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 Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

Resource Allocation in Vehicular Cloud Computing

Recently, we have witnessed the emergence of Cloud Computing, a paradigm shift adopted by information technology (IT) companies with a large installed infrastructure base that often goes under-utilized. The unmistakable appeal of cloud computing is that it provides scalable access to computing resources and to a multitude of IT services. Cloud computing and cloud IT services have seen and continue to see a phenomenal adoption rate around the world. Recently, Professor Olariu and his coworkers through series of research introduced a new concept, Vehicular Cloud Computing. A Vehicular Cloud (VC) is a network of vehicles in a parking lot that can provide computation services to users. In this model each vehicle is a computation node. Some of the applications of a VC include a datacenter at the airport, a data cloud in a parking lot, and a datacenter at the mall. The defining difference between vehicular and conventional clouds lies in the distributed ownership and, consequently, the unpredictable availability of computational resources. As cars enter and leave the parking lot, new computational resources become available while others depart, creating a dynamic environment where the task of efficiently assigning jobs to cars becomes very challenging. Our main contribution is a number of scheduling and fault-tolerant job assignment strategies, based on redundancy, that mitigate the effect of resource volatility in vehicular clouds. We offer a theoretical analysis of the expected job completion time in the case where cars do not leave during a checkpoint operation and also in the case where cars may leave while checkpointing is in progress, leading to system failure. A comprehensive set of simulations have shown that our theoretical predictions are accurate. We considered two different environments for scheduling strategy: deterministic and stochastic. In a deterministic environment the arrival and departure of cars are known. This scenario is for environments like universities where employees should be present at work with known schedules and the university rents out its employees\u27 cars as computation nodes to provide services as a vehicular cloud. We presented a scheduling model for a vehicular cloud based on mixed integer linear programming. This work investigates a job scheduling problem involving non-preemptive tasks with known processing time where job migration is allowed. Assigning a job to resources is valid if the job has been executed fully and continuously (no interruption). A job cannot be executed in parallel. In our approach, the determination of an optimal job schedule can be formulated as maximizing the utilization of VC and minimizing the number of job migrations. Utilization can be calculated as a time period that vehicles have been used as computation resources. For dynamic environment in terms of resource availability, we presented a stochastic model for job assignment. We proposed to make job assignment in VC fault tolerant by using a variant of the checkpointing strategy. Rather than saving the state of the computation, at regular times, the state of the computation is only recorded as needed. Also, since we do not assume a central server that stores checkpointed images, like conventional cloud providers do, in our strategy checkpointing is performed by a car and the resulting image is stored by the car itself. Once the car leaves, the image is lost. We consider two scenarios: in the first one, cars do not leave during checkpointing; in the second one, cars may leave during checkpointing, leading to system failure. Our main contribution is to offer theoretical predictions of the job execution time in both scenarios mentioned above. A comprehensive set of simulations have shown that our theoretical predictions are accurate