Cloud engineering is Search Based Software Engineering too

Harman, Mark; Lakhotia, Kiran; Singer, Jeremy; White, David R.; Yoo, Shin

doi:10.1016/j.jss.2012.10.027

Cited by 50 publications

(42 citation statements)

References 106 publications

(113 reference statements)

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“…b) MOEAs for Cloud Multi-Objective Optimization Problems Resolution: In [18], the authors have motivated the use of Search Based approaches for the resolution of cloud engineering problems. Multi-Objective Evolutionary Algorithms (MOEAs) [12], [36] is a class of search based approaches that addresses problems in which a decision maker aims at finding a solution that optimizes several conflicting objectives.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

Kateb

Fouquet

Bourcier

et al. 2014

2014 14th International Conference on Quality Software

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Abstract-Elasticity is a key feature for cloud infrastructures to continuously align allocated computational resources to evolving hosted software needs. This is often achieved by relaxing quality criteria, for instance security or privacy because quality criteria are often conflicting with performance. As an example, software replication could improve scalability and uptime while decreasing privacy by creating more potential leakage points. The conciliation of these conflicting objectives has to be achieved by exhibiting trade-offs. Multi-Objective Evolutionary Algorithms (MOEAs) have shown to be suitable candidates to find these trade-offs and have been even applied for cloud architecture optimizations. Still though, their runtime efficiency limits the widespread adoption of such algorithms in cloud engines, and thus the consideration of quality criteria in clouds. Indeed MOEAs produce many dead-born solutions because of the Darwinian inspired natural selection, which results in a resources wastage. To tackle MOEAs efficiency issues, we apply a process similar to modern biology. We choose specific artificial mutations by anticipating the optimization effect on the solutions instead of relying on the randomness of natural selection. This paper introduces the Sputnik algorithm, which leverages the past history of actions to enhance optimization processes such as cloud elasticity engines. We integrate Sputnik in a cloud elasticity engine, dealing with performance and quality criteria, and demonstrate significant performance improvement, meeting the runtime requirements of cloud optimization.

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Section: Introductionmentioning

confidence: 99%

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

Kateb

Fouquet

Bourcier

et al. 2014

2014 14th International Conference on Quality Software

View full text Add to dashboard Cite

show abstract

“…All IT functions with applications, networking, security, storage space and software are developedfor users to work in a service, based on the client server model. Dynamic Adaptive Automated Software Engineering (DAASE) as shown in [13], whose goal is to implant optimization deployed software to produce selfoptimizing adaptive systems. The distribution needs to be carefully chosen, so that it satisfies the testing objectives expressed in terms of functional or structural properties of the software.…”

Section: Introductionmentioning

confidence: 99%

“…The distribution needs to be carefully chosen, so that it satisfies the testing objectives expressed in terms of functional or structural properties of the software. The major security, privacy and trust issues in current existing cloud computing environments as described in [13] help users to be familiar with the tangible and intangible threats connected with users. Privacy and trust issues still occur in cloud computing environments by a quantifiable approach and also fails to satisfy the security demands.…”

Section: Introductionmentioning

confidence: 99%

“…The Secure cloud storage system as depicted in [10] supports privacy-preserving public auditing which performs audits for multiple users concurrently and proficiently. Public auditability for cloud storage is of serious consequence so that users resort to a Third Party Auditor (TPA) as shown in [13] check the integrity of outsourced data. To securely establish an efficient TPA, the auditing process brings in novel vulnerabilities towards user data privacy.…”

Section: Introductionmentioning

confidence: 99%

“…It promises many benefits, such as dropping infrastructure investments, the capability to rapidly acclimatize and compute power according to the demands and the adoption of a pay-as-you-go billing model. Trusted Third Party tasked with assuring specific security characteristics inside a cloud environment as demonstrated in [13] concerned in data communications but does not achieve the quality of services.…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Approach to Detect Software Anomalies in Cloud Computing Using Pragmatic Bayes Approach

