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Scientific workflows have been an increasingly important research area of distributed systems (such as cloud computing). Researchers have shown an increased interest in the automated processing scientific applications such as workflows. Recently, Function as a Service (FaaS) has emerged as a novel distributed systems platform for processing non-interactive applications. FaaS has limitations in resource use (e.g., CPU and RAM) as well as state management. In spite of these, initial studies have already demonstrated using FaaS for processing scientific workflows. DEWE v3 executes workflows in this fashion, but it often suffers from duplicate data transfers while using FaaS. This behaviour is due to the handling of intermediate data dependencies after and before each function invocation. These data dependencies could fill the temporary storage of the function environment. Our approach alters the job dispatch algorithm of DEWE v3 to reduce data dependency transfers. The proposed algorithm schedules jobs with precedence requirements to primarily run in the same function invocation. We evaluate our proposed algorithm and the original algorithm with small- and large-scale Montage workflows. Our results show that the improved system can reduce the total workflow execution time of scientific workflows over DEWE v3 by about 10\% when using AWS Lambda.
Ali Al-Haboobi; Gabor Kecskemeti. Execution Time Reduction in Function Oriented Scientific Workflows. Acta Cybernetica 2021, 1 .
AMA StyleAli Al-Haboobi, Gabor Kecskemeti. Execution Time Reduction in Function Oriented Scientific Workflows. Acta Cybernetica. 2021; ():1.
Chicago/Turabian StyleAli Al-Haboobi; Gabor Kecskemeti. 2021. "Execution Time Reduction in Function Oriented Scientific Workflows." Acta Cybernetica , no. : 1.
Discrete Event Simulation (DES) frameworks gained significant popularity to support and evaluate cloud computing environments. They support decision-making for complex scenarios, saving time and effort. The majority of these frameworks lack parallel execution. In spite being a sequential framework, DISSECT-CF introduced significant performance improvements when simulating Infrastructure as a Service (IaaS) clouds. Even with these improvements over the state of the art sequential simulators, there are several scenarios (e.g., large scale Internet of Things or serverless computing systems) which DISSECT-CF would not simulate in a timely fashion. To remedy such scenarios this paper introduces parallel execution to its most abstract subsystem: the event system. The new event subsystem detects when multiple events occur at a specific time instance of the simulation and decides to execute them either on a parallel or a sequential fashion. This decision is mainly based on the number of independent events and the expected workload of a particular event. In our evaluation, we focused exclusively on time management scenarios. While we did so, we ensured the behaviour of the events should be equivalent to realistic, larger-scale simulation scenarios. This allowed us to understand the effects of parallelism on the whole framework, while we also shown the gains of the new system compared to the old sequential one. With regards to scaling, we observed it to be proportional to the number of cores in the utilised SMP host.
Dilshad Hassan Sallo; Gabor Kecskemeti. A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF. Acta Cybernetica 2021, 1 .
AMA StyleDilshad Hassan Sallo, Gabor Kecskemeti. A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF. Acta Cybernetica. 2021; ():1.
Chicago/Turabian StyleDilshad Hassan Sallo; Gabor Kecskemeti. 2021. "A Parallel Event System for Large-Scale Cloud Simulations in DISSECT-CF." Acta Cybernetica , no. : 1.
The inevitable evolution of information technology has led to the creation of IoT-Fog-Cloud systems, which combine the Internet of Things (IoT), Cloud Computing and Fog Computing. IoT systems are composed of possibly up to billions of smart devices, sensors and actuators connected through the Internet, and these components continuously generate large amounts of data. Cloud and fog services assist the data processing and storage needs of IoT devices. The behaviour of these devices can change dynamically (e.g. properties of data generation or device states). We refer to systems allowing behavioural changes in physical position (i.e. geolocation), as the Internet of Mobile Things (IoMT). The investigation and detailed analysis of such complex systems can be fostered by simulation solutions. The currently available, related simulation tools are lacking a generic actuator model including mobility management. In this paper, we present an extension of the DISSECT-CF-Fog simulator to support the analysis of arbitrary actuator events and mobility capabilities of IoT devices in IoT-Fog-Cloud systems. The main contributions of our work are: (i) a generic actuator model and its implementation in DISSECT-CF-Fog, and (ii) the evaluation of its use through logistics and healthcare scenarios. Our results show that we can successfully model IoMT systems and behavioural changes of actuators in IoT-Fog-Cloud systems in general, and analyse their management issues in terms of usage cost and execution time.
