at each of the rankings in the future. Based on this probability distribution, an entropy value that represents a summary of the imbalance of the group of devices over the timeframe is derived. An imbalance alert is generated when one or more entropy values for a group of devices shows an imbalanced utilization of the devices going beyond an identified imbalance threshold.

Abstract: Embodiments monitor statistics from groups of devices and generate an alarm upon detecting a utilization imbalance that is beyond a threshold. Particular balance statistics are periodically sampled, over a timeframe, for a group of devices configured to have balanced utilization. The devices are ranked at every data collection timestamp based on the gathered device statistics. The numbers of times each device appears within each rank over the timeframe are tallied. The device/rank summations are collectively used as a probability distribution representing the probability of each device being ranked at each of the rankings in the future. Based on this probability distribution, an entropy value that represents a summary of the imbalance of the group of devices over the timeframe is derived. An imbalance alert is generated when one or more entropy values for a group of devices shows an imbalanced utilization of the devices going beyond an identified imbalance threshold.

Abstract: A high efficiency search table is implemented with a multiple hash algorithm. The search table allows for exact match searching of arbitrary data sets with fixed latency. The probability of collisions from the hash algorithms is reduced through the use of oversized pointer tables allowing for a level of indirection between hash values and table entries. In the event of a collision in all hash functions, a firmware assisted cuckoo algorithm is employed to resolve the collision.

Abstract: In a Ternary Content Addressable Memory (TCAM) optimized for network packet classification, network operation rules are preprocessed into sub-lists in accordance with a decision tree based packet classification algorithm. The decision tree is encoded as a lookup memory, which is accessed with a predetermined base address of higher order address bits combined with lower order address bits obtained from specific bit positions from a received network packet. Depending on the nodes traversed in the decision tree, different base addresses and values from different bit positions of the packet are used until a sub-list of potential rules is identified. A bitwise comparison of values of each rule against the packet is then executed, the matching rule with the highest precedence is returned as the matching table entry and the configured value associated with the entry is prepended to the packet.