No abstract

Today's distributed computing infrastructures encompass complex workflows for real-time data gathering, transferring, storage, and processing, quickly overwhelming centralized cloud centers. Recently, the computing continuum that federates the Cloud services with emerging Fog and Edge devices represents a relevant alternative for supporting the next-generation data processing workflows. However, eminent challenges in automating data processing across the computing continuum still exist, such as scheduling heterogeneous devices across the Cloud, Fog, and Edge layers.We propose a new scheduling algorithm called C 3 -MATCH, based on matching theory principles, involving two sets of players negotiating different utility functions: 1) workflow microservices prefering computing devices with lower data processing and queuing times; 2) computing continuum devices prefering microservices with corresponding resource requirements and less data transmission time. We evaluate C 3 -MATCH using realworld road sign inspection and sentiment analysis workflows on a federated computing continuum across four Cloud, Fog, and Edge providers. Our combined simulation and real execution results reveal that C 3 -MATCH achieves up to 67% lower completion time than three state-of-the-art methods with 10 ms-1000 ms higher transmission time.

Fog computing platforms became essential for deploying low-latency applications at the network's edge. However, placing and managing time-critical applications over a Fog infrastructure with many heterogeneous and resource-constrained devices over a dynamic network is challenging. This paper proposes an incremental multilayer resource-aware partitioning (M-RAP) method that minimizes resource wastage and maximizes service placement and deadline satisfaction in a dynamic Fog with many application requests. M-RAP represents the heterogeneous Fog resources as a multilayer graph, partitions it based on the network structure and resource types, and constantly updates it upon dynamic changes in the underlying Fog infrastructure. Finally, it identifies the device partitions for placing the application services according to their resource requirements, which must overlap in the same low-latency network partition. We evaluated M-RAP through extensive simulation and two applications executed on a real testbed. The results show that M-RAP can place 1.6 times as many services, satisfy deadlines for 43 % more applications, lower their response time by up to 58 %, and reduce resource wastage by up to 54 % compared to three state-of-the-art methods.

The accelerating growth of modern distributed applications with low delivery deadlines leads to a paradigm shift towards the multi-tier computing continuum. However, the geographical dispersion, heterogeneity, and availability of the continuum resources may result in failures and quality of service degradation, significantly negating its advantages and lowering users' satisfaction. We propose in this paper a proactive application placement (PROS) method relying on distributed coordination to prevent the quality of service violations through service-level agreements on the computing continuum. PROS employs a sigmoid function with adaptive weights for the different parameters to predict the service level agreement assurance of devices based on their past credentials and current capabilities. We evaluate PROS using two application workloads with different traffic stress levels up to 90 million services on a real testbed with 600 heterogeneous instances deployed over eight geographical locations. The results show that PROS increases the success rate by 7 %-33 %, reduces the response time by 16 %-38 %, and increases the deadline satisfaction rate by 19 %-42 % compared to two related work methods. A comprehensive simulation study with 1000 devices and a workload of up to 670 million services confirm the scalability of the results.

With the rapidly increasing popularity of social media applications, decentralized control and ownership is taking more attention to preserve user's privacy. However, the lack of central control in the decentralized social network poses new issues of collaborative decision making and trust to this permission-less environment. To tackle these problems and fulfill the requirements of social media services, there is a need for intelligent mechanisms integrated to the decentralized social media that consider trust in various aspects according to the requirement of services. In this paper, we describe an adaptive microservice-based design capable of finding relevant communities and accurate decision making by extracting semantic information and applying role-stage model while preserving anonymity. We apply this information along with exploiting Pareto solutions to estimate the trust in accordance with the quality of service and various conflicting parameters, such as accuracy, timeliness, and latency.