Disaggregated Memory Systems (DMS) hold substantial potential for cloud datacenters but face critical deployment barriers in multi-tenant RDMA environments. Existing DMS designs rely on idealized assumptions-overlooking interference from co-located RDMA applications, oversimplifying fabric topology considerations, and lacking elastic service-level objectives (SLOs) guarantees-resulting in performance degradation and resource inefficiency.

The Log-Structured Merge-tree (LSM-tree) has become a cornerstone of modern key-value stores (KVSs) due to its efficiency in handling write-intensive workloads. However, traditional monolithic LSM-tree designs suffer from write stalls caused by resource contention between Memtable flushing and SSTable compaction, while existing distributed systems adopt coarse-grained elasticity that limits resource utilization and responsiveness. This paper introduces ServerlessLSM, a kernelspace RDMA-odesigned Serverless workflow architecture for LSM-trees. By decoupling Memtable flushing and compaction into independent Serverless functions, ServerlessLSM enables fine-grained elasticity and low-latency state transfers through distributed OS primitives (e.g., remote fork, remote memory mapping). Evaluations demonstrate that ServerlessLSM reduces cold-start latency by$\mathbf{9 8 \%}$and achieves$\mathbf{2. 4} \times$higher throughput compared to state-of-the-art solutions, while maintaining space amplification below 11 %, validating its efficiency and costeffectiveness in cloud environments.

Large Language Model (LLM) inference suffers from inefficiencies in coupled prefill (P) and decoding (D) phases, leading to resource underutilization and scheduling bottlenecks. While disaggregated P-D architectures address this by isolating phases across asymmetric clusters, serverless deployments introduce critical challenges: cold-start latency during autoscaling and costly intermediate state transfers (e.g., KV cache) between distributed prefill and decoding instances. We present ServerlessPD, a system that co-designs remote fork with RDMA to enable near-instant autoscaling and zero-copy state transferring for serverless LLM inference. ServerlessPD introduces a RDMA-based OS kernel-integrated primitive that remotely forks active prefill instances into decoding instances across machines, bypassing cold starts by reusing pre-materialized GPU states, which grants child containers direct copy-on-write access to parent GPU memory. The system further employs GPU context interception to efficiently capture and replicate execution states, ensuring seamless state transfer. To optimize resource utilization, ServerlessPD integrates a dynamic launch-point algorithm that schedules fork operations based on real-time prefill-decoding dynamics, minimizing idle time and overlapping computation with state transfers. ServerlessPD demonstrates that RDMA-codeigned remote fork can unlock near-instant autoscaling and efficient state disaggregation for LLM serving.

Persistent key-value store based on LSM-trees represents one of the most advanced designs. Recent research shows that key-value separation has become a popular optimization method for LSM-tree. However, this storage architecture still incurs significant overhead when dealing with some query- and update-intensive workloads. In this paper, we propose$\text{LC}\text{KV}$, a key-value separated LSM-tree storage system built using the$\underline{L}earner-\underline{C}leaner$optimization to increase throughput. Learner represents the construction of learned indexes to increase query throughput, responsible for building models for the hot-readcold-written keys stored in the LSM-tree and values stored in the cold-written$\mathrm{v}\text{alue logs}$(vLogs). Cleaner represents the garbage$\mathrm{c}\text{ollector}$(GC) aimedat increasing update throughput, responsible not only for garbage collection but also for maintaining the sorting of the cold-written vLog. Evaluations show that LCKV outperforms other state-of-the-art solutions.

With the rapid development of cloud computing technology, a variety of emerging network traffic types have higher quality of service(QoS) requirements. However, the traditional Internet’s best-effort service cannot meet the demands of cloud computing applications. Therefore, we propose a stochastic network calculus(SNC) based QoS guarantee mechanism consisting of two parts. The first part is MTACC, a multi-threshold adaptive admission control algorithm based on network calculus. MTACC classifies network traffic, calculates resource requirements, and introduces admission probabilities and demarcation parameters. As the network load reaches various thresholds, MTACC adjusts the demarcation parameter to modify the admission probabilities accordingly. The second part is TSRA, a two-stage resource allocation algorithm. In the first stage, basic resources are allocated to ensure the minimum resource requirements of the network traffic based on SNC. In the second stage, additional idle resources are allocated to improve the QoS of the network traffic based on the resource utility function. Finally, we simulate the QoS guarantee techniques using NS3. By comparing with Simple Sum and SCAC, we verify the effectiveness of our proposed QoS guarantee techniques. They provide robust performance guarantees for multi-class traffic such as delay-sensitive, bandwidth-sensitive, and packet loss-sensitive traffic.

In order to minimize the damage of engineering blasting vibration, it is very important to accurately predict the blasting peak velocity. The GEP algorithm is used to analyze the relationship between blasting vibration and related parameters. Aiming to improve the time performance of GEP in dealing with large-scale engineering blasting data, the gene structure of GEP is adjusted to head, body and tail. Then the GEP algorithm is paralleled on Spark cluster. The optimized parallel GEP can greatly improves the global search efficiency. The experimental results show that the method can obviously reduce the running time and can improve the prediction accuracy in most cases.

Currently, SAAS (Software as a Service) solutions are usually provided for business, such as salesforce.com. Few work focus on the application software for high performance scientific computing. However, in the high performance cloud computing environment, traditional application software is not intrinsically service oriented. And the limitation of traditional software licenses is a bottleneck problem for large scale of dynamic users. To enable on-demand services for applications, we propose a solution: Application Software as a Service (ASAAS). It provides a web services portal, an on-demand software license service for the users. Application software is wrapped as web services on the basis of underlying computational resources. With a pay-for-use mode, there is no limitation for the licenses any more. The instant service rate, average job response time, and cost are analyzed for an evaluation. A case of implementation and the evaluation show that ASAAS can bring a much better effect than traditional mechanism.

By using personal information in a pervasive computing environment, context-aware applications can provide appropriate services for people. This personal information is often involved in personal privacy. In order to protect personal privacy concerns about personal information, privacy role is proposed to control access personal information. We also construct an information system about the privacy decision of personal information disclosure based on people's interaction history. In the initial period of personal information disclosure, the privacy decision is made by people and the information system is constructed based on the decision data. Then privacy disclosure policies are extracted from this information system using rough set theory. According to deducing from the privacy disclosure policies and people's context information, the contextaware application is assigned to an adequate privacy role. It reduces the distraction of privacy decision for people. A case study further shows the proposed method is effective. Finally, it provides about the overload performance of privacy role analysis personaengine.