Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures

The Internet of Things (IoT) has empowered the development of a plethora of new services, fueled by the deployment of devices located at the edge, providing multiple capabilities in terms of connectivity as well as in data collection and processing. With the inception of the Fog Computing paradigm, aimed at diminishing the distance between edge-devices and the IT premises running IoT services, the perceived service latency and even the security risks can be reduced, while simultaneously optimizing the network usage. When put together, Fog and Cloud computing (recently coined as fog-to-cloud, F2C) can be used to maximize the advantages of future computer systems, with the whole greater than the sum of individual parts. However, the specifics associated with cloud and fog resource models require new strategies to manage the mapping of novel IoT services into the suitable resources. Despite few proposals for service offloading between fog and cloud systems are slowly gaining momentum in the research community, many issues in service placement, both when the service is ready to be executed admitted as well as when the service is offloaded from Cloud to Fog, and vice-versa, are new and largely unsolved. In this paper, we provide some insights into the relevant features about service placement in F2C scenarios, highlighting main challenges in current systems towards the deployment of the next-generation IoT servicesPostprint (author's final draft

The Development of IoT Compression Technique To Cloud

The main problem of data transmission is how to reduce the length of data packet delivery, so it can reduce the time of sending data. One method that can be used to reduce the data size is by compressing the data size. Data compression is a technique for compressing data to get the data with smaller size than the original size so that it can shorten the data exchange timeThis study aims to develop the data compression techniques by modifying and combining the coding and modelling techniques based on the RAKE algorithm. This study testing experiments use 4 different methods in 5 different time-periods to determine the value of the compression, decompression efficiency parameters, and the data transmission time parameters.The result of this study is the data coding technique that using decimal to binary converter data and the modeling technique by calculating the residue from the sensor value will produce data in small sizes and get a compression efficiency value of 45%. For coding techniques using ASCII and modeling techniques with XOR operations will produce bigger size data and the compression efficiency value of 71%. In testing data decompression, the decompression efficiency value of 100%, there is no data loss

Multi-Criteria Decision-Making Approach for Container-based Cloud Applications: The SWITCH and ENTICE Workbenches

Many emerging smart applications rely on the Internet of Things (IoT) to provide solutions to time-critical problems. When building such applications, a software engineer must address multiple Non-Functional Requirements (NFRs), including requirements for fast response time, low communication latency, high throughput, high energy efficiency, low operational cost and similar. Existing modern container-based software engineering approaches promise to improve the software lifecycle; however, they fail short of tools and mechanisms for NFRs management and optimisation. Our work addresses this problem with a new decision-making approach based on a Pareto Multi-Criteria optimisation. By using different instance configurations on various geo-locations, we demonstrate the suitability of our method, which narrows the search space to only optimal instances for the deployment of the containerised microservice.This solution is included in two advanced software engineering environments, the SWITCH workbench, which includes an Interactive Development Environment (IDE) and the ENTICE Virtual Machine and container images portal. The developed approach is particularly useful when building, deploying and orchestrating IoT applications across multiple computing tiers, from Edge-Cloudlet to Fog-Cloud data centres

Blockchain leveraged decentralized IoT eHealth framework

Blockchain technologies recently emerging for eHealth, can facilitate a secure, decentral- ized and patient-driven, record management system. However, Blockchain technologies cannot accommodate the storage of data generated from IoT devices in remote patient management (RPM) settings as this application requires a fast consensus mechanism, care- ful management of keys and enhanced protocols for privacy. In this paper, we propose a Blockchain leveraged decentralized eHealth architecture which comprises three layers: (1) The Sensing layer –Body Area Sensor Networks include medical sensors typically on or in a patient body transmitting data to a smartphone. (2) The NEAR processing layer –Edge Networks consist of devices at one hop from data sensing IoT devices. (3) The FAR pro- cessing layer –Core Networks comprise Cloud or other high computing servers). A Patient Agent (PA) software replicated on the three layers processes medical data to ensure reli- able, secure and private communication. The PA executes a lightweight Blockchain consen- sus mechanism and utilizes a Blockchain leveraged task-offloading algorithm to ensure pa- tient’s privacy while outsourcing tasks. Performance analysis of the decentralized eHealth architecture has been conducted to demonstrate the feasibility of the system in the pro- cessing and storage of RPM data

Take one for the team: on the time efficiency of application-level buffer-aided relaying in edge cloud communication

[Abstract] Background Adding buffers to networks is part of the fundamental advance in data communication. Since edge cloud computing is based on the heterogeneous collaboration network model in a federated environment, it is natural to consider buffer-aided data communication for edge cloud applications. However, the existing studies generally pursue the beneficial features of buffering at a cost of time, not to mention that many investigations are focused on lower-layer data packets rather than application-level communication transactions. Aims Driven by our argument against the claim that buffers “can introduce additional delay to the communication between the source and destination”, this research aims to investigate whether or not (and if yes, to what extent) the application-level buffering mechanism can improve the time efficiency in edge-cloud data transmissions. Method To collect empirical evidence for the theoretical discussion, we built up a testbed to simulate a remote health monitoring system, and conducted both experimental and modeling investigations into the first-in-first-served (FIFS) and buffer-aided data transmissions at a relay node in the system. Results An empirical inequality system is established for revealing the time efficiency of buffer-aided edge cloud communication. For example, given the reference of transmitting the 11th data entity in the FIFS manner, the inequality system suggests buffering up to 50 data entities into one transmission transaction on our testbed. Conclusions Despite the trade-off benefits (e.g., energy efficiency and fault tolerance) of buffering data, our investigation argues that the buffering mechanism can also speed up data transmission under certain circumstances, and thus it would be worth taking data buffering into account when designing and developing edge cloud applications even in the time-critical context.Chilean National Research and Development Agency; 11180905Ministerio de Ciencia e Innovación de España e European Regional Development Fund; RTC-2017-5908-7Ministerio de Ciencia e Innovación de España e European Regional Development Fund; PID2019-105221RB-C41Xunta de Galicia e European Regional Development Fund; ED431C 2017/58Xunta de Galicia e European Regional Development Fund; ED431G 2019/0