Smart cities generate huge volumes of data to be processed by applications with different criticality and requirements. For example, a healthcare application needs lower latency when requested from an ambulance travelling to a hospital during an emergency compared to applications in less-critical domains. Cities can use Multi-access Edge Computing to reduce latency by placing applications’ services closer to users. A service placement process selects the set of servers to run the services for deployment. Smart cities challenge this selection as a large number of servers and services generate a large number of potential solutions with different QoS properties. Additionally, placement approaches must consider applications’ criticality and users’ mobility to offer an appropriate overall latency. Current approaches have considered servers’ utilisation and users’ location to place services. However, they do not consider applications’ criticality and mobile users’ paths. This paper presents MAACO, a Mobility-Aware, priority-driven, ACO-based service placement model that prioritises applications according to their criticality and minimises critical applications’ latency, while considering predicted paths for mobile users. Evaluation results show that MAACO achieves lower latency and waiting time compared against baselines at the cost of reduced load balance between the network servers.

Simulation can be a powerful technique for evaluating the performance of large-scale cloud computing services in a relatively low cost, low risk and time-sensitive manner. Large-scale data indexing, distribution and management is complex to analyse in a timely manner. In this paper, we extend the CloudSim cloud simulation framework to model and simulate a distributed search engine architecture and its workload characteristics. To test the simulation framework, we develop a model based on a real-world ElasticSearch deployment on Linknovate.com. An experimental evaluation of the framework, comparing simulated and actual query response time, precision and resource utilisation, suggests that the proposed framework is capable of predicting performance at different scales in a precise, accurate and efficient manner. The results can assist ElasticSearch users to manage their scalability and infrastructure requirements.

Communication service providers (CSPs) face competitive pressure to increase infrastructure utilization and reduce operational costs while at the same time maximize bandwidth and maintain quality of service for end users. Content Distribution Networks (CDNs) are widely used to achieve these goals by optimally distributing content to cache servers at the edge, access and core networks. More recently, virtualization and cloud computing are being used to deploy and utilize virtual Content Delivery Networks (vCDNs) to reduce costs and increase elasticity while avoiding performance, quality, reliability and availability limitations that characterize traditional CDNs. In order to study the effectiveness of virtual cache placements and optimal distribution over a real-world large-scale vCDN infrastructure, we model and simulate BT's network using a novel parallel simulation framework. Results show that virtual cache placement impacts on the number of accepted requests, resource consumption (compute and network), and end-user network latency. We also present a trade-off analysis between infrastructure provider utility and service customer utility to quantity the number and distribution of these virtual cache placements and to determine the optimal number of virtual cache servers that should be deployed.

The RECAP is a European Union funded project that seeks to develop a next-generation resource management solution, from both technical and business perspectives, when adopting technological solutions spanning across cloud, fog, and edge layers. The RECAP project is composed of a set of use cases that present highly complex and scenario-specific requirements that should be modelled and simulated in order to find optimal solutions for resource management. Due use cases characteristics, configuring simulation scenarios is a high time consuming task and requires staff with specialist expertise.

Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.