An economic market for the brokering of time and budget guarantees

Grids offer best effort services to users. Service level agreements offer the opportunity to provide guarantees upon services offered, in such a way that it captures the users’ requirements, while also considering concerns of the service providers. This is achieved via a process of converging requirements and service cost values from both sides towards an agreement. This paper presents the intelligent scheduling for quality of service market-oriented mechanism for brokering guarantees upon completion time and cost for jobs submitted to a batch-oriented compute service. Web Services agreement (negotiation) is used along with the planning of schedules in determining pricing, ensuring that jobs become prioritised depending on their budget constraints. An evaluation is performed to demonstrate how market mechanisms can be used to achieve this, whilst also showing the effects that scheduling algorithms can have upon the market in terms of rescheduling. The evaluation is completed with a comparison of the broker’s capabilities in relation to the literature

A grid broker pricing mechanism for temporal and budget guarantees

We introduce a pricing mechanism for Grid computing, with the aim of showing how a broker can accept the most appropriate jobs to be computed on time and on budget. We analyse the mechanism’s performance via discrete event simulation, and illustrate its viability, the benefits of a new admission policy and to how slack relates to machine heterogeneity

Resource discovery for distributed computing systems: A comprehensive survey

Large-scale distributed computing environments provide a vast amount of heterogeneous computing resources from different sources for resource sharing and distributed computing. Discovering appropriate resources in such environments is a challenge which involves several different subjects. In this paper, we provide an investigation on the current state of resource discovery protocols, mechanisms, and platforms for large-scale distributed environments, focusing on the design aspects. We classify all related aspects, general steps, and requirements to construct a novel resource discovery solution in three categories consisting of structures, methods, and issues. Accordingly, we review the literature, analyzing various aspects for each category

Effects of active commuting to work for 12 months on cardiovascular risk factors and body composition

Active commuting has the potential to decrease cardiovascular risk by increasing physical activity. We aimed to investigate the effects of active commuting to work for 12 months on body composition and cardiovascular risk factors. Therefore, 73 hospital employees (age: 46 ± 9 years, 36% males), with a predominantly passive way of commuting, were randomly assigned to an intervention group (IG) and a control group (CG) in a 2:1 fashion. The IG was further divided into a public transportation plus active commuting group (IG-PT) and a cycling group (IG-C). Both IGs were prompted to reach 150 min/wk of moderate intensity exercise. Daily self-reported commuting details were verified by GPS tracking. All subjects underwent assessment of body composition, resting blood pressure, glycemic control, and lipid profile at the beginning and end of the study. Data for final analyses were available in 62 subjects. Commuting details indicated that the subjects randomized to IG changed their commuting habits. HbA1c decreased by 0.2% [95%CI: -0.3, -0.2] in IG-PT but was not statistically different between groups (P = .06). LDL cholesterol decreased in IG-C by 0.8 mmol/L [-1.1, -0.4] and by 0.6 mmol/L [-1.2, 0.1] in IG-PT which can be considered biologically relevant but did not yield statistical significance. Body composition and blood pressure did not differ between groups. Active commuting to work for 12 months did not change body composition but yielded relevant changes in lipid profile and glycemic control. Health benefits of active commuting should be addressed by healthcare professionals when counseling individuals that seek to improve their cardiovascular risk profile

Health effects of active commuting to work: The available evidence before GISMO

Sedentary lifestyle is a major modifiable risk factor for many chronic diseases. Global guidelines recommend for maintaining health in adults, at least 150 minutes of moderate intensity of physical activity throughout the week, but compliance is insufficient and health problems arise. One obvious way to overcome this is to integrate physical activity into the daily routine for example by active commuting to work. Scientific evidence, however, is scarce and therefore we set out to perform this systematic review of the available literature to improve understanding of the efficiency of active commuting initiatives on health. Literature searches were performed in PubMed and Cochrane database. Altogether, 37 studies were screened. Thereof, eight publications were reviewed, which included 555 participants. The mean study duration of the reviewed research was 36 ± 26 (8-72) weeks. Overall, active commuting in previously untrained subjects of both sexes significantly improved exercise capacity, maximal power, blood pressure, lipid parameters including cholesterol, high-density lipoprotein, and waist circumference. Improvement was independent of the type of active commuting. Despite relatively few studies that were previously performed, this review revealed that active commuting has health beneficial effects comparable to those of moderate exercise training