Andras Markus; Mate Biro; Gabor Kecskemeti; Attila Kertesz. Actuator behaviour modelling in IoT-Fog-Cloud simulation. PeerJ Computer Science 2021, 7, e651 .
AMA StyleAndras Markus, Mate Biro, Gabor Kecskemeti, Attila Kertesz. Actuator behaviour modelling in IoT-Fog-Cloud simulation. PeerJ Computer Science. 2021; 7 ():e651.
Chicago/Turabian StyleAndras Markus; Mate Biro; Gabor Kecskemeti; Attila Kertesz. 2021. "Actuator behaviour modelling in IoT-Fog-Cloud simulation." PeerJ Computer Science 7, no. : e651.
Cloud Computing has become the major candidate for commercial and academic compute infrastructures. Its virtualized solutions enable efficient, high-rate exploitation of computational and storage resources due to recent advances in data centre consolidation. Resources leased from these providers are offered under many pricing schemes which are often times influenced by the utilised consolidation techniques. In this paper, we provide a foundation to understand the inter-relationship of pricing and consolidation. This has a potential to reach additional gains for the providers from a new angle. To this end we discuss the introduction of a pricing oriented extension of the DISSECT-CF cloud simulator, and introduce a simple consolidation framework that allows easy experimentation with combined pricing and consolidation approaches. Using our generic extensions, we show several simple but easy to combine pricing strategies. Finally, we analyse the impact of consolidators on the profitability of providers applying our simple schemes with the help of real world workload traces.
Gabor Kecskemeti; Andras Markus; Attila Kertesz. Towards Pricing-Aware Consolidation Methods for Cloud Datacenters. Communications in Computer and Information Science 2019, 152 -167.
AMA StyleGabor Kecskemeti, Andras Markus, Attila Kertesz. Towards Pricing-Aware Consolidation Methods for Cloud Datacenters. Communications in Computer and Information Science. 2019; ():152-167.
Chicago/Turabian StyleGabor Kecskemeti; Andras Markus; Attila Kertesz. 2019. "Towards Pricing-Aware Consolidation Methods for Cloud Datacenters." Communications in Computer and Information Science , no. : 152-167.
Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (\(\sim\) 20%) better workload predictions for the future of simulated clouds than random workload selection.
Gabor Kecskemeti; Zsolt Nemeth; Attila Kertesz; Rajiv Ranjan. Cloud workload prediction based on workflow execution time discrepancies. Cluster Computing 2018, 22, 737 -755.
AMA StyleGabor Kecskemeti, Zsolt Nemeth, Attila Kertesz, Rajiv Ranjan. Cloud workload prediction based on workflow execution time discrepancies. Cluster Computing. 2018; 22 (3):737-755.
Chicago/Turabian StyleGabor Kecskemeti; Zsolt Nemeth; Attila Kertesz; Rajiv Ranjan. 2018. "Cloud workload prediction based on workflow execution time discrepancies." Cluster Computing 22, no. 3: 737-755.
In the paradigm of Internet of Things (IoT), sensors, actuators and smart devices are connected to the Internet. Application providers utilize the connectivity of these devices with novel approaches involving cloud computing. Some applications require in depth analysis of the interaction between IoT devices and clouds. Research in this area is facing questions like how we should govern such large cohort of devices, which may easily go up often to tens of thousands. In this chapter we investigate IoT Cloud use cases, and derive a general IoT use case. Distributed systems simulators could help in such analysis, but they are problematic to apply in this newly emerging domain, since most of them are either too detailed, or not extensible enough to support the to be modelled devices. Therefore we also show how generic IoT sensors could be modelled in a state of the art simulator using our generalized case to exemplify how the fundamental properties of IoT entities can be represented in the simulator. Finally, we validate the applicability of the introduced IoT extension with a fitness and a meteorological use case.
Andras Markus; Andre Marques; Gabor Kecskemeti; Attila Kertesz. Efficient Simulation of IoT Cloud Use Cases. Transactions on Petri Nets and Other Models of Concurrency XV 2018, 313 -336.
