# Scalable Dew Computing
## 2 Related Work  
Scalability is the ability of an analyzed system to handle an increasing amount of workload, both in terms of processing power and storage resources, exploiting its potential to be easily expanded to accommodate the growth of demands [23], or tendency to handle various computing resources, including applications and communication infrastructure [15]. Scalable distributed computing systems evolved through the cloud, then later fog, towards dew computing [14]. To be scalable, dew computing must penetrate into sundry networks, vigorous end-user machines, and vibrant dew-system assemblies and disseminate a prominent sensor-aware performance in case of sudden growth in the dew end-users and applications [24]. Scalability of dew computing was expected to be a promising feature without going into depth analysis [25], extracting that extreme scalability is offered in dew computing by processing distributed in multiple local devices in the user's proximity. Dew computing systems can switch between upgrading and downgrading resources according to users' needs, such as switching between multi-core and single-core processors [26]. Scalability and elasticity at the level of edge and dew computing can be provided by realizing a negotiation protocol and assigning tasks to other smart devices on the edge of the network, or even going deeper to the end-user devices [27]. A model of a dew computing solution for IoT devices was developed, addressing vertical scalability to higher-level cloud-based servers [28], to minimize data latency and mitigate the data processing burden on a centralized server. Performance was evaluated on developed real-time processing IIoT communication services applied on a distributed and scalable computing hierarchy [29], also mainly addressing vertical scalability. The DeWatch architecture and framework were introduced to integrate smartwatches on a dew computing level and achieve a more scalable system [23], performing direct communication with nearby dew devices (from desktops, laptops, smartphones, smart home devices, and even with IoT smart sensors and devices) that are in proximity of the smartwatch ported by the user. This platform addresses both the vertical and horizontal scalability aspects and analyzes only the benefits of cloud computing service decentralization, with impacts on Big Data IoT processing. A formal definition of \"less\" concepts is introduced in an earlier paper [30], where \"less\" means that the user is relieved of the need to worry about computing resources, which will be provided by a nearby server, smart device, or an IoT embedded system. Therefore, the serverless concept corresponds to servers in a cloud, and the deviceless corresponds to nearby devices in an IoT environment. In the serverless scenario, the user writes functions and the cloud provider ensures server resources to execute them. In the deviceless scenario, the user writes functions and the provider of the nearby IoT infrastructure ensures device resources to execute them. The serverless platform provider accepts the burden to scale the software as needed in an elastic way to handle any kind of unanticipated load [31], while the users do not worry controlling the way their software is administered. A serverless real-time data analytics platform [32] is recommended for horizontal architectural designs between edge devices, especially for streaming data and integration of IoT. Available serverless implementations include, at least, Amazon Web Services (AWS) Lambda, Azure Functions, and Google Cloud Functions (GCP). Insights into architectures, resource utilization, and the performance evaluation of the scalability show a number of issues and limitations besides the declaration of unlimited elasticity and resource provision of serverless functions [33]. Some even claim that serverless computing moves one step forward and two steps back in a distributed computing environment [34].