Effects of active commuting on cardiovascular risk factors: GISMO-a randomized controlled feasibility study

A sedentary lifestyle is a major modifiable risk factor for many chronic diseases. Lifestyle modification in order to increase exercise capacity is key in the prevention and rehabilitation of chronic diseases. This could be achieved by active commute. The aim of this study was to assess the effects of daily active commuting on physical activity (PA) and exercise capacity. Seventy-three healthy hospital employees (age: 46 ± 9 years, 38% male), with a predominantly passive way of commuting, were randomly assigned to two parallel groups, a control group (CG, N = 22) or an intervention group (IG, N = 51), which was further split into public transportation/active commuting (IG-PT, N = 25) and cycling (IG-C, N = 26). Both intervention groups were asked to reach 150 min/wk of moderate- to vigorous-intensity exercise during their commute for 1 year. CG maintained a passive commuting mode. All participants underwent assessment of anthropometry, risk factor stratification, and exercise capacity by a medical doctor at the Institute of Sports Medicine, Prevention and Rehabilitation. Weekly physical activity, using the International Physical Activity Questionnaire and commuting behavior, using an online diary, were used to assess physical activity. At the end of the study, the change in exercise capacity did significantly differ between IG and CG (P = .003, ES = 0.82). Actively covered distances through commuting significantly differed between groups (walking P = .026; cycling P < .001). Therefore, active commuting improves exercise capacity and can be recommended to the working population to increase exercise capacity

Merging self-reported with technically sensed data for tracking mobility behavior in a naturalistic intervention study. Insights from the GISMO study

Sound exposure data are central for any intervention study. In the case of utilitarian mobility, where studies cannot be conducted in controlled environments, exposure data are commonly self-reported. For short-term intervention studies, wearable devices with location sensors are increasingly employed. We aimed to combine self-reported and technically sensed mobility data, in order to provide more accurate and reliable exposure data for GISMO, a long-term intervention study. Through spatio-temporal data matching procedures, we are able to determine the amount of mobility for all modes at the best possible accuracy level. Self-reported data deviate ±10% from the corrected reference. Derived modal split statistics prove high compliance to the respective recommendations for the control group (CG) and the two intervention groups (IG-PT, IG-C). About 73.7% of total mileage was travelled by car in CG. This share was 10.3% (IG-PT) and 9.7% (IG-C), respectively, in the intervention groups. Commuting distances were comparable in CG and IG, but annual mean travel times differ between  = 8,458 min (σ = 6,427 min) for IG-PT,  = 8,444 min (σ = 5,961 min) for IG-C, and  = 5,223 min (σ = 5,463 min) for CG. Seasonal variabilities of modal split statistics were observable. However, in IG-PT and IG-C no shift toward the car occurred during winter months. Although no perfect single-method solution for acquiring exposure data in mobility-related, naturalistic intervention studies exists, we achieved substantially improved results by combining two data sources, based on spatio-temporal matching procedures

Dose-response relationship of active commuting to work: Results of the GISMO study

The positive health benefits of regular exercise, particularly regarding cardiovascular risk and diseases, are well recognized and scientifically evident. However, a sedentary lifestyle is one of the most important cardiovascular risk factors that are still insufficiently addressed. Leisure-time active commuting like walking and biking is an ideal way to improve exercise behavior in the general population. The purpose of this substudy of the GISMO study was to assess dose-response relations in all commuters and the three subgroups of commuters (physically active by bicycle and/or walking, physically active by using public transportation (PT), and the controls using their own vehicles). As such, a positive dose-response relationship could be confirmed in all physically active commuters compared to the control group. Whether the commuters cycled, walked, or traveled by PT -the more the physical exercise they performed (measured in metabolic equivalent [MET]-hours), the larger their gain in physical fitness (measured in gained or "Delta" Watt during a maximal exercise test), and their physical fitness at the end of the study was P = .016 and P = .003, respectively. Health-related quality of life correlated in two out of eight subdomains of the SF-36 questionnaire with MET-hours achieved during the study period (General Health and Physical Functioning). No clearly significant dose-response could be observed regarding HDL(high-density lipoprotein)-cholesterol or body composition. Our results indicate a dose-response pattern of healthy commuting in exercise capacity and health-related quality of life to increase doses of physically active commuting