Exploring local service allocation in Community Networks

Abstract

Community Cloud computing is a new trend on cloud computing that aims to build service infrastructures upon Wireless Community Networks taking advantage of underused community physical resources. Service allocation protocols are a key design challenge that all cloud systems must properly address to optimize resource utilization. They are specially important when cloud services require a Quality of Service (QoS) and network stability or performance (delay, jitter, minimum bandwidth) cannot be guaranteed a-priory. This work presents a study that tries to understand how to address cloud service deployments in such scenario. In particular, we start proposing an allocation algorithm to find optimal solutions when there is a central authority that coordinates the process. These solutions optimize the communication cost in two ways: (1) minimizing the service overlay diameter and, (2) minimizing the coordination cost along the network. Based on the study of the algorithm and the experimental simulations, we study the variables that outcome optimal service allocations to the detriment of other solutions. We verify these findings using data mining techniques. Researchers can take advantage of the simulation results and our observations to design more reliable distributed algorithms able to dynamically self-adapt to network changes.

A two-stage Multi-Criteria Optimization method for service placement in decentralized edge micro-clouds

“…The authors evaluate its performance over the CN Guifi.net. Another study about placement in CN is proposed in Vega et al [31], which optimizes the communication cost in CN by minimizing the service overlay diameter and the coordination cost along the network. Coimbra et al [32] propose a novel service placement approach based on community finding using a scalable graph label propagation technique and decentralized election procedure.…”

A Lightweight Service Placement Approach for Community Network Micro-Clouds

“…In [17] the authors propose an optimal allocation solution for ambient intelligence environments using tasks replication to avoid network performance degradation. Some other works done in wireless settings are the work of Vega [40] and our work [31] which proposes several placement algorithms that minimize the coordination and overlay cost along a CN. The work of Coimbra in [11] presents a parallel and distributed solution designed as a scalable alternative for the problem of service placement in CNs.…”

Practical Service Placement Approach for Microservices Architecture

“…Some other works done in wireless settings are the work of Davide [25] and our recent work [7] which proposes several placement algorithms that minimize the coordination and overlay cost along a CN. The focus of the work in this paper is to design a low-complexity service placement heuristic for CN micro-clouds in order to maximise bandwidth and improve user QoS and QoE.…”