scholarly journals Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning

2021 ◽  
Vol 13 (1) ◽  
pp. 21-40
Author(s):  
Chenxiao Wang ◽  
Zach Arani ◽  
Le Gruenwald ◽  
Laurent d'Orazio ◽  
Eleazar Leal
Keyword(s):  
Machine Learning ◽  
Response Time ◽  
Cost Estimation ◽  
Service Providers ◽  
Database Management System ◽  
Query Execution ◽  
Execution Plan ◽  
Query Optimizer ◽  
Query Response Time ◽  
Cloud Database

In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.

A Grouping Cache Based Joins Query Algorithm in MANET

2012 ◽  
Vol 532-533 ◽  
pp. 914-918
Author(s):  
Ya Hong Guo ◽  
Xu Liu ◽  
Qian Qian Ren
Keyword(s):  
Dynamic Programming ◽  
Response Time ◽  
Query Plan ◽  
Execution Plan ◽  
Query Response Time ◽  
Optimal Execution ◽  
Simulation Results ◽  
Query Algorithm ◽  
Cache Mechanism ◽  
Dynamic Programming Scheme

Aiming at the problem of joins query in MANET, we proposed a grouping cache mechanism, which builds grouping cache considering the features of data. The proposed mechanism can implement the cooperating cache between groups and update cache information dynamically, which leads to the improvement of query hit ratio and decrease of response time. Based on the grouping cache, a novel joins query algorithm is presented. The algorithm optimizes the query plan using dynamic programming scheme. Then it constructs an optimal execution plan for each sub join query with available cache data taken into account. Simulation results indicated that the group-based cache mechanism can improve the hit ratio, reduce query response time and conserve energy of the network efficiently.

Workload Management Systems for the Cloud Environment

2017 ◽  
pp. 94-113
Author(s):  
Eman A. Maghawry ◽  
Rasha M. Ismail ◽  
Nagwa. L. Badr ◽  
Mohamed F. Tolba
Keyword(s):  
Response Time ◽  
Performance Management ◽  
Management System ◽  
Database Management System ◽  
Cloud Environment ◽  
Workload Management ◽  
The Core ◽  
Query Response Time ◽  
Virtual Resources ◽  
Core Problem

Workload Management is a performance management process in which an autonomic database management system on a cloud environment efficiently makes use of its virtual resources. Workload management for concurrent queries is one of the challenging aspects of executing queries over the cloud. The core problem is to manage any unpredictable overload with respect to varying resource capabilities and performances. This chapter proposes an efficient workload management system for controlling the queries execution over a cloud. The chapter presents architecture to improve the query response time. It handles the user's queries then selecting the suitable resources for executing these queries. Furthermore, managing the life cycle of virtual resources through responding to any load that occurs on the resources. This is done by dynamically rebalancing the queries distribution load across the resources in the cloud. The results show that applying this Workload Management System improves the query response time by 68%.

Distribution of Database in Cloud Based on Associated Matrix

2014 ◽  
Vol 644-650 ◽  
pp. 3375-3378
Author(s):  
Lei Yue Yao ◽  
Wei Yang
Keyword(s):  
Response Time ◽  
Association Rules ◽  
Joint Distribution ◽  
Distributed Storage ◽  
Data Distribution ◽  
Data Manipulation ◽  
Distribution Strategy ◽  
Query Response Time ◽  
Cloud Database ◽  
Associated Matrix

Database is distributed stored in cloud. The paper found that joint distribution can base on frequent data manipulation, and frequent connection property. So it proposed cloud database data distribution strategy based on association rules. The data distributed storage is according to the frequent data manipulation rules. Experiments demonstrate that this strategy can significantly reduce database query response time, especially in connection query response time.

scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Optimization Problem ◽  
Service Providers ◽  
Virtual Network ◽  
Placement Problem ◽  
Service Function ◽  
Network Functions ◽  
Bandwidth Consumption

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

scholarly journals Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 63
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Network Performance ◽  
Service Providers ◽  
Video Conferencing ◽  
Machine Learning Algorithms ◽  
Performance Parameters ◽  
High Definition ◽  
Internet Service ◽  
Novel Approach ◽  
5 Ghz

Video conferencing services based on web real-time communication (WebRTC) protocol are growing in popularity among Internet users as multi-platform solutions enabling interactive communication from anywhere, especially during this pandemic era. Meanwhile, Internet service providers (ISPs) have deployed fiber links and customer premises equipment that operate according to recent 802.11ac/ax standards and promise users the ability to establish uninterrupted video conferencing calls with ultra-high-definition video and audio quality. However, the best-effort nature of 802.11 networks and the high variability of wireless medium conditions hinder users experiencing uninterrupted high-quality video conferencing. This paper presents a novel approach to estimate the perceived quality of service (PQoS) of video conferencing using only 802.11-specific network performance parameters collected from Wi-Fi access points (APs) on customer premises. This study produced datasets comprising 802.11-specific network performance parameters collected from off-the-shelf Wi-Fi APs operating at 802.11g/n/ac/ax standards on both 2.4 and 5 GHz frequency bands to train machine learning algorithms. In this way, we achieved classification accuracies of 92–98% in estimating the level of PQoS of video conferencing services on various Wi-Fi networks. To efficiently troubleshoot wireless issues, we further analyzed the machine learning model to correlate features in the model with the root cause of quality degradation. Thus, ISPs can utilize the approach presented in this study to provide predictable and measurable wireless quality by implementing a non-intrusive quality monitoring approach in the form of edge computing that preserves customers’ privacy while reducing the operational costs of monitoring and data analytics.

scholarly journals Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 621
Author(s):  
Maghsoud Morshedi ◽  
Josef Noll
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Network Performance ◽  
Service Providers ◽  
Perceived Quality ◽  
Internet Service Providers ◽  
Performance Parameters ◽  
Internet Service ◽  
Access Points

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

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