Machine learning methods for service placement : a systematic review

With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research

Automating Computational Placement for the Internet of Things

PhD ThesisThe PATH2iot platform presents a new approach to distributed data analytics for Internet of Things applications. It automatically partitions and deploys stream-processing computations over the available infrastructure (e.g. sensors, field gateways, clouds and the networks that connect them) so as to meet non-functional requirements including network limitations and energy. To enable this, the user gives a high-level declarative description of the computation as a set of Event Processing Language queries. These are compiled, optimised, and partitioned to meet the non-functional requirements using a combination of distributed query processing techniques that optimise the computation, and cost models that enable PATH2iot to select the best deployment plan given the non-functional requirements. This thesis describes the resulting PATH2iot system, illustrated with two real-world use cases. First, a digital healthcare analytics system in which sensor battery life is the main non-functional requirement to be optimized. This shows that the tool can automatically partition and distribute the computation across a healthcare wearable, a mobile phone and the cloud - increasing the battery life of the smart watch by 453% when compared to other possible allocations. The energy cost of sending messages over a wireless network is a key component of the cost model, and we show how this can be modelled. Furthermore, the uncertainty of the model is addressed with two alternative approaches: one frequentist and one Bayesian The second use case is one in which an acoustic data analytics for transport monitoring is automatically distributed so as enable it to run over a low-bandwidth LORA network connecting the sensor to the cloud. Overall, the paper shows how the PATH2iot system can automatically bring the benefits of edge computing to the increasing set of IoT applications that perform distributed data analytics