Poster

Abstract

Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies for distributed IoT applications. A fog network consists of communication between resource-constrained fog nodes, which are computational networking storage and acceleration devices. By adopting the microservice architecture, applications are designed as a collection of independent and loosely coupled modular services called microservices installed in application containers. The placement problem is to efficiently allocate limited fog resources to applications with diverse resource requirements. This determines the overall system performance in terms of energy consumption, communication cost, load balancing and others. Placement of microservices can be done in two ways which presents a tradeoff between two placement objectives. The first strategy is placing maximal communicating microservices on each fog node. This keeps the chaining costs between the microservices low, but at the same time, it leads to high utilization at some fog nodes. Also, this strategy may not be feasible due to limited resources at fog nodes. On the other hand, the second strategy is to split communicating microservices over a network of fog nodes. This leads to data exchange between the fog nodes, which is referred to as communication cost. This strategy results in a load-balanced system, but at the same time, increases communication costs. We want low energy consumption at fog nodes and low communication costs of the applications. Placement strategies for cloud computing are generally centralized and not well suited for decentralized fog systems. Therefore distributed solutions with self-organization and management capabilities are required for efficient allocation of fog resources to applications with diverse resource requirements.