Scheduling Latency-Sensitive Applications in Edge Computing

Scoca, Vincenzo; Aral, Atakan; Brandić, Ivona; Nicola, Rocco De; Uriarte, Rafael Brundo

doi:10.5220/0006706201580168

Cited by 36 publications

(22 citation statements)

References 15 publications

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“…Different topologies of a graph can be identified and among them, we have respectively: line graph, tree application graph, and Directed Acyclic Graph (DAG). The DAG application topology is the most often used because it models a large range of realistic IoT applications like Video processing [14,22,127], gaming [165], or healthcare [59] applications. Figure 3.a) illustrates an example of DAG application (cognitive assistance application).…”

Section: Application(s) Modelmentioning

confidence: 99%

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An Overview of Service Placement Problem in Fog and Edge Computing

2020

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To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

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Section: Application(s) Modelmentioning

confidence: 99%

“…Proposes a latency-aware application management policy to achieve improvements in network conditions and service QoS. [127] C R , C N Designs a score-based scheduling framework for latency-sensitive applications that maximize the end-users service quality experienced.…”

Section: Categorymentioning

confidence: 99%

An Overview of Service Placement Problem in Fog and Edge Computing

2020

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“…Tasks can be migrated between edge and cloud virtual machines and allows to add a probabilistic network failure model to consider the congestion of the network between the devices. It has been used extensively in the related literature (e.g., [33], [34], and [35]). EdgeCloudSim When the simulation starts, the tasks are served in a chronological order based on their start time and regardless which application they belong to.…”

Section: Simulation Environmentmentioning

confidence: 99%

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

Jaddoa

Sakellari

Panaousis

et al. 2020

Simulation Modelling Practice and Theory

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Computation offloading is one of the primary technological enablers of the Internet of Things (IoT), as it helps address individual devices' resource restrictions. In the past, offloading would always utilise remote cloud infrastructures, but the increasing size of IoT data traffic and the real-time response requirements of modern and future IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network.

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“…An optimization problem is formulated to minimize the system delay and cost. The model presented in [51] focuses on a score-based edge service scheduling algorithm that evaluates both network and computational capabilities of edge nodes and outputs the maximum scoring mapping between services and resources. The aim is to allocate the requested tasks to the best possible nodes reducing the latency and increasing the performance.…”

Section: Related Workmentioning

confidence: 99%

A probabilistic model for assigning queries at the edge

Kolomvatsos

Anagnostopoulos

2019

Computing

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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.

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Scheduling Latency-Sensitive Applications in Edge Computing

Cited by 36 publications

References 15 publications

An Overview of Service Placement Problem in Fog and Edge Computing

An Overview of Service Placement Problem in Fog and Edge Computing

Dynamic decision support for resource offloading in heterogeneous Internet of Things environments

A probabilistic model for assigning queries at the edge

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