Yosuke Himura

Discovering configuration templates of virtualized tenant networks in multi-tenancy datacenters via graph-mining

Multi-tenant datacenter networking, with which multiple customer (tenant) networks are virtualized over a single shared physical infrastructure, is cost-effective but poses significant costs on manual configuration. Such tasks would be alleviated with configuration templates, whereas a crucial difficulty stems from creating appropriate (i.e., reusable) ones. In this work, we propose a graph-based method of mining configurations of existing tenants to extract their recurrent patterns that would be used as reusable templates for upcoming tenants. The effectiveness of the proposed method is demonstrated with actual configuration files obtained from a business datacenter network.

Discovering Cloud Operation History through Log Analysis

Management costs in private clouds will be promisingly reduced by reviewing ‘operation history,’ which is defined as a holistic view of past operation executions. Operation history provides insights into breakdown of operations: the breakdown clarifies cost-dominant operations to be improved and repetitive ones to be automated. Towards obtaining the operation history, a conventional approach relying on manual investigation is time-consuming, and another relying on agent-based monitoring is not often acceptable in sensitive mission-critical enterprise clouds. Different from these approaches, our idea is to discover the operation history by automatically analyzing ‘system logs’ that are easily accessible even in sensitive clouds. Since system logs contain only low-level debugging messages about programmatic events without direct contexts about operations, the challenge is to recover high-level operational contexts from low-level system logs. To address this challenge, we develop a method that first abstracts system logs using a pre-defined event sequence model, and then maps the abstracted events to high-level individual operations—this mapping between different contextual levels is achieved by using complementary cross-cloud reference data. Evaluation of an implementation revealed that this method reduces the time taken to discover the history by 99.9% compared to a conventional approach while achieving up to 95% correctness.

Evidence-based context-aware log data management for integrated monitoring system

Integrated monitoring system, enabled with semi-structured datastore, is a promising solution for monitoring SaaS systems. However, according to increasing scale of SaaS systems and their long-term of service operations, the monitoring system has faced the problem in response times of log analysis and storage consumption. Our empirical observation is that the problem is primarily derived from the unselective log processing of semi-structure datastore, whereas there should be heterogeneities in log data that we can take advantage of for efficient log management. Based on this observation, we first attest this insight by investigating the usage patterns of log data in a quantitative manner with an actual dataset of log access histories obtained from a SaaS system serving to enterprise users, and we show that there are heterogeneities in required retention period of logs, response time, and amount of data, depending on log data category and its analysis scenario. Armed with the evidence found from the investigation, we design a methodology of context-aware log management, key features of which are to speculatively pre-cache the log analysis and to proactively archive ones depending on log data category and analysis scenario. Evaluation with a prototype implementation shows that the proposed method reduces the response time and the storage consumption.