Cost-Aware IoT Extension of DISSECT-CF

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

Multi-cloud management strategies for simulating IoT applications

Currently, the Internet of Things (IoT) paradigm is closely coupled with Cloud technologies, and the support for managing IoT data is one of the primary concerns of Cloud Computing. In IoT Cloud systems, sensors and different smart devices are connected to the cloud, and large amounts of data are generated by these things that need to be managed by infrastructure Cloud services. Simulation platforms have the advantage of enabling investigations of complex systems without the need of purchasing and installing physical resources. In our previous works, we chose the DISSECT-CF simulator to examine IoT Cloud systems, and developed cost-aware policies for managing IoT and Cloud components. The aim of this paper is to further extend the simulation capabilities of this tool by introducing multi-cloud management and selection approaches. We detail our proposed method for the extension, and evaluate multi-cloud utilization through a meteorological case study

Cost-efficient Datacentre Consolidation for Cloud Federations

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

Multi-cloud management strategies for simulating IoT applications

The Internet of Things (IoT) paradigm is closely coupled with cloud technologies, and the support for managing sensor data is one of the primary concerns of Cloud Computing. IoT-Cloud systems are widely used to manage sensors and different smart devices connected to the cloud, hence a large amount of data is generated by these things that need to be efficiently stored and processed. Simulation platforms have the advantage of enabling the investigation of complex systems without the need of purchasing and installing physical resources. In our previous work, we chose the DISSECT-CF simulator to model IoT-Cloud systems, and we also introduced provider pricing models to enable cost-aware policies for experimentation. The aim of this paper is to further extend the simulation capabilities of this tool by enabling multi-cloud resource management. In this paper we introduce four cloud selection strategies aimed to reduce application execution time and utilization costs. We detail our proposed method towards multi-cloud extension, and evaluate the defined strategies through scenarios of a meteorological application

A parallel event system for large-scale cloud simulations in DISSECT-CF

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