An analysis of software aging in cloud environment

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

Proactive software rejuvenation solution for web enviroments on virtualized platforms

The availability of the Information Technologies for everything, from everywhere, at all times is a growing requirement. We use information Technologies from common and social tasks to critical tasks like managing nuclear power plants or even the International Space Station (ISS). However, the availability of IT infrastructures is still a huge challenge nowadays. In a quick look around news, we can find reports of corporate outage, affecting millions of users and impacting on the revenue and image of the companies. It is well known that, currently, computer system outages are more often due to software faults, than hardware faults. Several studies have reported that one of the causes of unplanned software outages is the software aging phenomenon. This term refers to the accumulation of errors, usually causing resource contention, during long running application executions, like web applications, which normally cause applications/systems to hang or crash. Gradual performance degradation could also accompany software aging phenomena. The software aging phenomena are often related to memory bloating/ leaks, unterminated threads, data corruption, unreleased file-locks or overruns. We can find several examples of software aging in the industry. The work presented in this thesis aims to offer a proactive and predictive software rejuvenation solution for Internet Services against software aging caused by resource exhaustion. To this end, we first present a threshold based proactive rejuvenation to avoid the consequences of software aging. This first approach has some limitations, but the most important of them it is the need to know a priori the resource or resources involved in the crash and the critical condition values. Moreover, we need some expertise to fix the threshold value to trigger the rejuvenation action. Due to these limitations, we have evaluated the use of Machine Learning to overcome the weaknesses of our first approach to obtain a proactive and predictive solution. Finally, the current and increasing tendency to use virtualization technologies to improve the resource utilization has made traditional data centers turn into virtualized data centers or platforms. We have used a Mathematical Programming approach to virtual machine allocation and migration to optimize the resources, accepting as many services as possible on the platform while at the same time, guaranteeing the availability (via our software rejuvenation proposal) of the services deployed against the software aging phenomena. The thesis is supported by an exhaustive experimental evaluation that proves the effectiveness and feasibility of our proposals for current systems

Novel individualized power training protocol preserves physical function in adult and older mice

Sarcopenia, the age-related loss of muscle mass and strength, contributes to frailty, functional decline, and reduced quality of life in older adults. Exercise is a recognized therapy for sarcopenia and muscle dysfunction, though not a cure. Muscle power declines at an increased rate compared to force, and force output declines earlier than mass. Thus, there is a need for research of exercise focusing on improving power output and functionality in older adults. Our primary purpose was proof-of-concept that a novel individualized power exercise modality would induce positive adaptations in adult mice, before the exercise program was applied to an aged cohort. We hypothesized that after following our protocol, both adult and older mice would show improved function, though there would be evidence of anabolic resistance in the older mice. Male C57BL/6 mice (12 months of age at study conclusion) were randomized into control (n = 9) and exercise (n = 6) groups. The trained group used progressive resistance (with a weighted harness) and intensity (~ 4-10 rpm) on a custom motorized running wheel. The mice trained similarly to a human workout regimen (4-5 sets/session, 3 sessions/week, for 12 weeks). We determined significant (p < 0.05) positive adaptations post-intervention, including: neuromuscular function (rotarod), strength/endurance (inverted cling grip test), training physiology (force/power output per session), muscle size (soleus mass), and power/velocity of contraction (in vitro physiology). Secondly, we trained a cohort of older male mice (28 months old at conclusion): control (n = 12) and exercised (n = 8). While the older exercised mice did preserve function and gain benefits, they also demonstrated evidence of anabolic resistance.F31 AG044108 - NIA NIH HHS; R01 AG017768 - NIA NIH HHS; TL1 TR001440 - Institute for Translational Sciences, University of Texas Medical BranchAccepted manuscrip

Online failure prediction in air traffic control systems

This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures

Online failure prediction in air traffic control systems

This thesis introduces a novel approach to online failure prediction for mission critical distributed systems that has the distinctive features to be black-box, non-intrusive and online. The approach combines Complex Event Processing (CEP) and Hidden Markov Models (HMM) so as to analyze symptoms of failures that might occur in the form of anomalous conditions of performance metrics identified for such purpose. The thesis presents an architecture named CASPER, based on CEP and HMM, that relies on sniffed information from the communication network of a mission critical system, only, for predicting anomalies that can lead to software failures. An instance of Casper has been implemented, trained and tuned to monitor a real Air Traffic Control (ATC) system developed by Selex ES, a Finmeccanica Company. An extensive experimental evaluation of CASPER is presented. The obtained results show (i) a very low percentage of false positives over both normal and under stress conditions, and (ii) a sufficiently high failure prediction time that allows the system to apply appropriate recovery procedures

