In-flash processing (IFP), which involves performing data computation inside NAND flash memory, holds high potential for improving the performance and energy efficiency of data-intensive application by minimizing data movement.Recent research has introduced several IFP architectures enabling bulk bitwise operations inside NAND flash chips to demonstrate this potential.However, previous IFP designs were limited to performing bitwise operations on data sensed within the same bitline, thus lacking the capability to handle more complex functions requiring interactions between data from different bitlines.This paper presents CrossBit, a new IFP architecture that enables both intra-bitline and inter-bitline operations with minimal additional circuitry integrated into commodity NAND flash memory.With the capability for inter-bitline operations, CrossBit facilitates in-flash error correction code (IF-ECC) operations, thereby enabling reliable multi-level cell (MLC) IFP.Moreover, CrossBit efficiently processes fundamental database queries that were previously inefficient with existing work that only supports intra-bitline operations.Experimental results show that the implementation of IF-ECC in CrossBit results in a substantial reduction in bit error rate (BER) for MLC operations, leading to 1.8× increase in bit-density by using MLC compared to previous IFP designs which uses SLC only.When used for accelerating fundamental database queries, CrossBit achieves notable average speedup and energy efficiency improvement of 2.1× and 2.5× compared to the state-of-the-art (SOTA) IFP architecture.We further demonstrate the practicality by processing the full set of end-toend database queries from the widely used Star schema benchmark, where CrossBit achieves 1.7× speedup.

Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.