Self-adaptation via concurrent multi-action evaluation for unknown context

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project. Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach. The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains. The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason. The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered. In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques

Performance Tuning of Database Systems Using a Context-aware Approach

Database system performance problems have a cascading effect into all aspects of an enterprise application. Database vendors and application developers provide guidelines, best practices and even initial database settings for good performance. But database performance tuning is not a one-off task. Database administrators have to keep a constant eye on the database performance as the tuning work carried out earlier could be invalidated due to multitude of reasons. Before engaging in a performance tuning endeavor, a database administrator must prioritize which tuning tasks to carry out first. This prioritization is done based on which tuning action would yield highest performance benefit. However, this prediction may not always be accurate. Experiment-based performance tuning methodologies have been introduced as an alternative to prediction-based performance tuning approaches. Experimenting on a representative system similar to the production one allows a database administrator to accurately gauge the performance gain for a particular tuning task. In this paper we propose a novel approach to experiment-based performance tuning with the use of a context-aware application model. Using a proof-of-concept implementation we show how it could be used to automate the detection of performance changes, experiment creation and evaluate the performance tuning outcomes for mixed workload types through database configuration parameter changes

System evolution for unknown context through multi-action evaluation

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular context change. One way is for the system developers to encompass all possible context changes in the domain knowledge. Then, the system matches a context change to that in the domain knowledge and chooses the corresponding action. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, there are situations where a system encounters unknown contexts. In such cases, instead of one action being implemented and evaluated, multiple actions could be implemented concurrently. This parallel evaluation of actions could quicken the evolution time taken to select the best action suited to unknown context compared to the iterative approach. This paper proposes a framework for context-aware systems that finds the best action for unknown context through multi-action evaluation and self-adaptation. In a case study, we show how our multi-action evaluation system can be implemented for a hypothetical hotelier who uses the name-your-own-price mechanism to sell his perishable inventory

Context-Aware Framework for Performance Tuning via Multi-action Evaluation

Context-aware systems perform adaptive changes in several ways. One way is for the system developers to encompass all possible context changes in a context-aware application and embed them into the system. However, this may not suit situations where the system encounters unknown contexts. In such cases, system inferences and adaptive learning are used whereby the system executes one action and evaluates the outcome to self-adapts/self-learns based on that. Unfortunately, this iterative approach is time-consuming if high number of actions needs to be evaluated. By contrast, our framework for context-aware systems finds the best action for unknown context through concurrent multi-action evaluation and self-adaptation which reduces significantly the evolution time in comparison to the iterative approach. In our implementation we show how the context-aware multi-action system can be used for a context-aware evaluation for database performance tuning

Intratumoral Hydrogen Peroxide With Radiation Therapy in Locally Advanced Breast Cancer: Results From a Phase 1 Clinical Trial.

Purpose Hydrogen peroxide (H2O2) plays a vital role in normal cellular processes but at supraphysiological concentrations causes oxidative stress and cytotoxicity, a property that is potentially exploitable for the treatment of cancer in combination with radiation therapy (RT). We report the first phase 1 trial testing the safety and tolerability of intratumoral H2O2 + external beam RT as a novel combination in patients with breast cancer and exploratory plasma marker analyses investigating possible mechanisms of action.Methods and materials Twelve patients with breast tumors ≥3 cm (surgically or medically inoperable) received intratumoral H2O2 with either 36 Gy in 6 twice-weekly fractions (n = 6) or 49.5 Gy in 18 daily fractions (n = 6) to the whole breast ± locoregional lymph nodes in a single-center, nonrandomized study. H2O2 was mixed in 1% sodium hyaluronate gel (final H2O2 concentration 0.5%) before administration to slow drug release and minimize local discomfort. The mixture was injected intratumorally under ultrasound guidance twice weekly 1 hour before RT. The primary endpoint was patient-reported maximum intratumoral pain intensity before and 24 hours postinjection. Secondary endpoints included grade ≥3 skin toxicity and tumor response by ultrasound. Blood samples were collected before, during, and at the end of treatment for cell-death and immune marker analysis.Results Compliance with H2O2 and RT was 100%. Five of 12 patients reported moderate pain after injection (grade 2 Common Terminology Criteria for Adverse Events v4.02) with median duration 60 minutes (interquartile range, 20-120 minutes). Skin toxicity was comparable to RT alone, with maintained partial/complete tumor response relative to baseline in 11 of 12 patients at last follow-up (median 12 months). Blood marker analysis highlighted significant associations of TRAIL, IL-1β, IL-4, and MIP-1α with tumor response.Conclusions Intratumoral H2O2 with RT is well tolerated with no additional toxicity compared with RT alone. If efficacy is confirmed in a randomized phase 2 trial, the approach has potential as a cost-effective radiation response enhancer in multiple cancer types in which locoregional control after RT alone remains poor