A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

A Decentralized Latency-Aware Task Allocation and Group Formation Approach with Fault Tolerance for IoT Applications

© 2013 IEEE. Development of internet of things (IoT) and smart devices eased life by offering numerous applications targeting to provide real-time low latency services, but they also brought challenges in handling huge data generated from the powerful computations, to get a job done. Decentralized edge computing could help to achieve latency requirements of the applications by executing them closer to the user at edge of network, but most of the current studies actually deployed centralized approaches for cluster computing at edge, which put extra overhead of cluster formation and management. In this article, we propose to group heterogeneous edge nodes on task arrival with a more decentralized method and execute tasks in parallel to meet their deadline. On the other hand, to guarantee successful execution of critical IoT application running in an edge network, fault tolerance has to be significantly considered. For resource limited edge devices, there is a great need for efficient fault tolerance techniques, which can provide reliability based on device's local information, without worrying about overall network topology. In this article, our novel method is to increase task reliability being executed in distributed edge computing environment through finding reliability of an edge node locally, and by providing fault tolerance to increase overall application availability. Our proposed fault tolerance technique works in decentralized mode by executing new algorithms to handle above mentioned problems. Our experiment results show that our approach is effective as well as providing desired goals of achieving deadline for latency-aware IoT applications, with staggering decrease in overall network traffic along with ensuring reliability and availability

Nomadic fog storage

Mobile services incrementally demand for further processing and storage. However, mobile devices are known for their constrains in terms of processing, storage, and energy. Early proposals have addressed these aspects; by having mobile devices access remote clouds. But these proposals suffer from long latencies and backhaul bandwidth limitations in retrieving data. To mitigate these issues, edge clouds have been proposed. Using this paradigm, intermediate nodes are placed between the mobile devices and the remote cloud. These intermediate nodes should fulfill the end users’ resource requests, namely data and processing capability, and reduce the energy consumption on the mobile devices’ batteries. But then again, mobile traffic demand is increasing exponentially and there is a greater than ever evolution of mobile device’s available resources. This urges the use of mobile nodes’ extra capabilities for fulfilling the requisites imposed by new mobile applications. In this new scenario, the mobile devices should become both consumers and providers of the emerging services. The current work researches on this possibility by designing, implementing and testing a novel nomadic fog storage system that uses fog and mobile nodes to support the upcoming applications. In addition, a novel resource allocation algorithm has been developed that considers the available energy on mobile devices and the network topology. It also includes a replica management module based on data popularity. The comprehensive evaluation of the fog proposal has evidenced that it is responsive, offloads traffic from the backhaul links, and enables a fair energy depletion among mobiles nodes by storing content in neighbor nodes with higher battery autonomy.Os serviços móveis requerem cada vez mais poder de processamento e armazenamento. Contudo, os dispositivos móveis são conhecidos por serem limitados em termos de armazenamento, processamento e energia. Como solução, os dispositivos móveis começaram a aceder a estes recursos através de nuvens distantes. No entanto, estas sofrem de longas latências e limitações na largura de banda da rede, ao aceder aos recursos. Para resolver estas questões, foram propostas soluções de edge computing. Estas, colocam nós intermediários entre os dispositivos móveis e a nuvem remota, que são responsáveis por responder aos pedidos de recursos por parte dos utilizadores finais. Dados os avanços na tecnologia dos dispositivos móveis e o aumento da sua utilização, torna-se cada mais pertinente a utilização destes próprios dispositivos para fornecer os serviços da nuvem. Desta forma, o dispositivo móvel torna-se consumidor e fornecedor do serviço nuvem. O trabalho atual investiga esta vertente, implementado e testando um sistema que utiliza dispositivos móveis e nós no “fog”, para suportar os serviços móveis emergentes. Foi ainda implementado um algoritmo de alocação de recursos que considera os níveis de energia e a topologia da rede, bem como um módulo que gere a replicação de dados no sistema de acordo com a sua popularidade. Os resultados obtidos provam que o sistema é responsivo, alivia o tráfego nas ligações no core, e demonstra uma distribuição justa do consumo de energia no sistema através de uma disseminação eficaz de conteúdo nos nós da periferia da rede mais próximos dos nós consumidores

A probabilistic model for assigning queries at the edge

Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results

Elastic computation placement in edge-based environments

Today, technologies such as machine learning, virtual reality, and the Internet of Things are integrated in end-user applications more frequently. These technologies demand high computational capabilities. Especially mobile devices have limited resources in terms of execution performance and battery life. The offloading paradigm provides a solution to this problem and transfers computationally intensive parts of applications to more powerful resources, such as servers or cloud infrastructure. Recently, a new computation paradigm arose which exploits the huge amount of end-user devices in the modern computing landscape - called edge computing. These devices encompass smartphones, tablets, microcontrollers, and PCs. In edge computing, devices cooperate with each other while avoiding cloud infrastructure. Due to the proximity among the participating devices, the communication latencies for offloading are reduced. However, edge computing brings new challenges in form of device fluctuation, unreliability, and heterogeneity, which negatively affect the resource elasticity. As a solution, this thesis proposes a computation placement framework that provides an abstraction for computation and resource elasticity in edge-based environments. The design is middleware-based, encompasses heterogeneous platforms, and supports easy integration of existing applications. It is composed of two parts: the Tasklet system and the edge support layer. The Tasklet system is a flexible framework for computation placement on heterogeneous resources. It introduces closed units of computation that can be tailored to generic applications. The edge support layer handles the characteristics of edge resources. It copes with fluctuation and unreliability by applying reactive and proactive task migration. Furthermore, the performance heterogeneity and the consequent bottlenecks are handled by two edge-specific task partitioning approaches. As a proof of concept, the thesis presents a fully-fledged prototype of the design, which is evaluated comprehensively in a real-world testbed. The evaluation shows that the design is able to substantially improve the resource elasticity in edge-based environments

Distributed Computing in a Cloud of Mobile Phones

For the past few years we have seen an exponential growth in the number of mobile devices and in their computation, storage and communication capabilities. We also have seen an increase in the amount of data generated by mobile devices while performing common tasks. Additionally, the ubiquity associated with these mobile devices, makes it more reasonable to start thinking in a different use for them, where they will begin to act as an important part in the computation of more demanding applications, rather than relying exclusively on external servers to do it. It is also possible to observe an increase in the number of resource demanding applications, whereas these resort to the use of services, offered by infrastructure Clouds. However, with the use of these Cloud services, many problems arise, such as: the considerable use of energy and bandwidth, high latency values, unavailability of connectivity infrastructures, due to the congestion or the non existence of it. Considering all the above, for some applications it starts to make sense to do part or all the computation locally in the mobile devices themselves. We propose a distributed computing framework, able to process a batch or a stream of data, which is being generated by a cloud composed of mobile devices, that does not require Internet services. Differently from the current state-of-the-art, where both computation and data are offloaded to mobile devices, our system intends to move the computation to where the data is, reducing significantly the amount of data exchanged between mobile devices. Based on the evaluation performed, both on a real and simulated environment, our framework has proved to support scalability, by benefiting significantly from the usage of several devices to handle computation, and by supporting multiple devices submitting computation requests while not having a significant increase in the latency of a request. It also proved that is able to deal with churn without being highly penalized by it