The investigation of treatment outcomes for adults with chronic brain injury following intensive multidisciplinary treatment

Although communication sciences and disorders (COMD) research supports intensive treatment for individuals with chronic brain injury, funding to provide these services is limited. This study explored the use of interdepartmental university resources to provide an intensive, multidisciplinary chronic brain injury program. Methodologically, treatment regimes were designed with clinical faculty as practicum experiences for COMD master’s degree students. Subjects with a single onset head injury or cerebral vascular accident greater than one year were recruited to participate in the Intensive Treatment, Weekly Treatment or Control Groups. Pre, Post, and Post-Post Testing were used to measure cognitive-linguistic, quality of life and physical function. Additionally, treatment groups participated in electronic Experience Sampling Method (ESM) probes which queried their perception of happiness, tiredness, stress, and communication satisfaction throughout treatment using a Palm Zire 31 Personal Data Assistant. Both treatment programs were contextually-oriented, stressing functional multi-modality communication and compensatory techniques. Three hours of small and medium group COMD treatment were administered to the Weekly Group once weekly. The six subjects in the Intensive Group received a 35 hour weekly program including: COMD (12 hours), modified Tai Chi (3 hours), and psychological support for them and caregivers (4 ¼ hours). One-way repeated measures analysis of variance with partial eta squared effect size was used to analyze measures in the standardized battery. Intensive Group cognitive linguistic function appeared to significantly differ from the other groups on the Communication Activities of Daily Living-2 and Aphasia Diagnostic Profile Writing subtest suggestive of functional communication gains. Results of the ESM probes indicate that the Intensive Group was reportedly more happy and satisfied with their communication than the Weekly Group. The participants of the Intensive Group appeared to physically benefit from 3 weeks of modified Tai Chi in rate of ambulation. Limitations of the study, including self selection of treatment condition and differing severity across treatment groups, must be addressed by expanding the subject pool in follow-up research

Retention and Motivation of Veteran Teachers.

The workforce in the United States is aging. Teachers, like all other workers in the country, are also aging. The veteran teachers in our nation\u27s public schools possess wisdom gained through their on-the-job experience. With looming teacher shortages in our public schools, it is imperative that we retain this wisdom. Administrators, school boards, and the community have an obligation to tap this wisdom for the benefit of children. This study was conducted, therefore, with the purpose of learning how to do that. The data were collected through the process of one-on-one interviews with 21 veteran teachers in Knox County, Tennessee. This was done to make use of the knowledge gained by actual working professionals. The findings of this study were that veteran teachers did have a great deal of wisdom to share. The 21 interviewed teachers gave information that allowed the researcher to compile recommendations as to how administrators and other interested parties could retain and motivate all teachers, whether veteran or novice. The results of this study might prove useful to anyone interested in retaining teachers in our nation\u27s schools. This knowledge could benefit the education of our nation\u27s most valuable resource--our children

Building safety in Hong Kong uilding safety in Hong Kong

Thesis (B.Sc)--University of Hong Kong, 2006.published_or_final_versio

China: Surpassing the “Middle Income Trap”

This open access book explores one of the most fiercely debated issues in China: if and how China will surpass the middle income trap that has plagued many developing countries for years. This book gives readers a clear picture of China today and acts as a reference for other developing countries. China is facing many setbacks and experiencing an economic slowdown in recent years due to some serious issues, and income inequality is one such issue deferring China’s development potential by creating a middle income trap. This book thoroughly investigates both the unpromising factors and favorable conditions for China to overcome the trap. It illustrates that traps may be encountered at any stage of development and argues that political stability is the prerequisite to creating a favorable environment for economic development and addressing this “middle income trap”. Written by one of China's central planners, this book offers precious insights into the industrial policies that are transforming China and the world and will be of interest to China scholars, economists and political scientists