Energy-efficiency enhanced virtual machine deployment policy for data-intensive applications in cloud platforms

2014 ◽  
Vol 8 (4) ◽  
pp. 181
Author(s):  
Xiao Peng ◽  
Chen Runtong
Keyword(s):  
Energy Efficiency ◽  
Virtual Machine ◽  
Data Intensive ◽  
Data Intensive Applications

Two Reconfigurable NDP Servers: Understanding the Impact of Near-Data Processing on Data Center Applications

2021 ◽  
Vol 17 (4) ◽  
pp. 1-27
Author(s):  
Xiaojia Song ◽  
Tao Xie ◽  
Stephen Fischer
Keyword(s):  
Energy Efficiency ◽  
Data Processing ◽  
Data Center ◽  
Experimental Results ◽  
Full Size ◽  
Data Intensive ◽  
Processing Rate ◽  
New Findings ◽  
Data Intensive Applications ◽  
The Impact

Existing near-data processing (NDP)-powered architectures have demonstrated their strength for some data-intensive applications. Data center servers, however, have to serve not only data-intensive but also compute-intensive applications. An in-depth understanding of the impact of NDP on various data center applications is still needed. For example, can a compute-intensive application also benefit from NDP? In addition, current NDP techniques focus on maximizing the data processing rate by always utilizing all computing resources at all times. Is this “always running in full gear” strategy consistently beneficial for an application? To answer these questions, we first propose two reconfigurable NDP-powered servers called RANS ( R econfigurable A RM-based N DP S erver) and RFNS ( R econfigurable F PGA-based N DP S erver). Next, we implement a single-engine prototype for each of them based on a conventional data center and then evaluate their effectiveness. Experimental results measured from the two prototypes are then extrapolated to estimate the properties of the two full-size reconfigurable NDP servers. Finally, several new findings are presented. For example, we find that while RANS can only benefit data-intensive applications, RFNS can offer benefits for both data-intensive and compute-intensive applications. Moreover, we find that for certain applications the reconfigurability of RANS/RFNS can deliver noticeable energy efficiency without any performance degradation.

scholarly journals 6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1709
Author(s):  
Agbotiname Lucky Imoize ◽  
Oluwadara Adedeji ◽  
Nistha Tandiya ◽  
Sachin Shetty
Keyword(s):  
Wireless Communication ◽  
Psychological Health ◽  
Future Research ◽  
Agriculture Education ◽  
Social Psychological ◽  
Research Issues ◽  
Data Intensive ◽  
Wireless Communication Network ◽  
Data Intensive Applications

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

scholarly journals EZIOTracer

2021 ◽  
Vol 55 (1) ◽  
pp. 88-98
Author(s):  
Mohammed Islam Naas ◽  
François Trahay ◽  
Alexis Colin ◽  
Pierre Olivier ◽  
Stéphane Rubini ◽  
...  
Keyword(s):  
Analysis Framework ◽  
Comprehensive Understanding ◽  
Kernel Space ◽  
Data Intensive ◽  
Storage Performance ◽  
Performance Requirements ◽  
Memory Footprint ◽  
Extreme Performance ◽  
Data Intensive Applications ◽  
Kernel Level

Tracing is a popular method for evaluating, investigating, and modeling the performance of today's storage systems. Tracing has become crucial with the increase in complexity of modern storage applications/systems, that are manipulating an ever-increasing amount of data and are subject to extreme performance requirements. There exists many tracing tools focusing either on the user-level or the kernel-level, however we observe the lack of a unified tracer targeting both levels: this prevents a comprehensive understanding of modern applications' storage performance profiles. In this paper, we present EZIOTracer, a unified I/O tracer for both (Linux) kernel and user spaces, targeting data intensive applications. EZIOTracer is composed of a userland as well as a kernel space tracer, complemented with a trace analysis framework able to merge the output of the two tracers, and in particular to relate user-level events to kernel-level ones, and vice-versa. On the kernel side, EZIOTracer relies on eBPF to offer safe, low-overhead, low memory footprint, and flexible tracing capabilities. We demonstrate using FIO benchmark the ability of EZIOTracer to track down I/O performance issues by relating events recorded at both the kernel and user levels. We show that this can be achieved with a relatively low overhead that ranges from 2% to 26% depending on the I/O intensity.

Custom templates based heterogeneous resource allocation for data-intensive applications

2020 ◽  
Author(s):  
◽  
Ronny Bazan Antequera
Keyword(s):  
High Performance ◽  
Real Data ◽  
University Of Missouri ◽  
Application Performance ◽  
Data Intensive ◽  
Edge Based ◽  
The Right ◽  
Heterogeneous Cloud ◽  
Data Intensive Applications ◽  
Cloud Resources

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT REQUEST OF AUTHOR.] The increase of data-intensive applications in science and engineering fields (i.e., bioinformatics, cybermanufacturing) demand the use of high-performance computing resources. However, data-intensive applications' local resources usually present limited capacity and availability due to sizable upfront costs. Moreover, using remote public resources presents constraints at the private edge network domain. Specifically, mis-configured network policies cause bottlenecks due to the other application cross-traffic attempting to use shared networking resources. Additionally, selecting the right remote resources can be cumbersome especially for those users who are interested in the application execution considering nonfunctional requirements such as performance, security and cost. The data-intensive applications have recurrent deployments and similar infrastructure requirements that can be addressed by creating templates. In this thesis, we handle applications requirements through intelligent resource 'abstractions' coupled with 'reusable' approaches that save time and effort in deploying new cloud architectures. Specifically, we design a novel custom template middleware that can retrieve blue prints of resource configuration, technical/policy information, and benchmarks of workflow performance to facilitate repeatable/reusable resource composition. The middleware considers hybrid-recommendation methodology (Online and offline recommendation) to leverage a catalog to rapidly check custom template solution correctness before/during resource consumption. Further, it prescribes application adaptations by fostering effective social interactions during the application's scaling stages. Based on the above approach, we organize the thesis contributions under two main thrusts: (i) Custom Templates for Cloud Networking for Data-intensive Applications: This involves scheduling transit selection, engineering at the campus-edge based upon real-time policy control. Our solution ensures prioritized application performance delivery for multi-tenant traffic profiles from a diverse set of actual data intensive applications in bioinformatics. (ii) Custom Templates for Cloud Computing for Data-intensive Applications: This involves recommending cloud resources for data-intensive applications based on a custom template catalog. We develop a novel expert system approach that is implemented as a middleware to abstracts data-intensive application requirements for custom templates composition. We uniquely consider heterogeneous cloud resources selection for the deployment of cloud architectures for real data-intensive applications in cybermanufacturing.

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