Proactive cloud service assurance framework for fault remediation in cloud environment

Abstract

Cloud resiliency is an important issue in successful implementation of cloud computing systems. Handling cloud faults proactively, with a suitable remediation technique having minimum cost is an important requirement for a fault management system. The selection of best applicable remediation technique is a decision making problem and considers parameters such as i) Impact of remediation technique ii) Overhead of remediation technique ii) Severity of fault and iv) Priority of the application. This manuscript proposes an analytical model to measure the effectiveness of a remediation technique for various categories of faults, further it demonstrates the implementation of an efficient fault remediation system using a rulebased expert system. The expert system is designed to compute a utility value for each remediation technique in a novel way and select the best remediation technique from its knowledgebase. A prototype is developed for experimentation purpose and the results shows improved availability with less overhead as compared to a reactive fault management system.

A systematic literature review of machine learning methods in predicting court decisions

“…The globalised world today demands speedy and efficient handling of every action [1]- [3]. The fastmoving actions are essential in ensuring that the services can be implemented in line with the rapid development of technology and information, including in the legal system [4]- [20].…”

MicroRAS: Automatic Recovery in the Absence of Historical Failure Data for Microservice Systems

“…A membership function defines how the input value is mapped to the membership degree between 0 and 1, where 0 means the input does not belong to the given fuzzy set, and 1 means the input completely belongs to it. Similar to [19], [31], the membership functions for the three fuzzy sets in inputs are respectively a R-function, a trapezoidal function and a L-Functions, as shown in Figure 5(a). The membership function used in the output is three triangular functions, as shown in Figure 5(b).…”

“…Model-based approaches model different aspects of a healing process, such as the properties of the fault [43], the properties of the actions [44], or use theoretical techniques, like Markov decision theory [44]- [46]. Similar to our method, consequences of recovery actions are also considered in [18], [19], [47]. M.Fu et al [47] define the impact of an action on service response times which are caused by the increasing requests introduced by different recovery patterns.…”

“…However, the impact of a performance issue in microservice systems, such as hardware failure, software bugs, resource contention, etc, cannot manifest in the number of requests, so their impact model is not suitable to our problem. Others [18], [19] define their models based on probabilistic parameters learned from recovery history (e.g., the prior probability of the system being in a stable state after executing an action). However, these probabilistic parameters are difficult to obtain in frequently updated microservice systems.…”

A novel independent job rescheduling strategy for cloud resilience in the cloud environment

“…Although a cloud data center often has its own proactive fault handling plan, the threat of unplanned outages still exists [1][2][3][4]. Therefore, cloud resiliency is an important issue to successfully keep a stable cloud computing environment under fault scenarios [5]. To rescue a series of operations from a fault event, a variety of fault handling approaches have been proposed [6,7].…”