AMA StyleAndras Markus, Andre Marques, Gabor Kecskemeti, Attila Kertesz. Efficient Simulation of IoT Cloud Use Cases. Transactions on Petri Nets and Other Models of Concurrency XV. 2018; ():313-336.
Chicago/Turabian StyleAndras Markus; Andre Marques; Gabor Kecskemeti; Attila Kertesz. 2018. "Efficient Simulation of IoT Cloud Use Cases." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 313-336.
Virtual machine (VM) images (VMIs) often share common parts of significant size as they are stored individually. Using existing de-duplication techniques for such images are non-trivial, impose serious technical challenges, and requires direct access to clouds’ proprietary image storages, which is not always feasible. We propose an alternative approach to split images into shared parts, called fragments, which are stored only once. Our solution requires a reasonably small set of base images available in the cloud, and additionally only the increments will be stored without the contents of base images, providing significant storage space savings. Composite images consisting of a base image and one or more fragments are assembled on-demand at VM deployment. Our technique can be used in conjunction with practically any popular cloud solution, and the storage of fragments is independent of the proprietary image storage of the cloud provider.
Ákos Hajnal; Gabor Kecskemeti; Attila Csaba Marosi; József Kovács; Péter Kacsuk; Róbert Lovas. ENTICE VM Image Analysis and Optimised Fragmentation. Journal of Grid Computing 2018, 16, 247 -263.
AMA StyleÁkos Hajnal, Gabor Kecskemeti, Attila Csaba Marosi, József Kovács, Péter Kacsuk, Róbert Lovas. ENTICE VM Image Analysis and Optimised Fragmentation. Journal of Grid Computing. 2018; 16 (2):247-263.
Chicago/Turabian StyleÁkos Hajnal; Gabor Kecskemeti; Attila Csaba Marosi; József Kovács; Péter Kacsuk; Róbert Lovas. 2018. "ENTICE VM Image Analysis and Optimised Fragmentation." Journal of Grid Computing 16, no. 2: 247-263.
Cloud Computing has become mature enough to enable the virtualized management of multiple datacentres. Datacentre consolidation is an important method for the efficient operation of such distributed infrastructures. Several approaches have been developed to improve the efficiency e.g. in terms of power consumption, but only a few attention has been turned to combining pricing methods with consolidation techniques. In this paper we discuss how we introduced cost models to the DISSECT-CF simulator to foster the development of cost efficient datacentre consolidation solutions. We also exemplify the usage of this extended simulator by performing cost-aware datacentre consolidation. We apply real world traces to simulate cloud load, and propose 7 strategies to address the problem.
Gabor Kecskemeti; Andras Markus; Attila Kertész. Cost-efficient Datacentre Consolidation for Cloud Federations. Proceedings of the 8th International Conference on Cloud Computing and Services Science 2018, 213 -220.
AMA StyleGabor Kecskemeti, Andras Markus, Attila Kertész. Cost-efficient Datacentre Consolidation for Cloud Federations. Proceedings of the 8th International Conference on Cloud Computing and Services Science. 2018; ():213-220.
Chicago/Turabian StyleGabor Kecskemeti; Andras Markus; Attila Kertész. 2018. "Cost-efficient Datacentre Consolidation for Cloud Federations." Proceedings of the 8th International Conference on Cloud Computing and Services Science , no. : 213-220.
In the age of the Internet of Things (IoT), more and more sensors, actuators and smart devices get connected to the network. Application providers often combine this connectivity with novel scenarios involving cloud computing. Before implementing changes in these large-scale systems, an in-depth analysis is often required to identify governance models, bottleneck situations, costs and unexpected behaviours. Distributed systems simulators help in such analysis, but they are often problematic to apply in this newly emerging domain. For example, most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. To overcome these issues, we discuss our IoT cost analysis oriented extension of DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF). Thus, we present an in-depth analysis of IoT and cloud related pricing models of the most widely used commercial providers. Then, we show how the fundamental properties (e.g., data production frequency) of IoT entities could be linked to the identified pricing models. To allow the adoption of unforeseen scenarios and pricing schemes, we present a declarative modelling language to describe these links. Finally, we validate our extensions by analysing the effects of various identified pricing models through five scenarios coming from the field of weather forecasting.