Nethaji¹,

Chandrasekar²

2014

IJMECS

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Abstract-Software detection of anomalies is a vital element of operations in data centers and service clouds. Statistical Process Control (SPC) cloud charts sense routine anomalies and their root causes are identified based on the differential profiling strategy. By automating the tasks, most of the manual overhead incurred in detecting the software anomalies and the analysis time are reduced to a larger extent but detailed analysis of profiling data are not performed in most of the cases. On the other hand, the cloud scheduler judges both the requirements of the user and the available infrastructure to equivalent their requirements. OpenStack prototype works on cloud trust management which provides the scheduler but complexity occurs when hosting the cloud system. At the same time, Trusted Computing Base (TCB) of a computing node does not achieve the scalability measure. This unique paradigm brings about many software anomalies, which have not been well studied. This work, a Pragmatic Bayes approach studies the problem of detecting software anomalies and ensures scalability by comparing information at the current time to historical data. In particular, PB approach uses the two component Gaussian mixture to deviations at current time in cloud environment. The introduction of Gaussian mixture in PB approach achieves higher scalability measure which involves supervising massive number of cells and fast enough to be potentially useful in many streaming scenarios. Wherein previous works has been ensured for scheduling often lacks of scalability, this paper shows the superiority of the method using a Bayes per section error rate procedure through simulation, and provides the detailed analysis of profiling data in the marginal distributions using the Amazon EC2 dataset. Extensive performance analysis shows that the PB approach is highly efficient in terms of runtime, scalability, software anomaly detection ratio, CPU utilization, density rate, and computational complexity.

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Survey on the run‐time systems of enterprise application integration platforms focusing on performance

Freire¹,

Frantz²,

Roos‐Frantz³

et al. 2018

Softw Pract Exp

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Companies are taking advantage of cloud computing to upgrade their business processes. Cloud computing requires interaction with many kinds of applications, so it is necessary to improve the performance of software tools that allow keeping information on all these applications consistent and synchronised. Integration platforms are specialised software tools that provide support to design, implement, run, and monitor integration solutions, which aim to orchestrate a set of applications so as to promote compatibility among their data or to develop new functionality on top of the current ones. The run-time system is the part of the integration platform responsible for running the integration solutions, which makes its performance the uttermost important issue. The contribution of this article is two-fold: a framework and an evaluation of integration platforms. The former is a framework composed of ten properties grouped into two dimensions to evaluate the run-time systems focusing on performance. Using this framework as reference, the second contribution is an evaluation of nine open-source integration platforms, which represent the state-of-the-art, provide support to the integration patterns, and follow the pipes-and-filters architectural style. In addition, as a result of this work, we suggest open research directions that can be explored to improve the performance of the run-time systems and at the same time may be useful to adapt them to the context of cloud computing. KEYWORDScloud computing, enterprise application integration, integration patterns, integration platform, run-time system Companies often need to use their software ecosystems 1,2 to support and improve their business processes. These ecosystems are composed of many applications, usually designed without taking into account their possible integration. The field of study known as Enterprise Integration Applications (EAI) seeks to provide methodologies, techniques, and tools for the design and implementation of integration solutions. 3 In general terms, an integration solution aims to orchestrate a set of applications to keep them synchronised or provide new features that can be built from those already developed. An integration solution is composed of processes that contain the integration logic and ports that encapsulate adapters with communication protocols to connect applications to the integration solution. . 341 language is focused on the elaboration of conceptual models for the integration solution, with a level of abstraction close to the domain of the problem. The development toolkit is a set of tools that allows the implementation of the solution, that is, the transformation of the conceptual model into executable code. The testing environment allows testing individual parts or the entire integration solution, with the aim of identifying and eliminating possible defects in the implementation. The monitoring tool is used to monitor, at run time, the operation of the integration solution and detects errors that may occur during message proce...

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Cloud engineering is Search Based Software Engineering too

Cited by 50 publications

References 106 publications

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

Optimizing Multi-objective Evolutionary Algorithms to Enable Quality-Aware Software Provisioning

An Automatic Approach to Detect Software Anomalies in Cloud Computing Using Pragmatic Bayes Approach

Survey on the run‐time systems of enterprise application integration platforms focusing on performance

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