Load-aware inter-co-processor parallelism in database query processing

Breß, Sebastian; Siegmund, Norbert; Heimel, Max; Saecker, Michael; Lauer, Tobias; Bellatreche, Ladjel; Saake, Gunter

doi:10.1016/j.datak.2014.07.003

Cited by 13 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Breß et al 76 have distributed query workloads on available (co-)processors for efficient query processing by providing precise estimates for the query optimizer. They have linearized query plans into operator streams and model data dependencies between operators.…”

Section: An Overview Of Non-deterministic Adaptive Query Optimizationmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Query optimization is considered as one of the main challenges of query processing phases in the cloud environments. The query optimizer attempts to provide the most optimal execution plan by considering the possible query plans. Therefore, the execution cost of a query can be affected by some factors, including communication costs, unavailability of resources, and access to large distributed data sets. In addition, it is known as NP-hard problem and many researchers are focused on this problem in recent years. Some techniques are proposed for solving this problem. Deterministic and non-deterministic methods are two main categories to study these techniques. The deterministic and non-deterministic query optimization methods can be further divided into three subcategories, cost-based query plan enumeration, multiple query optimization, and adaptive query optimization methods. Moreover, this paper presents the advantages and disadvantages of the algorithms for solving the query optimization problems in the cloud environments. Moreover, these techniques are compared in terms of optimization, time, cost, efficiency, and scalability. Finally, some key areas are offered to improve the cloud query optimization mechanisms in the future. KEYWORDScloud computing, database, query optimization, review INTRODUCTIONThe data transfer operation and resource sharing are facilitated by rapid progress of the distributed IT-based systems. 1,2 Cloud computing supports several computers through a network. 3 The cloud computing has a large-scale distributed architecture and virtualized services to deliver the requests to users. 4,5 Moreover, the cloud computing provides important financial advantages and long level cooperation possibilities for organizations and institutions. 6 The cloud computing is defined as a distributed IT-based technology based on service business model. 7This paradigm provides many benefits for users, such as the provision of computing capabilities, heterogeneous network access, scalability, and elasticity with measured services. 8,9 The cloud computing gives shared access to a large pool of resources, including data storage, memory, processing, and virtual machines. 10 A cloud client, such as a web browser and mobile app can be helpful in accessing these services. 11 Enormous amounts of data are retrieved from geo-distributed data sources and cross-layer data-handling requirements to make a change in business model. 12 The cloud storage as one of the main services is provided by cloud computing, 13 which allows the users to store their data in virtual pools instead of their servers. 14 In addition, subscribers can access the data from any area of cloud. 15 Therefore, the reliability and availability are necessary to recover the information and query processing.The query processing involves three main steps, as shown in Figure 1. First, the query is translated into an expression of the relational algebra.Second, an optimal evaluation plan for the query plan is generated. The query optimization is the main part o...

show abstract

Section: An Overview Of Non-deterministic Adaptive Query Optimizationmentioning

confidence: 99%

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Azhir

Navimipour

Hosseinzadeh

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Breß et.al. [24] proposed a workload optimization scheme, called probability outsourcing. They considered benchmarking of 4 database operations aggregate, select,sort, and join across GPU devices.…”

Section: Related Workmentioning

confidence: 99%

TripleID-Q: RDF Query Processing Framework Using GPU

Chantrapornchai

Choksuchat

2018

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Resource Description Framework (RDF) data represents information linkage around the Internet. It uses Internationalized Resources Identifier (IRI) which can be referred to external information. Typically, an RDF data is serialized as a large text file which contains millions of relationships. In this work, we propose a framework based on TripleID-Q, for query processing of large RDF data in a GPU. The key elements of the framework are 1) a compact representation suitable for a Graphics Processing Unit (GPU) and 2) its simple representation conversion method which optimizes the preprocessing overhead. Together with the framework, we propose parallel algorithms which utilize thousands of GPU threads to look for specific data for a given query as well as to perform basic query operations such as union, join, and filter. The TripleID representation is smaller than the original representation 3-4 times. Querying from TripleID using a GPU is up to 108 times faster than using the traditional RDF tool. The speedup can be more than 1,000 times over the traditional RDF store when processing a complex query with union and join of many subqueries.

show abstract

“…Takizawa et al [ 11 ] present a runtime environment SPRAT(Stream Programming with Runtime Auto-Tuning) that dynamically selects an appropriate processor, so as to improve the energy efficiency. Breß et al [ 12 ] propose heuristics that optimize database query processing for response time and throughput based on load-aware inter-co-processor parallelism. Furthermore, Lang et al [ 13 ] develop an energy-aware model to predict how to distribute the data to the processing units to achieve the most energy-efficient execution.…”

Section: Related Workmentioning

confidence: 99%

Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics

Zhang

Duan

Chen

et al. 2016

Sensors

View full text Add to dashboard Cite

Storm-based stream processing is widely used for real-time large-scale distributed processing. Knowing the run-time status and ensuring performance is critical to providing expected dependability for some applications, e.g., continuous video processing for security surveillance. The existing scheduling strategies’ granularity is too coarse to have good performance, and mainly considers network resources without computing resources while scheduling. In this paper, we propose Healthcare4Storm, a framework that finds Storm insights based on Storm metrics to gain knowledge from the health status of an application, finally ending up with smart scheduling decisions. It takes into account both network and computing resources and conducts scheduling at a fine-grained level using tuples instead of topologies. The comprehensive evaluation shows that the proposed framework has good performance and can improve the dependability of the Storm-based applications.

show abstract

Load-aware inter-co-processor parallelism in database query processing

Cited by 13 publications

References 6 publications

Deterministic and non‐deterministic query optimization techniques in the cloud computing

Deterministic and non‐deterministic query optimization techniques in the cloud computing

TripleID-Q: RDF Query Processing Framework Using GPU

Healthcare4VideoStorm: Making Smart Decisions Based on Storm Metrics

Contact Info

Product

Resources

About