# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 1 Introduction and Motivation  
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 are 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 usage 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 hyperscale, 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 hyperscale 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 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.