Introduction

Today, we are in the midst of a new evolution of the Internet, the Internet of Things (IoT), “a global network and service infrastructure of variable density and connectivity with self-configuring capabilities based on standard and interoperable protocols and formats. IoT consists of heterogeneous things that have identities, physical and virtual attributes, and are seamlessly and securely integrated into the Internet” ^[1]. Forecasts suggest that the number of connected things will continue to explode over the next five years reaching 42 billion and generating 79.4 zettabytes of data ^[2]. This explosion of data is changing the characteristics of Internet traffic fundamentally.

The traditional model of cloud computing was designed and built out using substantially different economic and technical paradigms than IoT, with centralised storage and processing in the cloud and associated economies of scale from centralising these functions in multi-tenant data centers. In contrast, IoT is characterised by extremeness-extremely peaky traffic, extreme heterogeneity of device form, function and latency requirements, as well as extreme geographic distribution of infrastructure from the cloud to the device, the so-called cloud-to-thing (C2T) continuum. To support computing across the C2T continuum, new paradigms of computing have emerged, e.g., fog and edge computing. These new paradigms facilitate the deployment of distributed, latency-aware applications and services by enabling local computing capability on a network-accessible device (thing) or somewhere between the cloud and the edge, the fog ^[3]. The complexity of such distributed infrastructure mandates automated orchestration of applications and end-to-end management of networking, infrastructure and workload placement. This is required to efficiently and dynamically deploy workloads that meet specific targets (many of which may have geographic, latency, or other user profile idiosyncrasies), while at the same time (1) minimising the cost and energy consumption of finite physical hardware resources, and (2) meeting service level agreement (SLA) commitments ^[4].

Designing resource management mechanisms that identify and select resources (resource provisioning), and then map and execute workloads based on those resources and the required service levels (resource scheduling) across the C2T continuum, at hyper-scale, increases complexity at orders of magnitude significantly greater than traditional cloud computing. As such, there is a substantial ongoing effort to review the efficacy of extant resource management mechanisms in the IoT context and propose new mechanisms that can accommodate the specific constraints and requirements of edge and fog computing ^[5]. The evaluation of such mechanisms is not without its challenges. Due to the cost, complexity and risk associated with both experimenting on and replicating modern hyper-scale infrastructure, simulation software tools are used extensively to investigate different aspects of resource management and impact, under different scenarios ^[6]. Even so, such tools only recently evolved to simulate geographically distributed cloud infrastructure, warehouse-scale computing, and heterogeneous resources ^[7].

This article presents a methodical survey of scholarly literature on the use of simulation software tools for evaluating resource management methods across the C2T continuum. There have been a number of literature surveys on (i) resource management in the cloud, and more recently fog and edge computing ^{[5][8][9][10][11][12]}, (ii) open-source cloud simulators ^[13][14], and (iii) simulating fog and edge computing applications ^[15]. These surveys often conflate mathematical modelling techniques with simulation software tools or do not identify or discuss the use of the simulation software tools and techniques in sufficient detail. No dedicated literature survey could be identified on the use of simulation software for evaluating resource management methods across the C2T continuum. Not only has the research base grown in both resource management and simulation software, but the emergence of joint cloud-fog-edge computing use cases requires a methodical literature survey to evaluate and integrate the existing research available in this field. Our survey meets this call. Our contribution is as follows: (1) extending and complementing existing surveys with a dedicated survey on the use of simulation software tools for evaluating resource management methods across the C2T continuum; (2) identifying simulation software tools for researchers to employ or extend in order to implement and evaluate their resource management proposals in this domain; and (3) categorising extant research to enable researchers efficiently identify gaps in the literature and thereby inform relevant research agendas.

Systematic literature review results

Table 1 summarises the selected research works found. As can be seen, there is a significant imbalance in the evaluation of resource management across the C2T continuum. At a high level, every paper included resource scheduling. More specifically, 28 of the 35 papers (80%) focus on resource allocation; it was the primary focus of all papers regardless of C2T layer. This is not wholly unsurprising given that resource allocation is the central task in resource scheduling. This suggests a significant opportunity for research in evaluating resource mapping, resource monitoring and load balancing using simulation, in all layers of the C2T continuum. More research works are also needed With regard to resource provisioning, addressed both detection and selection tasks. Furthermore, there were only ten fog and edge computing articles in total and the remainder of the articles focus on the cloud layer. 

Table 1: Selected articles: Comparative study (* denotes an extension or variant of CloudSim). 

 Discussions and final considerations

This paper provides two key contributions to the research community. First, to the best of our knowledge, this is the first methodical review of its kind on the evaluation of resource management mechanisms across the C2T continuum that focuses exclusively on studies using simulation software. Second, it categorises extant research and highlights areas where future research contributions in resource management, (ii) cloud, fog and edge computing and their interaction, and (iii) simulation, are required. Our suggestions for research contributions are based on the idea that every key task in resource provisioning and resource scheduling is important for investigation for the C2T continuum and the future IoT. This review also provides an insight into one part of the simulation, resource management, and cloud, fog, and edge computing research community, and how it is evolving. Finally, it is worth noting that the Internet of Things has the potential to change how society interacts and operates. Therefore, it will be useful for future research efforts to combine insights from other disciplines into these research communities and research agendas.