Design Of A Performance Prediction System For A Distributed Computer Environment And Its Adaptation On The Cloud Computing

Grid computing is a major research area with strong involvement from both academia and the computing industry. The common vision is that grid computing represents the culmination of truly general distributed computing across various resources in a ubiquitous, open-ended infrastructure to support a wide range of different application areas. Although significant progress has been made in the design and deployment of grids, many challenges still remain before the goal of a user-friendly, efficient, and reliable grid can be realized. Grid research issues cover many areas of computer science to address the fundamental capabilities and services that are required in a heterogeneous environment, such as adaptability, scalability, reliability and security, and to support applications as diverse as ubiquitous local services, enterprise-scale virtual organizations, and internet-scale distributed supercomputing. Cloud computing is also emerging as an alternate platform for large-scale distributed applications where resources are typically provided by a single administrative domain in a pay per-use mode. To some, cloud computing is a natural evolution of grid computing, to others, it is a complementary and perhaps competing technology. Grid and cloud research will greatly benefit from interactions with the many related areas of computer science, making Euro-Par an excellent venue to present results and discuss issues.

Computing has changed the way humans solve problems. It has changed the world more than any other innovation in the recent past. Over the last couple of years, there has been an increased interest in utilizing computing processor powers to its maximum. Cloud, cluster and grid computing are the most common types of computing. All these share some common features toward achieving utility although marginal differences exist in their processes and approaches. The paper presents the relationship and the differences of grid computing from cloud computing in terms of their architecture, applications, security and tools.

In this paper, we review and summarize some of the leading computing concepts including cluster, grid, and cloud computing. These ideas relate to bringing computers together to build a more powerful computational engine that is capable of processing multiple tasks concurrently. In cluster computing, a group of computers are located close by and directly linked with extremely high speed connections in order to perform as a single computer system. In grid computing, computer resources from multiple Internet domains are combined to reach a common computational goal. Compared with clusters, grids tend to be more loosely coupled, heterogeneous, and geographically distributed. Cloud computing is an emerging technology allowing a large number of interconnected computers, hardware and/or software to be rented as a commodity like electricity. The service types of cluster, grid, and cloud have been compared. We conclude that cloud computing has the potential to meet various computing demands in a cost effective easy way provided that some crucial issues like privacy, security, migration, and management are properly addressed.

The 2013 IEEE Latin America Conference on Cloud Computing and Communications (Latin- Cloud 2013) is the second edition of a conference that aims at becoming the premier forum for discussions of recent advances in cloud computing and communications, virtualization, utility computing, grid computing, service-oriented architectures, and software- defined networks for the datacenter in this region of the world. The Cloud Computing and Communications paradigm has defined unprecedented business models, promoting on-demand computing resource allocation, elastic scaling, and elimination of up-front costs. Capitalizing on paradigms such as virtualization, utility computing, grid computing, and service oriented architectures, cloud computing and communications has already changed the way we compute and communicate. To cope with technology challenges for the support of diverse services and applications, the IEEE Communications Society (ComSoc), Latin America Region, with the support of the IEEE Cloud Computing Initiative, supported the 2013 IEEE Latin America Conference on Cloud Computing and Communications (LatinCloud 2013). The conference has the goal of establishing a scientific forum for the discussion of the rather relevant topic of cloud computing and its critically associated aspects of communications in the Latin American context.

Cloud have wide-ranging implications in several areas of computing, notably massive knowledge, cloud computing has simply up to the highest of any list of technology subjects. Furthermore, it is one of the most important chop-chop developing technologies due to the availability of a wide range of computer infrastructure and services. The expansion of cloud computing has included mainframe computing, cluster and grid computing, distributed and parallel computing technologies, and so on. Cloud computing has mostly supplanted grid, cluster, and mainframe computing. This refers to the delivery of numerous services over the web differently. Such as databases, infrastructure, run time platforms, knowledge storage, servers, networking, and code area unit samples of these resources. Price savings, bigger productivity, speed, efficiency, high performance, high computing power, and security are simply many of the explanations why consumers and businesses embrace the on demand computing. This article goes through the definition of cloud computing as well as its properties, various cloud services, deployment models, and security concerns.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We'll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues

An increasing number of applications are being hosted on cloud based platforms. Cloud platforms are serving as a general computing facility and applications being hosted on these platforms range from simple multi-tier web applications to complex social networking, eCommerce and Big Data applications. High availability, performance and auto-scaling are key requirements of Cloud based applications. Cloud platforms serve these requirements using dynamic provisioning of resources in on-demand, multi-tenant fashion. A key challenge for cloud service providers is to ensure the Quality of Service (QoS), as a user / customer requires more explicit guarantees of QoS for provisioning of services. Cloud service performance problems can directly lead to extensive financial loses. Thus, control and verification of QoS become a vital concern for any production level deployment. Therefore, it is crucial to address performance as a managed objective. The success of cloud services depends critically on automated problem diagnostics and predictive analytics enabling organizations to manage their performance proactively. Moreover, effective and advance monitoring is equally important for performance management support in clouds. In this thesis, we explore the key techniques for developing monitoring and performance management systems to achieve robust cloud systems. At first, two case studies are presented as a motivation for the need of a scalable monitoring and analytics framework. It includes a case study on performance issues of a software service, which is hosted on a virtualized platform. In the second case study, cloud services are analyzed that are offered by a large IT service provider. A generalization of case studies forms the basis for the requirement specifications which are used for state-of-the-art analysis. Although, some solutions for particular challenges have already been provided, a scalable approach for performance problem diagnosis and prediction is still missing. For addressing this issue, a distributed scalable monitoring and analytics framework is presented in the first part of this thesis. We conducted a thorough analysis of technologies to be used by our framework. The framework makes use of existing monitoring and analytics technologies. However, we develop custom collectors to retrieve data non-intrusively from different layers of cloud. In addition, we develop the analytics subscriber and publisher components to retrieve service related events from different APIs and sends alerts to the SLA Management component for taking corrective measures. Further, we implemented an Open Cloud Computing Interface (OCCI) monitoring extension using OCCI Mixin mechanism. To deal with performance problem diagnosis, a novel distributed parallel approach for performance anomaly detection is presented. First all anomalous metrics are found from a distributed database of time-series for a particular window. For comparative analysis three light-weight statistical anomaly detection techniques are selected. We extend these techniques to work with MapReduce paradigm and assess and compare the methods in terms of precision, recall, execution time, speedup and scale up. Next, we correlate the anomalous metrics with the target SLO in order to locate the suspicious metrics. We implemented and evaluated our approach on a production Cloud encompassing Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) service models. Experimental results confirm that our approach is efficient and effective in capturing the metrics causing performance anomalies. Finally, we present the design and implementation of an online anomaly prediction system for cloud computing infrastructures. We further present an experimental evaluation of a set of anomaly prediction methods that aim at predicting upcoming periods of high utilization or poor performance with enough lead time to enable the appropriate scheduling, scaling, and migration of virtual resources. Using real data sets gathered from Cloud platforms of a university data center, we compare several approaches ranging from time-series (e.g. auto regression (AR)) to statistical classification methods (e.g. Bayesian classifier). We observe that linear time-series models, especially AR models, are most likely suitable to model QoS measures and forecast their future values. Moreover, linear time-series models can be integrated with Machine Learning (ML) methods to improve proactive QoS management.

As the demand for energy increases, the need to generate and distribute energy to the customers with greater efficiency also increases. Introduction of smart grids provides platform for the utilities to collect and analyze consumption data in real time. This helps them to define the generation profile and offer competitive energy prices to the customers. Customer on the other hand can use the knowledge of his own consumption profile to define and tune his energy usage. Education about smart grid environment which involves software, hardware devices, and network technologies for data collection and analysis is important for both utilities and customers. Research and development in Internet technologies promote remote laboratory as a cost-effective solution for users located across the globe. Cloud computing platform can further reduce costs involved in data storage and software used. This paper presents the idea of developing the remote laboratory located at South Westphalia University in Soest, Germany, further by integrating cloud computing and smart grid simulation environment. This will educate people across the globe by offering them hands on experience on smart grid technology and thus will contribute to the field of power engineering education.KeywordsSmart GridCloud ComputingTelelabRemote LaboratorySouth Westphalia UniversityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Educational institutions throughout the World have become highly dependent on information technology for their teaching-learning, service delivery and business requirements. The term cloud computing is completely new trend and technology which is known as third party revolution after computer and internet, in distributed computing, parallel computing, grid computing and in the case of distributed database improves the power of digital library. Cloud computing is making it possible to separate the process of building an infrastructure for service provisioning from the library of providing end user services. Cloud computing provides people the way to share distributed resources and services that belong to different organizations or sites. Cloud computing share distributed resources via the network in the open environment. The digital libraries have to face with some technique and service problems along with many advantages relative to the traditional libraries, and all these produce the practical needs of cloud computing. Cloud computing has influenced information resources, information users, information personnel and information facilities of digital libraries with its unique advantages and then promoted the development of digital libraries. Developed country like Ethiopia Libraries may soon be building and managing their own data centers. Different models show that libraries maintain more control over the applications and data stores that contain sensitive, private information about patrons. Provisioning and maintenance of infrastructure for Web based digital library present several challenges. In this paper we discuss problems faced with digital library and development efforts to overcome that problem. Infrastructure virtualization and cloud computing are particularly attractive choices which is challenged by both growth in the size of the indexed document collection, new features and most prominently usage. With the overview of Cloud Computing to university library, services of libraries will have a new leap in the near future. Services provided by libraries will become more user centric, more professional and more effective, etc. And we all believe that libraries will create more knowledge benefits for Ethiopia with the help of Cloud Computing.

Arc4nix: A cross-platform geospatial analytical library for cluster and cloud computing

As the current hot technology, many large companies have launched their own cloud computing projects, and the ways to realize each project is different, and ultimately lead to a fragmented state to explain the concept of cloud computing today. Almost all manufacturers do not deny that cloud computing is follow-up and development of grid computing, but their philosophy is different. This article aims to explore the similarities and differences between grid computing and cloud computing, from features, technology and operation costs, and to explore the solution of technical problems related to outstanding solutions through grid technology in the development of cloud computing.

Purpose– Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.Design/methodology/approach– A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.Findings– The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.Research limitations/implications– The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.Practical implications– Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.Originality/value– It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.

With the popularity of e-learning, to construct a perfect e-learning environment has become a popular research topic in the field of modern distance education. Cloud computing is a new computing model which is based on the grid computing, it is the core technology of the next generation of network computing platform, especially in the field of education, cloud computing is the basic environment and platform of the future e-learning. It provides secure data storage, convenient internet services and strong computing power. This essay mainly focuses on the research of the application of cloud computing in E-learning environment. Taking the “Google collaborative platform” as a case study, one factor of E-learning environment construction-“the collaborative learning platform” has been built, and also the “collaborative learning” has been given detailed illustration.