Andras Markus; Attila Kertesz; Gabor Kecskemeti. Cost-Aware IoT Extension of DISSECT-CF. Future Internet 2017, 9, 47 .
AMA StyleAndras Markus, Attila Kertesz, Gabor Kecskemeti. Cost-Aware IoT Extension of DISSECT-CF. Future Internet. 2017; 9 (3):47.
Chicago/Turabian StyleAndras Markus; Attila Kertesz; Gabor Kecskemeti. 2017. "Cost-Aware IoT Extension of DISSECT-CF." Future Internet 9, no. 3: 47.
The use of virtual machines (VMs) in Cloud computing provides various benefits in the overall software engineering lifecycle. These include efficient elasticity mechanisms resulting in higher resource utilization and lower operational costs. The VMs as software artifacts are created using provider-specific templates, called virtual machine images (VMI), and are stored in proprietary or public repositories for further use. However, some technology-specific choices can limit the interoperability among various Cloud providers and bundle the VMIs with nonessential or redundant software packages, leading to increased storage size, prolonged VMI delivery, stagnant VMI instantiation, and ultimately vendor lock-in. To address these challenges, we present a set of novel functionalities and design approaches for efficient operation of distributed VMI repositories, specifically tailored for enabling (1) simplified creation of lightweight and size optimized VMIs tuned for specific application requirements; (2) multi-objective VMI repository optimization; and (3) efficient reasoning mechanism to help optimizing complex VMI operations. The evaluation results confirm that the presented approaches can enable VMI size reduction by up to 55%, while trimming the image creation time by 66%. Furthermore, the repository optimization algorithms can reduce the VMI delivery time by up to 51% and cut down the storage expenses by 3%. Moreover, by implementing replication strategies, the optimization algorithms can increase the system reliability by 74%.
Dragi Kimovski; Attila Marosi; Sandi Gec; Nishant Saurabh; Attila Kertesz; Gabor Kecskemeti; Vlado Stankovski; Radu Prodan. Distributed environment for efficient virtual machine image management in federated Cloud architectures. Concurrency and Computation: Practice and Experience 2017, 30, e4220 .
AMA StyleDragi Kimovski, Attila Marosi, Sandi Gec, Nishant Saurabh, Attila Kertesz, Gabor Kecskemeti, Vlado Stankovski, Radu Prodan. Distributed environment for efficient virtual machine image management in federated Cloud architectures. Concurrency and Computation: Practice and Experience. 2017; 30 (20):e4220.
Chicago/Turabian StyleDragi Kimovski; Attila Marosi; Sandi Gec; Nishant Saurabh; Attila Kertesz; Gabor Kecskemeti; Vlado Stankovski; Radu Prodan. 2017. "Distributed environment for efficient virtual machine image management in federated Cloud architectures." Concurrency and Computation: Practice and Experience 30, no. 20: e4220.
Cloud computing is the cornerstone for elastic and on-demand service provisioning to achieve more efficient resource utilisation and quicker responses to varying application loads. Virtual machines, one of the building blocks of clouds, can be created using provider specific templates stored in proprietary repositories, which may lead to provider lock-in and decreased portability. Despite these enabling technologies, large scale service oriented applications are still mostly inelastic. Such applications often use monolithic services that limit the elasticity (e.g., by obstructing the replicability of parts of a monolithic service). Decomposing these services to smaller, more targeted and more modular services would open towards elasticity, but the decomposition process is mostly manual. This paper introduces a methodology for decomposing monolithic services to several so called microservices. The proposed methodology applies several achievements of the ENTICE project: its image synthesis and optimisation tools. Finally, the paper provides insights on how these achievements help revitalise past monolithic services, and what techniques are applied to aid future microservice developers.
Gabor Kecskemeti; Attila Kertesz; Attila Csaba Marosi. Towards a Methodology to Form Microservices from Monolithic Ones. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 284 -295.
AMA StyleGabor Kecskemeti, Attila Kertesz, Attila Csaba Marosi. Towards a Methodology to Form Microservices from Monolithic Ones. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():284-295.
Chicago/Turabian StyleGabor Kecskemeti; Attila Kertesz; Attila Csaba Marosi. 2017. "Towards a Methodology to Form Microservices from Monolithic Ones." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 284-295.
Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application.
Gabor Kecskemeti; Attila Kertesz; Zsolt Nemeth. Cloud Workload Prediction by Means of Simulations. Proceedings of the Computing Frontiers Conference 2017, 279 -282.
AMA StyleGabor Kecskemeti, Attila Kertesz, Zsolt Nemeth. Cloud Workload Prediction by Means of Simulations. Proceedings of the Computing Frontiers Conference. 2017; ():279-282.
Chicago/Turabian StyleGabor Kecskemeti; Attila Kertesz; Zsolt Nemeth. 2017. "Cloud Workload Prediction by Means of Simulations." Proceedings of the Computing Frontiers Conference , no. : 279-282.
Virtualization is a key enabling technology in Cloud computing that allows users to run multiple virtual machines (VMs) with their own application environment on top of physical hardware. It permits scaling up and down of applications by elastic on-demand provisioning of VMs in response to their variable load to achieve increased utilization efficiency at a lower operational cost, while guaranteeing the desired level of Quality of Service (QoS) to the end-users. Typically, VMs are created using provider-specific templates that are stored in proprietary repositories, leading to provider lock-in and hampering portability or simultaneous usage of multiple federated Clouds. In this context, optimization at the level of the virtual machine image is needed both by the applications and by the underlying Cloud providers for improved resource usage, operational costs, elasticity, storage use, and other desired QoS-related features. To overcome those issues, the ENTICE project researches and creates a novel VM repository and operational environment for federated Cloud infrastructures. There exists a large variety of industrial applications that can strongly benefit by the ENTICE environment. In this paper we present an interesting selection of complementary use cases that drive the definition of the essential requirements for the ENTICE environment, and more importantly, validate the introduced innovations.
Radu Prodan; Thomas Fahringer; Dragi Kimovski; Gabor Kecskemeti; Attila Csaba Marosi; Vlado Stankovski; Jonathan Becedas; Jose Julio Ramos; Craig Sheridan; Darren Whigham; Carlos Rodrigo Rubia Marcos. Use Cases towards a Decentralized Repository for Transparent and Efficient Virtual Machine Operations. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) 2017, 478 -485.
AMA StyleRadu Prodan, Thomas Fahringer, Dragi Kimovski, Gabor Kecskemeti, Attila Csaba Marosi, Vlado Stankovski, Jonathan Becedas, Jose Julio Ramos, Craig Sheridan, Darren Whigham, Carlos Rodrigo Rubia Marcos. Use Cases towards a Decentralized Repository for Transparent and Efficient Virtual Machine Operations. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). 2017; ():478-485.
Chicago/Turabian StyleRadu Prodan; Thomas Fahringer; Dragi Kimovski; Gabor Kecskemeti; Attila Csaba Marosi; Vlado Stankovski; Jonathan Becedas; Jose Julio Ramos; Craig Sheridan; Darren Whigham; Carlos Rodrigo Rubia Marcos. 2017. "Use Cases towards a Decentralized Repository for Transparent and Efficient Virtual Machine Operations." 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) , no. : 478-485.
In the past few years, several studies proposed to reduce the impact of bushfires by mapping their occurrences and spread. Most of these prediction/mapping tools and models were designed to run either on a single local machine or a High performance cluster, neither of which can scale with users’ needs. The process of installing these tools and models their configuration can itself be a tedious and time consuming process. Thus making them, not suitable for time constraint cyber–physical emergency systems. In this research, to improve the efficiency of the fire prediction process and make this service available to several users in a scalable and cost-effective manner, we propose a scalable Cloud based bushfire prediction framework, which allows forecasting of the probability of fire occurrences in different regions of interest. The framework automates the process of selecting particular bushfire models for specific regions and scheduling users’ requests within their specified deadlines. The evaluation results show that our Cloud based bushfire prediction system can scale resources and meet user requirements.
Saurabh Kumar Garg; Jagannath Aryal; Hao Wang; Tejal Shah; Gabor Kecskemeti; Rajiv Ranjan. Cloud computing based bushfire prediction for cyber–physical emergency applications. Future Generation Computer Systems 2017, 79, 354 -363.
