Incentive-Based Scheduling for Green Computational Grid

2021 ◽  
pp. 272-293
Author(s):  
Low Tang Jung ◽  
Ahmed Abba Haruna
Keyword(s):  
High Performance ◽  
Data Centers ◽  
Large Data ◽  
Computing System ◽  
Green Computing ◽  
Grid Environment ◽  
Computing Grid ◽  
Main Component ◽  
It Operations ◽  
Over Time

In the computing grid environment, jobs scheduling is fundamentally the process of allocating computing jobs with choices relevant to the available resources. As the scale of grid computing system grows in size over time, exponential increase in energy consumption is foreseen. As such, large data centers (DC) are embarking on green computing initiatives to address the IT operations impact on the environment. The main component within a computing system consuming the most electricity and generating the most heat is the microprocessor. The heat generated by these high-performance microprocessors is emitting high CO2 footprint. Therefore, jobs scheduling with thermal considerations (thermal-aware) to the microprocessors is important in DC grid operations. An approach for jobs scheduling is proposed in this chapter for reducing electricity usage (green computing) in DC grid. This approach is the outcome of the R&D works based on the DC grid environment in Universiti Teknologi PETRONAS, Malaysia.

HAMR: A dataflow-based real-time in-memory cluster computing engine

2016 ◽  
Vol 31 (5) ◽  
pp. 361-374 ◽  
Author(s):  
Yao Wu ◽  
Long Zheng ◽  
Brian Heilig ◽  
Guang R Gao
Keyword(s):  
Big Data ◽  
Memory Management ◽  
High Performance ◽  
Cluster Computing ◽  
Programming Model ◽  
Distributed Processing ◽  
Large Data ◽  
Computing System ◽  
Fine Grain ◽  
Execution Model

As the attention given to big data grows, cluster computing systems for distributed processing of large data sets become the mainstream and critical requirement in high performance distributed system research. One of the most successful systems is Hadoop, which uses MapReduce as a programming/execution model and takes disks as intermedia to process huge volumes of data. Spark, as an in-memory computing engine, can solve the iterative and interactive problems more efficiently. However, currently it is a consensus that they are not the final solutions to big data due to a MapReduce-like programming model, synchronous execution model and the constraint that only supports batch processing, and so on. A new solution, especially, a fundamental evolution is needed to bring big data solutions into a new era. In this paper, we introduce a new cluster computing system called HAMR which supports both batch and streaming processing. To achieve better performance, HAMR integrates high performance computing approaches, i.e. dataflow fundamental into a big data solution. With more specifications, HAMR is fully designed based on in-memory computing to reduce the unnecessary disk access overhead; task scheduling and memory management are in fine-grain manner to explore more parallelism; asynchronous execution improves efficiency of computation resource usage, and also makes workload balance across the whole cluster better. The experimental results show that HAMR can outperform Hadoop MapReduce and Spark by up to 19x and 7x respectively, in the same cluster environment. Furthermore, HAMR can handle scaling data size well beyond the capabilities of Spark.

High-Density Computing: Efficient Versus Conventional Design

2015 ◽  
Author(s):  
Dan Comperchio ◽  
Sameer Behere
Keyword(s):  
High Performance ◽  
Data Centers ◽  
Energy Savings ◽  
Large Data ◽  
Resource Planning ◽  
Efficient Design ◽  
Efficient Resource ◽  
Enterprise Level ◽  
Using Data

Data centers are expensive to build and operate. Large data centers cost $9–13/W to build [1] and can consume more than forty times, and up to over two hundred times, the amount of energy and resources consumed by a typical building [2], [3]. Therefore, space and energy considerations need to be accounted for when evaluating competing designs for high-performance computing (HPC) installations. This paper describes the results of an incremental cost and energy savings analysis conducted using data collected from a real-world case study to evaluate the impacts of efficient resource planning and implementing a total cost of ownership (TCO) model in the analysis of IT equipment and systems. The analysis presented demonstrates the advantages of using the latest technologies and IT strategies when planning the growth of new HPC installations at an enterprise level. The data also indicates an efficient design can significantly reduce the space, power, and cooling requirements of the HPC deployment while maintaining the performance and reliability criteria.

scholarly journals A Hybrid-parallel Architecture for Applications in Bioinformatics

