A Vision of a Decisional Model for Re-optimizing Query Execution Plans Based on Machine Learning Techniques

International audienceMany of the existing cloud database query optimization algorithms target reducing the monetary cost paid to cloud service providers in addition to query response time. These query optimization algorithms rely on an accurate cost estimation so that the optimal query execution plan (QEP) is selected. The cloud environment is dynamic, meaning the hardware configuration, data usage, and workload allocations are continuously changing. These dynamic changes make an accurate query cost estimation difficult to obtain. Concurrently, the query execution plan must be adjusted automatically to address these changes. In order to optimize the QEP with a more accurate cost estimation, the query needs to be optimized multiple times during execution. On top of this, the most updated estimation should be used for each optimization. However, issues arise when deciding to pause the execution for minimum overhead. In this paper, we present our vision of a method that uses machine learning techniques to predict the best timings for optimization during execution

A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems

International audienceCurrent mobile cloud database systems are widespread and require special considerations for mobile devices. Although many systems rely on numerous metrics for use and optimization, few systems leverage metrics for decisional cache replacement on the mobile device. In this paper we introduce the Lowest Scored Replacement (LSR) policy-a novel cache replacement policy based on a predefined score which leverages contextual mobile data and user preferences for decisional replacement. We show an implementation of the policy using our previously proposed MOCCAD-Cache as our decisional semantic cache and our Normalized Weighted Sum Algorithm (NWSA) as a score basis. Our score normalization is based on the factors of query response time, energy spent on mobile device, and monetary cost to be paid to a cloud provider. We then demonstrate a relevant scenario for LSR, where it excels in comparison to the Least Recently Used (LRU) and Least Frequently Used (LFU) cache replacement policies

A Vision of a Decisional Model for Re-optimizing Query Execution Plans Based on Machine Learning Techniques

International audienceMany of the existing cloud database query optimization algorithms target reducing the monetary cost paid to cloud service providers in addition to query response time. These query optimization algorithms rely on an accurate cost estimation so that the optimal query execution plan (QEP) is selected. The cloud environment is dynamic, meaning the hardware configuration, data usage, and workload allocations are continuously changing. These dynamic changes make an accurate query cost estimation difficult to obtain. Concurrently, the query execution plan must be adjusted automatically to address these changes. In order to optimize the QEP with a more accurate cost estimation, the query needs to be optimized multiple times during execution. On top of this, the most updated estimation should be used for each optimization. However, issues arise when deciding to pause the execution for minimum overhead. In this paper, we present our vision of a method that uses machine learning techniques to predict the best timings for optimization during execution

Modernizing Storage Conditions for Fresh Osteochondral Allografts by Optimizing Viability at Physiologic Temperatures and Conditions

Objective. Osteochondral allograft (OCA) transplantation has demonstrated good long-term outcomes in treatment of cartilage defects. Viability, a key factor in clinical success, decreases with peri-implantation storage at 4°C during pathogen testing, matching logistics, and transportation. Modern, physiologic storage conditions may improve viability and enhance outcomes. Design. Osteochondral specimens from total knee arthroplasty patients (6 males, 5 females, age 56.4 ± 2.2 years) were stored in media and incubated at normoxia (21% O2) at 22°C or 37°C, and hypoxia (2% O2) at 37°C. Histology, live-dead staining, and quantitative polymerase chain reaction (qPCR) was performed 24 hours after harvest and following 7 days of incubation. Tissue architecture, cell viability, and gene expression were analyzed. Results. No significant viability or gene expression deterioration of cartilage was observed 1-week postincubation at 37°C, with or without hypoxia. Baseline viable cell density (VCD) was 94.0% ± 2.7% at day 1. At day 7, VCD was 95.1% (37°C) with normoxic storage and 92.2% (37°C) with hypoxic storage (P ≥ 0.27). Day 7 VCD (22°C) incubation was significantly lower than both the baseline and 37°C storage values (65.6%; P < 0.01). COL1A1, COL1A2, and ACAN qPCR expression was unchanged from baseline (P < 0.05) for all storage conditions at day 7, while CD163 expression, indicative of inflammatory macrophages and monocytes, was significantly lower in the 37°C groups (P < 0.01). Conclusion. Physiologic storage at 37°C demonstrates improved chondrocyte viability and metabolism, and maintained collagen expression compared with storage at 22°C. These novel findings guide development of a method to optimize short-term fresh OCA storage, which may lead to improved clinical results