Elastic Deployment of Software Containers in GeoDistributed Computing Environments Software containers are ever more adopted to manage and execute distributed applications. Indeed, they enable to quickly scale the amount of computing resources by means of horizontal and vertical elasticity. Most of the existing works consider the deployment of containers in centralized data centers. However, to exploit the diffused presence of edgefog computing resources, we need new solutions that deploy containers while also considering their placement on decentralized resources. In this paper, we present a twostep approach that manages the runtime adaptation of containerbased applications deployed over geodistributed virtual machines. In the first step, our approach exploits Reinforcement Learning (RL) solutions to control the horizontal and vertical elasticity of the containers. In the second step, it addresses the container placement by solving a suitable integer linear programming problem or using a networkaware heuristic. A wide set of simulation results shows the benefits and flexibility of the proposed approach, which can satisfy stringent application requirements expressed in terms of response time percentiles.

Characterizing and Balancing the Workloads of SemiContainerized Clouds With the development of Cloud computing, many applications are deployed on the Clouds (either public Clouds or private Clouds) for the elastic resource management. A key challenge here is satisfying the QualityofService requirement of online services that requires short response time while maximizing the resource utilization. While applications should be colocated to improve the resource utilization, the colocation results in performance interference between the colocated applications that hurts the QoS of latencysensitive services. To understand this problem in depth, we characterize and analyze the real businesslevel application colocation scenario based on the trace of an actual Cloud product released by Alibaba. Our observation shows that the datacenter suffers from severe load imbalance that often results in QoS violation of the online services on the servers with heavy workload. To balance the workload, we propose a scheduling algorithm based on the resource reservation and utilization of containers. Based on the comparison between the experiment and the existing operation results, the proposed algorithm reduces the standard deviation of the load on servers from 5.27 to 1.43 (CPU) and 9.83 to 1.93 (Memory).