ABSTRACT In hyperscale, multi‐tenant serverless databases, the substantial performance disparity between cache hits (microseconds, CPU‐bound) and cache misses (milliseconds, I/O‐bound) leads to severe interference among tenants. Traditional reactive resource management models, which respond only after an expensive cache miss occurs, fail to prevent I/O‐intensive noisy neighbors from degrading the latency experienced by well‐behaved tenants. This paper proposes a novel preemptive isolation approach: the Predictive Cache‐Aware Dispatcher (PCAD). Embedded within the database proxy layer, PCAD leverages a real‐time machine learning model—the Cache Oracle—to predict the cache state of each request prior to dispatch. This foresight enables two key optimizations: (1) preemptive routing of likely cache misses to a dedicated, concurrency‐limited I/O worker pool, thereby protecting the CPU‐optimized fast path from contention, and (2) speculative prefetching to hide disk access latency for predicted misses. Using production traces from a large‐scale multi‐tenant cloud database, we demonstrate that the Cache Oracle achieves over 91% F1‐score in cache miss prediction. Simulations show that PCAD effectively eliminates cross‐tenant interference caused by I/O bursts, sustaining stable sub‐millisecond P99 latency, while speculative prefetching reduces perceived cache miss latency by over 90%. This preemptive predict and prevent approach marks a significant advancement toward more robust and high‐performance autonomous database systems.

This paper introduces the SecureSphere framework, which employs the Reed Solomon Secret Sharing (RS-SS) scheme for secure and robust data outsourcing and query processing. It ensures data confidentiality, even in collusion scenarios among cloud servers, and guarantees query result integrity against arbitrary failures and malicious actions using RS-SS’s error detection and correction capabilities. SecureSphere also enhances resilience against inference attacks by expanding and partitioning each attribute domain, assigning ranges to attribute values, and selecting random coefficients from these ranges to create the distribution polynomial in the RS-SS scheme. It processes aggregation SQL queries server-side without inter-server communication, and experimental results confirm its practical feasibility in real-world applications.

Summary IOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network.

Summary Ensuring the seamless operation of cloud computing services is paramount for meeting user demands and ensuring business continuity. Fault‐tolerant self‐healing techniques play a crucial role in enhancing the reliability and availability of cloud platforms, minimizing downtime and ensuring uninterrupted service delivery. This article systematically categorizes and analyzes existing research on fault‐tolerant self‐healing techniques published between 2005 and 2024. We provide a comprehensive technical taxonomy organizing self‐healing techniques based on fault tolerance processes, encompassing considerations for both reliability and availability. Additionally, we evaluate applications of proactive self‐healing techniques, highlighting their achievements, and limitations in enhancing service continuity. Strategies to address identified weaknesses are discussed, alongside future research challenges and open issues in the domain of cloud resilience. Through this analysis, the article contributes to understanding self‐healing techniques in cloud computing, offering insights into their effectiveness in ensuring service continuity. The findings aim to guide future research efforts in developing more robust and resilient cloud infrastructures, ultimately enhancing overall service reliability and availability. By emphasizing the importance of fault tolerance and self‐healing techniques, this article lays the foundation for advancing the state‐of‐the‐art in cloud computing.

This paper presents the extended temporal aligner as a temporal primitive, and proposes a set of reduction rules that employ this primitive to convert a temporal join operator to its non-temporal equivalent. The rules cover all types of temporal joins, including inner join, outer joins, and anti-join. Preliminary experimental results demonstrate that the integration of the extended temporal aligner and the reduction rules can efficiently process temporal join queries.