Survey on Placement Methods in the Edge and Beyond

Abstract

Edge computing is a (r)evolutionary extension of traditional cloud computing. It expands central cloud infrastructure with execution environments close to the users in terms of latency in order to enable a new generation of cloud applications. This paradigm shift has opened the door for telecommunications operators, mobile and fixed network vendors: they have joined the cloud ecosystem as essential stakeholders considerably influencing the future success of the technology. A key problem in edge computing is the optimal placement of computational units (virtual machines, containers, tasks or functions) of novel distributed applications. These components are deployed to a geographically distributed virtualized infrastructure and heterogeneous networking technologies are invoked to connect them while respecting quality requirements. The optimal hosting environment should be selected based on multiple criteria by novel scheduler algorithms which can cope with the new challenges of distributed cloud architecture where networking aspects cannot be ignored. The research community has dedicated significant efforts to this topic during recent years and a vast number of theoretical results have been published addressing different variants of the related mathematical problems. However, a comprehensive survey focusing on the technical and analytical aspects of the placement problem in various edge architectures is still missing. This survey provides a comprehensive summary and a structured taxonomy of the vast research on placement of computational entities in emerging edge infrastructures. Following the given taxonomy, the research papers are analyzed and categorized according to several dimensions, such as the capabilities of the underlying platforms, the structure of the supported services, the problem formulation, the applied mathematical methods, the objectives and constraints incorporated in the optimization problems, and the complexity of the proposed methods. We summarize the gained insights and important lessons learned, and finally, we reveal some important research gaps in the current literature.

Aerial Computing: A New Computing Paradigm, Applications, and Challenges

“…The role and applications of aerial computing have not been presented. [10]- [12] Reviews of seminal MEC architectures, computation offloading issues, resource management, and optimal placement problems.…”

“…The use of MEC for IoT applications, such as critical and massive IoT, wearable IoT, smart energy, and IoT automotive, was discussed in [9]. Computation offloading, resource management, and optimal placement problems in edge computing systems were discussed in [10]- [12]. The amalgamation of MEC and AI, referred to as edge intelligence and intelligent edge, has been reviewed in several studies [13], [14].…”

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

“…Martinez et al [79] mainly focused on designing and evaluating Fog computing systems and frameworks. Lin et al [64] and Sonkoly et al [111] mainly studied diferent approaches for modeling the resources and communication types for computation oloading in Edge computing. Finally, Islam et al [56] proposed a taxonomy for context-aware scheduling in Fog computing and surveyed the related techniques in terms of contextual information.…”

Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions

“…Martinez et al [86] mainly focused on designing and evaluating Fog computing systems and frameworks. Lin et al [68] and Sonkoly et al [123] mainly studied and categorized different approaches for modeling the resources and communication types for computation offloading in Edge computing. Finally, Islam et al [60] proposed a taxonomy for context-aware scheduling in Fog computing and surveyed the related techniques in terms of contextual information such as user and networking characteristics.…”

“…Besides, the performance of scheduling techniques should be continuously evaluated to offer the best performance. As depicted in Table 1, the existing surveys barely study and provide comprehensive taxonomy for the above-mentioned 3.5 [152] 3 [34] 2.5 [113] 2 [79] 1.5 [118] 1.5 [86] 1.5 [68] 1.5 [60] 1 [123] 0.5 [7] 0.5 This Survey Current : Full Cover, : Partial Cover, : Does Not Cover perspectives. In this work, we identify the main parameters of each perspective and present a taxonomy accordingly.…”