AMA StyleSaurabh Kumar Garg, Jagannath Aryal, Hao Wang, Tejal Shah, Gabor Kecskemeti, Rajiv Ranjan. Cloud computing based bushfire prediction for cyber–physical emergency applications. Future Generation Computer Systems. 2017; 79 ():354-363.
Chicago/Turabian StyleSaurabh Kumar Garg; Jagannath Aryal; Hao Wang; Tejal Shah; Gabor Kecskemeti; Rajiv Ranjan. 2017. "Cloud computing based bushfire prediction for cyber–physical emergency applications." Future Generation Computer Systems 79, no. : 354-363.
With the rise of internet of things (IoT) technology, it is anticipated that large-scale sensor-based systems will permeate society, calling for novel methodologies to design, test, and operate these systems. IoT relies on networked interconnected physical devices often featuring computational capabilities. The sheer number of these interconnected devices plays a key role in the IoT revolution. For example, Gartner research predicts that IoT will connect up to 50 to 100 billion devices by 2020. It is estimated that IoT will generate ~1.7 trillion US dollars in value by 2020 with an approximate growth rate of 20% year over year.
Gabor Kecskemeti; Giuliano Casale; Devki Nandan Jha; Justin Lyon; Rajiv Ranjan. Modelling and Simulation Challenges in Internet of Things. IEEE Cloud Computing 2017, 4, 62 -69.
AMA StyleGabor Kecskemeti, Giuliano Casale, Devki Nandan Jha, Justin Lyon, Rajiv Ranjan. Modelling and Simulation Challenges in Internet of Things. IEEE Cloud Computing. 2017; 4 (1):62-69.
Chicago/Turabian StyleGabor Kecskemeti; Giuliano Casale; Devki Nandan Jha; Justin Lyon; Rajiv Ranjan. 2017. "Modelling and Simulation Challenges in Internet of Things." IEEE Cloud Computing 4, no. 1: 62-69.
In order to minimise their energy use, data centre operators are constantly exploring new ways to construct computing infrastructures. As low power CPUs, exemplified by ARM-based devices, are becoming increasingly popular, there is a growing trend for the large scale deployment of low power servers in data centres. For example, recent research has shown promising results on constructing small scale data centres using Raspberry Pi (RPi) single-board computers as their building blocks. To enable larger scale experimentation and feasibility studies, cloud simulators could be utilised. Unfortunately, state-of-the-art simulators often need significant modification to include such low power devices as core data centre components. In this paper, we introduce models and extensions to estimate the behaviour of these new components in the DISSECT-CF cloud computing simulator. We show that how a RPi based cloud could be simulated with the use of the new models. We evaluate the precision and behaviour of the implemented models using a Hadoop-based application scenario executed both in real life and simulated clouds.
Gabor Kecskemeti; Wajdi Hajji; Fung Po Tso. Modelling Low Power Compute Clusters for Cloud Simulation. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) 2017, 39 -45.
AMA StyleGabor Kecskemeti, Wajdi Hajji, Fung Po Tso. Modelling Low Power Compute Clusters for Cloud Simulation. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). 2017; ():39-45.
Chicago/Turabian StyleGabor Kecskemeti; Wajdi Hajji; Fung Po Tso. 2017. "Modelling Low Power Compute Clusters for Cloud Simulation." 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) , no. : 39-45.
In Internet of Things (IoT), sensors, actuators and smart devices are connected to the Internet. Application providers combine this connectivity with novel scenarios involving cloud computing. Some require in depth analysis of the interaction between IoT devices and clouds. Research focuses on questions like how to govern such large cohort of devices (i.e., often over tens of thousands). Distributed systems simulators help in such analysis, but they are problematic to apply in this newly emerging domain. Most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. This paper introduces our attempt to show how a state of the art simulator could model generic IoT sensors. We show the fundamental properties of IoT entities represented in the simulator. Based on these properties, we present an XML based, declarative modelling language aiming at: (i) describing the behaviour of sensors and their relation to clouds, and (ii) allowing rapid prototyping of simulations. Finally, we validate the applicability of our IoT extensions in five scenarios in the field of weather forecasting.
Andras Markus; Gabor Kecskemeti; Attila Kertesz. Flexible Representation of IoT Sensors for Cloud Simulators. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) 2017, 199 -203.
