In order to alleviate the energy constraint in smartphones, computation offloading is regarded as an effective solution. In computation offloading, how to partition a given application is one of the key issues. On obtaining the best partition, due to the time complexity of partition algorithm, most researchers give up looking for a global optimal solution but to find local optimal. In this paper, a global optimal application partition algorithm is presented, the main strategy is scaling down the problem through merging the correlated nodes firstly, then using 0-1ILP to obtain the partition in order to make the mobile energy consumption achieve global optimal minimum, thus it has less time complexity than 0-1 ILP. Experimental results show that the proposed algorithm made the same global optimal partition results as 0-1 ILP, while it consumed less time and energy than 0-1 ILP.

Event matching plays a critical role in content-based publish/subscribe system. Most traditional methods focus on existing subscriptions separation and combination. However, an event usually comes with certain probability distribution in each dimension. Thus taking both existing subscriptions and probable coming event into consideration can improve event matching time efficiency. Based on that, we put forward PF (Priority Filter), a highly efficient event matching algorithm. By building up a unified model with historical subscriptions for continuous and discrete attributes, we derive formulas to calculate each attribute’s filtering rate. Besides, in order to guarantee time efficiency both in matching, inserting, and deleting, a red-black tree regarded as a priority filter is built up on all attributes according to filtering rate. Experiments demonstrate that PF has a 30% faster speed compared to existing methods with acceptable insertion and deletion time and memory consumption.