Yong Qi — Page 2 | Search arXiv

arXiv2014

Bio-inspired Mechanism and Model Exploration of Software Aging

Software systems situated in network environment may experience performance degradation, availability decrease and even crash during long time running, which is called software aging. This phenomenon has been studied for more than 15 years, but most of the literatures studied software as a black box, none of them uncovered the fundamental and widely accepted mechanism of software aging as far as we know. Through analyzing the characteristics between biological aging and software aging, we find some interesting common points and bridge the gap between these two seemingly unrelated phenomena. The free radical aging theory in biological studies is also applicative to explore the mechanism and model of software aging. This paper finds an equivalent concept named `software free radical' in software aging to free radical in biological aging. In our study, the accumulation of `software free radical' is a root cause of software aging. Using the free radical modeling methodology in biological aging, we give a model for describing the kinetic of software aging based on feedback loops. Although this paper doesn't give enough theoretical proof of the modeling method, the practical results show that the feedback loop model can describe the kinetic of software aging precisely. To further validate the aging mechanism, we propose several software rejuvenation strategies focusing on cleaning the `software free radical'. The results show that software aging can be mitigated effectively by strengthening negative feedback loop or weakening positive feedback loop. This paper is the first try to answer the question `How software ages' through interdisciplinary studies. Leveraging the conclusions in this paper, people can design better software systems or keep their systems at a high performance level during long time running.

cs.SE↗

arXiv2013

BigDataBench: a Big Data Benchmark Suite from Web Search Engines

This paper presents our joint research efforts on big data benchmarking with several industrial partners. Considering the complexity, diversity, workload churns, and rapid evolution of big data systems, we take an incremental approach in big data benchmarking. For the first step, we pay attention to search engines, which are the most important domain in Internet services in terms of the number of page views and daily visitors. However, search engine service providers treat data, applications, and web access logs as business confidentiality, which prevents us from building benchmarks. To overcome those difficulties, with several industry partners, we widely investigated the open source solutions in search engines, and obtained the permission of using anonymous Web access logs. Moreover, with two years' great efforts, we created a sematic search engine named ProfSearch (available from http://prof.ict.ac.cn). These efforts pave the path for our big data benchmark suite from search engines---BigDataBench, which is released on the web page (http://prof.ict.ac.cn/BigDataBench). We report our detailed analysis of search engine workloads, and present our benchmarking methodology. An innovative data generation methodology and tool are proposed to generate scalable volumes of big data from a small seed of real data, preserving semantics and locality of data. Also, we preliminarily report two case studies using BigDataBench for both system and architecture researches.

cs.IR↗