AMA StyleAndras Markus, Gabor Kecskemeti, Attila Kertesz. Flexible Representation of IoT Sensors for Cloud Simulators. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). 2017; ():199-203.
Chicago/Turabian StyleAndras Markus; Gabor Kecskemeti; Attila Kertesz. 2017. "Flexible Representation of IoT Sensors for Cloud Simulators." 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) , no. : 199-203.
Vincenzo De Maio; Gabor Kecskemeti; Radu Prodan. An improved model for live migration in data centre simulators. Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 2016, 108 -117.
AMA StyleVincenzo De Maio, Gabor Kecskemeti, Radu Prodan. An improved model for live migration in data centre simulators. Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18. 2016; ():108-117.
Chicago/Turabian StyleVincenzo De Maio; Gabor Kecskemeti; Radu Prodan. 2016. "An improved model for live migration in data centre simulators." Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning - IC4E '18 , no. : 108-117.
The emergence of IoT systems introduced new kind of challenges for the designers of such large scale highly distributed systems. The sheer number of participating devices raises a crucial question: how they can be coordinated. Engineers often opt for using a simulator to evaluate new approaches or scenarios in various environments. This raises the second crucial question: how such a large system can be simulated efficiently. Existing simulators (even if they are IoT focused) are often focused on some particular scenarios and not capable to evaluate coordination approaches. In this paper we propose a chemical coordination model and a new extension to the DISSECT-CF cloud simulator. We expect that their combination on one hand ensures a distributed adaptive coordination on the other hand allows the separation of simulation problems into manageable sizes; these enable the analysis of large scale IoT systems with decentralized coordination approaches.
Gabor Kecskemeti; Zsolt Nemeth. Foundations for Simulating IoT Control Mechanisms with a Chemical Analogy. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016, 367 -376.
AMA StyleGabor Kecskemeti, Zsolt Nemeth. Foundations for Simulating IoT Control Mechanisms with a Chemical Analogy. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2016; ():367-376.
Chicago/Turabian StyleGabor Kecskemeti; Zsolt Nemeth. 2016. "Foundations for Simulating IoT Control Mechanisms with a Chemical Analogy." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 367-376.
Cloud computing is based on Virtual Machines (VM) or containers, which provide their own software execution environment that can be deployed by facilitating technologies on top of various physical hardware. The use of VMs or containers represents an efficient way to automatize the overall software engineering and operation life-cycle. Some of the benefits include elasticity and high scalability, which increases the utilization efficiency and decreases the operational costs. VMs or containers as software artifacts are created using provider-specific templates and are stored in proprietary or public repositories for further use. However, technology specific choices may reduce their portability, lead to a vendor lock-in, particularly when applications need to run in federated Clouds. In this paper we present the current state of development of the novel concept of a VM repository and operational environment for federated Clouds named ENTICE. The ENTICE environment has been designed to receive unmodified and functionally complete VM images from its users, and transparently tailor and optimise them for specific Cloud infrastructures with respect to their size, configuration, and geographical distribution, such that they are loaded, delivered, and executed faster and with improved QoS compared to their current behaviour. Furthermore, in this work a specific use case scenario for the ENTICE environment has been provided and the underlying novel technologies have been presented.
Dragi Kimovski; Nishant Saurabh; Sandi Gec; Polona Stefanic; Gabor Kecskemeti; Vlado Stankovski; Radu Prodan; Thomas Fahringer. Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach. 2016 5th IEEE International Conference on Cloud Networking (Cloudnet) 2016, 242 -247.
AMA StyleDragi Kimovski, Nishant Saurabh, Sandi Gec, Polona Stefanic, Gabor Kecskemeti, Vlado Stankovski, Radu Prodan, Thomas Fahringer. Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach. 2016 5th IEEE International Conference on Cloud Networking (Cloudnet). 2016; ():242-247.
Chicago/Turabian StyleDragi Kimovski; Nishant Saurabh; Sandi Gec; Polona Stefanic; Gabor Kecskemeti; Vlado Stankovski; Radu Prodan; Thomas Fahringer. 2016. "Towards an Environment for Efficient and Transparent Virtual Machine Operations: The ENTICE Approach." 2016 5th IEEE International Conference on Cloud Networking (Cloudnet) , no. : 242-247.