Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Current perspective of symbiotic organisms search technique in cloud computing environment: a review

Nature-inspired algorithms in computer science and engineering are algorithms that take their inspiration from living things and imitate their actions in order to construct functional models. The SOS algorithm (symbiotic organisms search) is a new promising metaheuristic algorithm. It is based on the symbiotic relationship that exists between different species in an ecosystem. Organisms develop symbiotic bonds like mutualism, commensalism, and parasitism to survive in their environment. Standard SOS has since been modified several times, either by hybridization or as better versions of the original algorithm. Most of these modifications came from engineering construction works and other discipline like medicine and finance. However, little improvement on the standard SOS has been noticed on its application in cloud computing environment, especially cloud task scheduling. As a result, this paper provides an overview of SOS applications in task scheduling problem and suggest a new enhanced method for better performance of the technique in terms of fast convergence speed

Resource provisioning and scheduling algorithms for hybrid workflows in edge cloud computing

In recent years, Internet of Things (IoT) technology has been involved in a wide range of application domains to provide real-time monitoring, tracking and analysis services. The worldwide number of IoT-connected devices is projected to increase to 43 billion by 2023, and IoT technologies are expected to engaged in 25% of business sector. Latency-sensitive applications in scope of intelligent video surveillance, smart home, autonomous vehicle, augmented reality, are all emergent research directions in industry and academia. These applications are required connecting large number of sensing devices to attain the desired level of service quality for decision accuracy in a sensitive timely manner. Moreover, continuous data stream imposes processing large amounts of data, which adds a huge overhead on computing and network resources. Thus, latency-sensitive and resource-intensive applications introduce new challenges for current computing models, i.e, batch and stream. In this thesis, we refer to the integrated application model of stream and batch applications as a hybrid work ow model. The main challenge of the hybrid model is achieving the quality of service (QoS) requirements of the two computation systems. This thesis provides a systemic and detailed modeling for hybrid workflows which describes the internal structure of each application type for purposes of resource estimation, model systems tuning, and cost modeling. For optimizing the execution of hybrid workflows, this thesis proposes algorithms, techniques and frameworks to serve resource provisioning and task scheduling on various computing systems including cloud, edge cloud and cooperative edge cloud. Overall, experimental results provided in this thesis demonstrated strong evidences on the responsibility of proposing different understanding and vision on the applications of integrating stream and batch applications, and how edge computing and other emergent technologies like 5G networks and IoT will contribute on more sophisticated and intelligent solutions in many life disciplines for more safe, secure, healthy, smart and sustainable society

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Monitoring and resource management taxonomy in interconnected cloud infrastructures: a survey

Cloud users have recently expanded dramatically. The cloud service providers (CSPs) have also increased and have therefore made their infrastructure more complex. The complex infrastructure needs to be distributed appropriately to various users. Also, the advances in cloud computing have led to the development of interconnected cloud computing environments (ICCEs). For instance, ICCEs include the cloud hybrid, intercloud, multi-cloud, and federated clouds. However, the sharing of resources is not facilitated by specific proprietary technologies and access interfaces used by CSPs. Several CSPs provide similar services but have different access patterns. Data from various CSPs must be obtained and processed by cloud users. To ensure that all ICCE tenants (users and CSPs) benefit from the best CSPs, efficient resource management was suggested. Besides, it is pertinent that cloud resources be monitored regularly. Cloud monitoring is a service that works as a third-party entity between customers and CSPs. This paper discusses a complete cloud monitoring survey in ICCE, focusing on cloud monitoring and its significance. Several current open-source monitoring solutions are discussed. A taxonomy is presented and analyzed for cloud resource management. This taxonomy includes resource pricing, assignment of resources, exploration of resources, collection of resources, and disaster management

National Center for Genome Analysis Program Year 3 Report – September 15, 2013 – September 14, 2014

On September 15, 2011, Indiana University (IU) received three years of support to establish the National Center for Genome Analysis Support (NCGAS). This technical report describes the activities of the third 12 months of NCGASThe facilities supported by the Research Technologies division at Indiana University are supported by a number of grants. The authors would like to acknowledge that although the National Center for Genome Analysis Support is funded by NSF 1062432, our work would not be possible without the generous support of the following awards received by our parent organization, the Pervasive Technology Institute at Indiana University. • The Indiana University Pervasive Technology Institute was supported in part by two grants from the Lilly Endowment, Inc. • NCGAS has also been supported directly by the Indiana METACyt Initiative. The Indiana METACyt Initiative of Indiana University is supported in part by the Lilly Endowment, Inc. • This material is based in part upon work supported by the National Science Foundation under Grant No. CNS-0521433. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (NSF)

WebAL Comes of Age: A review of the first 21 years of Artificial Life on the Web

We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994—when the first WebAL work appeared—up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010–2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area

Open source software ecosystems : a systematic mapping

Context: Open source software (OSS) and software ecosystems (SECOs) are two consolidated research areas in software engineering. OSS influences the way organizations develop, acquire, use and commercialize software. SECOs have emerged as a paradigm to understand dynamics and heterogeneity in collaborative software development. For this reason, SECOs appear as a valid instrument to analyze OSS systems. However, there are few studies that blend both topics together. Objective: The purpose of this study is to evaluate the current state of the art in OSS ecosystems (OSSECOs) research, specifically: (a) what the most relevant definitions related to OSSECOs are; (b) what the particularities of this type of SECO are; and (c) how the knowledge about OSSECO is represented. Method: We conducted a systematic mapping following recommended practices. We applied automatic and manual searches on different sources and used a rigorous method to elicit the keywords from the research questions and selection criteria to retrieve the final papers. As a result, 82 papers were selected and evaluated. Threats to validity were identified and mitigated whenever possible. Results: The analysis allowed us to answer the research questions. Most notably, we did the following: (a) identified 64 terms related to the OSSECO and arranged them into a taxonomy; (b) built a genealogical tree to understand the genesis of the OSSECO term from related definitions; (c) analyzed the available definitions of SECO in the context of OSS; and (d) classified the existing modelling and analysis techniques of OSSECOs. Conclusion: As a summary of the systematic mapping, we conclude that existing research on several topics related to OSSECOs is still scarce (e.g., modelling and analysis techniques, quality models, standard definitions, etc.). This situation calls for further investigation efforts on how organizations and OSS communities actually understand OSSECOs.Peer ReviewedPostprint (author's final draft

A NOVEL TYPE OF FLEXIBLE SOFT ANALYTIC NETWORK PROCESS TO SOLVE THE MULTIPLE-ATTRIBUTE DECISION-MAKING PROBLEM

  Research and development of scientific and technological products have been changing with each passing day in this new millennium. Decisions related to the production of technical products are the key to affecting the sustainable development and market share of enterprises. However, the decision-making related to the production of technology products contains many different evaluation criteria as well as qualitative and quantitative evaluation attributes. Moreover, the correlation between criteria must be considered so it can be treated as a complex multiple-attribute decision-making (MADM) problem. Moreover, performing a multi-attribute decision evaluation often encounters incomplete or missing information provided by experts, which will lead to difficulties in the solution process. In view of the incomplete or missing information of the assessment data, the traditional analytic network process (ANP) method and decision-making trial and evaluation laboratory ANP (DANP) method will delete the incomplete information during the process of assessment and decision-making, and this will bring about non-objective assessment results. In order to solve the above problems, this study proposes a novel type of flexible soft ANP (SANP) method to solve the MADM problems and uses a practical example of smartphone text entry to prove the effectiveness and suitability of the proposed SANP method

Accessible software frameworks for reproducible image analysis of host-pathogen interactions

Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird