Empirical Validation of Cyber-Foraging Architectural Tactics for Surrogate Provisioning

Background Cyber-foraging architectural tactics are used to build mobile applications that leverage proximate, intermediate cloud surrogates for computation offload and data staging. Compared to direct access to cloud resources, the use of intermediate surrogates improves system qualities such as response time, energy efficiency, and resilience. However, the state-of-the-art mostly focuses on introducing new architectural tactics rather than quantitatively comparing the existing tactics, which can help software architects and software engineers with new insights on each tactic. Aim Our work aims at empirically evaluating the architectural tactics for surrogate provisioning, specifically with respect to resilience and energy efficiency. Method We follow a systematic experimentation framework to collect relevant data on Static Surrogate Provisioning and Dynamic Surrogate Provisioning tactics. Our experimentation approach can be reused for validation of other cyber-foraging tactics. We perform statistical analysis to support our hypotheses, as compared to baseline measurements with no cyber-foraging tactics deployed. Results Our findings show that Static Surrogate Provisioning tactics provide higher resilience than Dynamic Surrogate Provisioning tactics for runtime environmental changes. Both surrogate provisioning tactics perform with no significant difference with respect to their energy efficiency. We observe that the overhead of the runtime optimization algorithm is similar for both tactic types. Conclusions The presented quantitative evidence on the impact of different tactics empowers software architects and software engineers with the ability to make more conscious design decisions. This contribution, as a starting point, emphasizes the use of quantifiable metrics to make better-informed trade-offs between desired quality attributes. Our next step is to focus on the impact of runtime programmable infrastructure on the quality of cyber-foraging systems

Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

This chapter presents the authors’ work for the Case Study entitled “Delivering Social Media with Scalability” within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with potential solutions addressing the problems of the area and described how these fit in the general ecosystem

The estimation of survival function for colon cancer data in Tehran using non-parametric Bayesian model

Background: Colon cancer is the third cause of cancer deaths. Although colon cancer survival time has increased in recent years, the mortality rate is still high. The Cox model is the most common regression model often used in medical research in survival analysis, but most of the time the effect of at least one of the independent factors changes over time, so the model cannot be used. In the current study, the survival function for colon cancer patients in Tehran is estimated using non-parametric Bayesian model. Methods: In this survival study, 580 patients with colon cancer who were recorded in the Cancer Research Center of Shahid Beheshti University of Medical Sciences since April 2005 to November 2006 were studied and followed up for a period of 5 years. Survival function was plotted with nonparametric Bayesian model and was compared with the Kaplan-Meier curve. Results: Of the total of 580 patients, 69.9 of patients were alive. 45.9 of patients were male and the mean age of cancer diagnosis was 65.12 (SD= 12.26) and 87.7 of the patients underwent surgery. There was a significant relationship between age at diagnosis and sex and the survival time while there was a non-significant relationship between the type of treatment and the survival time. The survival functions corresponding to the two treatment groups cross, in comparison with the patients who had no surgery in the first 30 months, showed a higher level of risk in the patients who underwent a surgery. After that, the survival probability for the patients undergoing a surgery has increased. Conclusion: The study showed that survival rate has been higher in women and in the patients who were below 60 years at the time of diagnosis

Amyotrophic lateral sclerosis progression: Iran-ALS clinical registry, a multicentre study

This study was designed to evaluate ALS progression among different subgroups of Iranian patients. Three hundred and fifty-eight patients from centres around the country were registered and their progression rate was evaluated using several scores including Manual Muscle Test scoring (MMT) and the revised ALS Functional Rating Scale (ALSFRS-R). Progression rate was analysed separately in subgroups regarding gender, onset site, stage of disease and riluzole consumption. A significant difference in MMT deterioration rate (p = 0.01) was noted between those who used riluzole and those who did not. No significant difference was observed in progression rates between male/female and bulbar-onset/limb-onset groups using riluzole. In conclusion, riluzole has a significant effect on muscle force deterioration rate but not functional scale. Progression rate was not influenced by site of onset or gender