Connected objects in the Internet of Things (IoT) domain are widespread everywhere. They interact with each other and cooperate with their neighbors to achieve a common goal. Most of these objects generate a huge amount of data, often requiring a process under strict time constraints. Being motivated by the question of optimizing the execution time of these IoT tasks, we remain aware of the sensitivity to latency and the volume of data generated. In this article, we propose a hybrid Cloud-Fog multi-agent approach to schedule a set of dependent IoT tasks modeled as a workflow. The major advantage of our approach is to allow to model IoT workflow planning as a multi-objetif optimization problem in order to create a compromise planning solution in terms of response time, cost and makespan. In addition to taking into account data communications between workflow tasks, during the planning process, our approach has two other advantages: (1) maximizing the use of Fog Computing in order to minimize response time, and (2) the use of elastic cloud computing resources at minimum cost. The implementation of the MAS-GA (Multi-Agent System based Genetic Algorithm), which we have proposed in this context; the series of experiments carried out on different corpora, as well as the analysis of the found results confirm the feasibility of our approach and its performance in terms of cost which represents an average gain of 21.38% compared to Fog and 25.49% compared to Cloud, makespan which represents a gain of 14.13% compared to Fog and a slight increase of 5.24% compared to Cloud and in response time which represents an average gain of 46.66% compared to Cloud with a slight increase of 6.66% compared to Fog, while strengthening the collaboration between Fog computing and Cloud computing.

Cooperative agents-based approach for workflow scheduling on fog-cloud computing

Future Data Center Energy-Conservation and Emission-Reduction Technologies in the Context of Smart and Low-Carbon City Construction

Energy aware fuzzy approach for placement and consolidation in cloud data centers

Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism

Abstract Distributed service applications make heavy use of clouds and multi-clouds, and must (i) meet service quality goals (e.g. response time) while (ii) satisfying cloud resource constraints and (iii) conserving power. Deployment algorithms must (iv) provide a solution meeting these requirements within a short time to be useful in practice. Very few existing deployment methods address the first three requirements, and those that do take too long to find a deployment. The Low-Power Multi-Cloud Application Deployment (LPD) algorithm fills this gap with a low-complexity heuristic combination of generalized graph partitioning between clouds, bin-packing within each cloud and queueing approximations to control the response time. LPD has no known competitor that quickly finds a solution that satisfies response time bounds. A host execution time approximation for contention is fundamental to achieving sufficient solution speed. LPD is intended for use by cloud managers who must simultaneously manage hosts and application deployments and plan capacity to offer services such as Serverless Computing. On 104 test scenarios deploying up to 200 processes with up to 240 replicas (for scaling), LPD always produced a feasible solution within 100 s (within 20 seconds in over three-quarters of cases). Compared to the Mixed Integer Program solution by CPLEX (which took a lot longer and was sometimes not found) LPD solutions gave power consumption equal to MIP in a third of cases and within 6% of MIP in 95% of cases. In 93% of all 104 cases the power consumption is within 20% of an (unachievable) lower bound. LPD is intended as a stand-alone heuristic to meet solution time restrictions, but could easily be adapted for use as a repair mechanism in a Genetic Algorithm. 

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Leila Helali

Papers

A survey of data center consolidation in cloud computing systems