2017 ◽  
Author(s):  
Jan Christian Kässens
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Data Centers ◽  
Association Studies ◽  
Parallel Architecture ◽  
Large Data ◽  
Scientific Data ◽  
Genome Wide Association Studies ◽  
Application Development ◽  
Concurrent Software

Since the advent of Next Generation Sequencing (NGS) technology, the amount of data from whole genome sequencing has been rising fast. In turn, the availability of these resources led to the tapping of whole new research fields in molecular and cellular biology, producing even more data. On the other hand, the available computational power is only increasing linearly. In recent years though, special-purpose high-performance devices started to become prevalent in today’s scientific data centers, namely graphics processing units (GPUs) and, to a lesser extent, field-programmable gate arrays (FPGAs). Driven by the need for performance, developers started porting regular applications to GPU frameworks and FPGA configurations to exploit the special operations only these devices may perform in a timely manner. However, applications using both accelerator technologies are still rare. Major challenges in joint GPU/FPGA application development include the required deep knowledge of associated programming paradigms and the efficient communication both types of devices. In this work, two algorithms from bioinformatics are implemented on a custom hybrid-parallel hardware architecture and a highly concurrent software platform. It is shown that such a solution is not only possible to develop but also its ability to outperform implementations on similar- sized GPU or FPGA clusters in terms of both performance and energy consumption. Both algorithms analyze case/control data from genome- wide association studies to find interactions between two or three genes with different methods. Especially in the latter case, the newly available calculation power and method enables analyses of large data sets for the first time without occupying whole data centers for weeks. The success of the hybrid-parallel architecture proposal led to the development of a high- end array of FPGA/GPU accelerator pairs to provide even better runtimes and more possibilities.

scholarly journals A Hybrid-parallel Architecture for Applications in Bioinformatics

2017 ◽  
Author(s):  
Jan Christian Kässens
Keyword(s):  
Graphics Processing Units ◽  
High Performance ◽  
Data Centers ◽  
Association Studies ◽  
Parallel Architecture ◽  
Large Data ◽  
Scientific Data ◽  
Genome Wide Association Studies ◽  
Application Development ◽  
Concurrent Software

Since the advent of Next Generation Sequencing (NGS) technology, the amount of data from whole genome sequencing has been rising fast. In turn, the availability of these resources led to the tapping of whole new research fields in molecular and cellular biology, producing even more data. On the other hand, the available computational power is only increasing linearly. In recent years though, special-purpose high-performance devices started to become prevalent in today’s scientific data centers, namely graphics processing units (GPUs) and, to a lesser extent, field-programmable gate arrays (FPGAs). Driven by the need for performance, developers started porting regular applications to GPU frameworks and FPGA configurations to exploit the special operations only these devices may perform in a timely manner. However, applications using both accelerator technologies are still rare. Major challenges in joint GPU/FPGA application development include the required deep knowledge of associated programming paradigms and the efficient communication both types of devices. In this work, two algorithms from bioinformatics are implemented on a custom hybrid-parallel hardware architecture and a highly concurrent software platform. It is shown that such a solution is not only possible to develop but also its ability to outperform implementations on similar- sized GPU or FPGA clusters in terms of both performance and energy consumption. Both algorithms analyze case/control data from genome- wide association studies to find interactions between two or three genes with different methods. Especially in the latter case, the newly available calculation power and method enables analyses of large data sets for the first time without occupying whole data centers for weeks. The success of the hybrid-parallel architecture proposal led to the development of a high- end array of FPGA/GPU accelerator pairs to provide even better runtimes and more possibilities.

Synthezis the strucrure of cloud computing system to control of mobile robots group

2016 ◽  
Vol 11 (1) ◽  
pp. 72-80
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Cloud Computing ◽  
Mobile Robots ◽  
Distinctive Feature ◽  
Information Exchange ◽  
High Performance ◽  
Computing System ◽  
Control Information ◽  
Cloud Computing System ◽  
Group Control ◽  
Reliability And Robustness

In article one of possible approaches to synthezis of group control of mobile robots which is based on use of cloud computing is considered. Distinctive feature of the offered techniques is adequate reflection of specifics of a scope and the robots of tasks solved by group in architecture of control-information systems, methods of the organization of information exchange, etc. The approach offered by authors allows to increase reliability and robustness of collectives of robots, to lower requirements to airborne computers when saving summary high performance in general.

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