scholarly journals JIST: Just-In-Time Scheduling Translation for Parallel Processors

2005 ◽  
Vol 13 (3) ◽  
pp. 239-253 ◽  
Author(s):  
Giovanni Agosta ◽  
Stefano Crespi Reghizzi ◽  
Gerlando Falauto ◽  
Martino Sykora
Keyword(s):  
Virtual Machines ◽  
Instruction Scheduling ◽  
Mobile Systems ◽  
Just In Time ◽  
Vliw Processors ◽  
Java Bytecode ◽  
Time Scheduling ◽  
Application Fields ◽  
The Impact ◽  
Vliw Architectures

The application fields of bytecode virtual machines and VLIW processors overlap in the area of embedded and mobile systems, where the two technologies offer different benefits, namely high code portability, low power consumption and reduced hardware cost. Dynamic compilation makes it possible to bridge the gap between the two technologies, but special attention must be paid to software instruction scheduling, a must for the VLIW architectures. We have implemented JIST, a Virtual Machine and JIT compiler for Java Bytecode targeted to a VLIW processor. We show the impact of various optimizations on the performance of code compiled with JIST through the experimental study on a set of benchmark programs. We report significant speedups, and increments in the number of instructions issued per cycle up to 50% with respect to the non-scheduling version of the JITcompiler. Further optimizations are discussed.

The Impact of Just-in-Time Manufacturing and Its Infrastructure on Manufacturing Performance

1997 ◽  
Vol 43 (9) ◽  
pp. 1246-1257 ◽  
Author(s):  
Sadao Sakakibara ◽  
Barbara B. Flynn ◽  
Roger G. Schroeder ◽  
William T. Morris
Keyword(s):  
Just In Time ◽  
Manufacturing Performance ◽  
The Impact

scholarly journals Key facets to build up eHealth and mHealth interventions to enhance physical activity, sedentary behavior and nutrition in healthy subjects – An umbrella review

2020 ◽  
Author(s):  
Janis Fiedler ◽  
Tobias Eckert ◽  
Kathrin Wunsch ◽  
Alexander Woll
Keyword(s):  
Physical Activity ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Healthy Eating ◽  
Social Contexts ◽  
Just In Time ◽  
Umbrella Review ◽  
Behavior Change Techniques ◽  
Adaptive Interventions ◽  
The Impact

Abstract Background: Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology.Methods: PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized.Results: The systematic search reveled eleven systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions.Conclusion: Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions.Trial registration: The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902.

scholarly journals Virtual Disks Performance Analysis Using Flexible I/O and Powerstat

2021 ◽  
Vol 9 (10) ◽  
pp. 267-296
Author(s):  
Pritam Patange
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Disk File ◽  
Virtual Machine Monitor ◽  
Image File ◽  
File Formats ◽  
Physical Infrastructure ◽  
Disk Image ◽  
The Impact

Abstract: Cloud computing has experienced significant growth in the recent years owing to the various advantages it provides such as 24/7 availability, quick provisioning of resources, easy scalability to name a few. Virtualization is the backbone of cloud computing. Virtual Machines (VMs) are created and executed by a software called Virtual Machine Monitor (VMM) or the hypervisor. It separates compute environments from the actual physical infrastructure. A disk image file representing a single virtual machine is created on the hypervisor’s file system. In this paper, we analysed the runtime performance of multiple different disk image file formats. The analysis comprises of four different parameters of performance namely- bandwidth, latency, input-output operations performed per second (IOPS) and power consumption. The impact of the hypervisor’s block and file sizes is also analysed for the different file formats. The paper aims to act as a reference for the reader in choosing the most appropriate disk file image format for their use case based on the performance comparisons made between different disk image file formats on two different hypervisors – KVM and VirtualBox. Keywords: Virtualization, Virtual disk formats, Cloud computing, fio, KVM, virt-manager, powerstat, VirtualBox.

scholarly journals An analysis of software aging in cloud environment

2020 ◽  
Vol 10 (6) ◽  
pp. 5985
Author(s):  
Shruthi P. ◽  
Nagaraj G. Cholli
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Service ◽  
Software Systems ◽  
System Failure ◽  
Dynamic Nature ◽  
Software Aging ◽  
Software Rejuvenation ◽  
The Impact

Cloud Computing is the environment in which several virtual machines (VM) run concurrently on physical machines. The cloud computing infrastructure hosts multiple cloud service segments that communicate with each other using the interfaces. This creates distributed computing environment. During operation, the software systems accumulate errors or garbage that leads to system failure and other hazardous consequences. This status is called software aging. Software aging happens because of memory fragmentation, resource consumption in large scale and accumulation of numerical error. Software aging degrads the performance that may result in system failure. This happens because of premature resource exhaustion. This issue cannot be determined during software testing phase because of the dynamic nature of operation. The errors that cause software aging are of special types. These errors do not disturb the software functionality but target the response time and its environment. This issue is to be resolved only during run time as it occurs because of the dynamic nature of the problem. To alleviate the impact of software aging, software rejuvenation technique is being used. Rejuvenation process reboots the system or re-initiates the softwares. This avoids faults or failure. Software rejuvenation removes accumulated error conditions, frees up deadlocks and defragments operating system resources like memory. Hence, it avoids future failures of system that may happen due to software aging. As service availability is crucial, software rejuvenation is to be carried out at defined schedules without disrupting the service. The presence of Software rejuvenation techniques can make software systems more trustworthy. Software designers are using this concept to improve the quality and reliability of the software. Software aging and rejuvenation has generated a lot of research interest in recent years. This work reviews some of the research works related to detection of software aging and identifies research gaps.

The impact of big data on innovation and value generation in pharmaceutical sales and marketing

2021 ◽  
Vol 3 (2) ◽  
pp. 37-52
Author(s):  
Antonio Pesqueira
Keyword(s):  
Big Data ◽  
New Technology ◽  
Unmet Need ◽  
Pharmaceutical Companies ◽  
Future Trends ◽  
Pharmaceutical Sales ◽  
Sales And Marketing ◽  
Investment Opportunities ◽  
Application Fields ◽  
The Impact

Using Big Data in the pharmaceutical industry is a relatively new technology, and the benefits and applications are yet to be understood. There are some cases currently being piloted, but others have already been adopted by some pharmaceutical organizations, proving the unmet need in a field that is still in its infancy. This paper aims to understand how and if Big Data can contribute to commercial innovation, as well as future trends, investment opportunities. Participants from 26 pharmaceutical companies participated in different focus groups where topics were grouped by individuals and evaluation areas were discussed to discover any potential connections between Big Data and Innovation in commercial pharmaceutical environments. This study used the collected data to analyze and draw conclusions about how many life sciences leaders and professionals already know about Big Data and are identifying examples and processes where Big data is supporting and generating innovation. In addition, we were able to understand that the industry is already comfortable with Big Data, and there were some very accurate research results regarding the most pertinent application fields and key considerations moving forward. Using the network analysis findings and the relationships and connections explained by respondents, we can reveal how Big Data and innovation are interconnected.

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