Automated service classification plays a pivotal role in the processes of service discovery, selection, and composition. In real scenarios, service classification usually faces an open world where out-of-distribution (OOD) service samples exist and cannot be fully covered by a predefined class label set. This problem arises when either new services (mobile applications, protocols, etc.) appear or known services change their behaviour. Hence, the task of OOD detection has been attracting more and more attention in recent years. In this paper, based on feature representations with supervised contrastive learning, we propose a novel joint learning model that can estimate whether an input service sample is OOD and in the meantime, predict its in-distribution (IND) class label. Moreover, we propose to synthesize OOD service samples with known IND service samples from different classes, which can further help in training the proposed model. The extensive experimental evaluation on three datasets shows that our method outperforms the state-of-the-art model in most cases, and can obtain a significant increase in Accuracy and F1-score over the unknown class when the proportion of IND classes is low.

State machine replication (SMR) is a fundamental fault tolerant technique for distributed systems to guarantee consistency among replicas via sequential execution of commands. With the development of cloud computing, parallel SMR has been recently proposed for large scale cloud datacenters. In this paper, we propose PDFE, a novel parallel SMR scheme, which realizes flexible dispatch of parallel ordered commands for parallel executing. In PDFE, the mapping/binding between ordering threads and work threads becomes dynamic, and commands can be dispatched according to the work load level of different threads. Such flexibility can help achieve two levels of load balancing: load balancing between ordering threads and work threads, and load balancing among work threads. The major challenge in our work lies in the inconsistency problem caused by dynamic changes in command dis-patch, and it is addressed by a specially designed mechanism. Compared with existing parallel SMR schemes, PDFE can achieve better load balancing and higher system efficiency. Such advantages are validated by experimental performance evaluation.

Large Web applications usually require replicating data across geo-distributed datacenters to achieve high locality, durability and availability. However, maintaining strong consistency in geo-replicated systems usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to cope with such a challenge. In this paper, we propose DTC (Dynamic Transaction Chopping), a novel technique that chops transactions and checks their conflicts in a dynamic and automatic way, during application execution. DTC mainly consists of two parts: a dynamic chopper that chops transaction dynamically according to data partition scheme, and a conflict detection algorithm for determining the safety of the dynamic chopping. Compared with existing transaction chopping technique for geo-replicated systems, DTC has several advantages, including transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to dynamic partition schemes. We implement our DTC technique and conduct experiments to examine the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much more piecewise execution than the existing chopping approach does, and reduce execution time obviously.