A Rejuvenation Strategy of Two-Granularity Software Based on Adaptive Control

Fang, Yunyu; Yin, Beibei; Ning, Gaorong; Zhang, Zheng; Cai, Kai-Yuan

doi:10.1109/prdc.2017.23

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…The result indicates that the k-NN algorithm performs better than the decision tree. The execution result is tabulated in [20] Instead of fixed thresholds, the method used in this work regularly regulates the thresholds by taking feedback information in the running process into account. Recommended adaptive threshold for aging detection Adaptive thresholding is a part of the overall software aging prediction strategy in this research work.…”

Section: Evaluation Of the Proposed Methodsmentioning

confidence: 99%

Software aging prediction – a new approach

Parashivamurthy¹,

Cholli

2023

IJECE

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To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed.

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Section: Evaluation Of the Proposed Methodsmentioning

confidence: 99%

Software aging prediction – a new approach

Parashivamurthy¹,

Cholli

2023

IJECE

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“…Fang et al, [13] opined that, in some systems where the memory is shared between operating system and application software, as the memory consumption is closely related to system performance and changes constantly, using empirical thresholds may cause waste of resourcesor system outage. The researchers proposed an adaptive strategy adopted for optimization of the thresholds.…”

Section: Software Aging Detection Techniquesmentioning

confidence: 99%

“…Concluded that it is impossible to stop software aging but it is possible to reduce its speed and progress Y. Fang, et al, [13] a. Instead of fixed thresholds, the method used in this work regularly regulates the thresholds by taking feedback information in the running process into account.…”

Section: Platform Specificmentioning

confidence: 99%

An analysis of software aging in cloud environment

Shruthi¹,

Cholli²

2020

IJECE

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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.

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“…It can also be described as the preemptive periodic rollback of applications continuously running in order to prevent potential failures [26]. Traditional strategies for software rejuvenation are classified into time-based and inspection-based [27] . The former approach builds the rejuvenation model, establishing functions between time and cost as well as between workload and cost in an effort to specify the optimal rejuvenation scheduling.…”

Section: Introductionmentioning

confidence: 99%

A software availability model based on multilevel software rejuvenation and markov chain

Ghobadi¹,

Rashidi²

2021

Turk J Elec Eng & Comp Sci

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Increasing use of software, rapid and unavoidable changes in the operational environment bring many problems for software engineers. One of these problems is the aging and degradation of software performance. Software rejuvenation is a proactive and preventive approach to counteract software aging. Generally, when software is initiated, amounts of memory are allocated. Then, the body of software is executed for providing a service and when the software is terminated, the allocated memory is released. In this paper, a rejuvenation model based on multilevel software rejuvenation and Markov chain presented. In this model, the system performance as a result of degraded physical memory and memory usage is divided into four equal levels by services. Hence, we offer four types of policies for software rejuvenation. In addition, the system availability is determined, and a cost function for the model is introduced. The cost function includes the time of performing rejuvenation, the number of system services at any time, and the number of rejuvenation actions. To validate the proposed model, a case study in the banking system in Iran has been studied. Due to the differences in the use of the system over time, it is better to perform the four different policies with regard to the use of the system. The numerical results show that the proposed model is convenient for the system so that the costs are reduced per day.

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A Rejuvenation Strategy of Two-Granularity Software Based on Adaptive Control

Cited by 4 publications

References 18 publications

Software aging prediction – a new approach

Software aging prediction – a new approach

An analysis of software aging in cloud environment

A software availability model based on multilevel software rejuvenation and markov chain

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