Abstract
As microservice architecture is becoming more popular than ever, developers intend to transform traditional monolithic applications into service-based applications (composed by a number of services). To deploy a service-based application in clouds, besides the resource demands of each service, the traffic demands between collaborative services are crucial for the overall performance. Poor handling of the traffic demands can result in severe performance degradation, such as high response time and jitter. However, current cluster schedulers fail to place services at the best possible machine, since they only consider the resource constraints but ignore the traffic demands between services. To address this problem, we propose a new approach to optimize the placement of service-based applications in clouds. The approach first partitions the application into several parts while keeping overall traffic between different parts to a minimum and then carefully packs the different parts into machines with respect to their resource demands and traffic demands. We implement a prototype scheduler and evaluate it with extensive experiments on testbed clusters. The results show that our approach outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic.
Highlights
Microservice architecture is a new trend rising fast for application development, as it enhances flexibility to incorporate different technologies, it reduces complexity by using lightweight and modular services, and it improves overall scalability and resilience of the system
We implement a prototype scheduler based on our proposed algorithms and evaluate it on testbed clusters
k partition (KP)-heuristic packing (HP), we find that binary partition (BP)-HP performs slightly better and more stable than KP-HP, but KP-HP may find a better solution in some cases
Summary
Microservice architecture is a new trend rising fast for application development, as it enhances flexibility to incorporate different technologies, it reduces complexity by using lightweight and modular services, and it improves overall scalability and resilience of the system. When deploying a service-based application in clouds, the scheduler has to carefully schedule each service, which may have diverse resource demands, on distributed compute clusters. In order to achieve a desired performance of a service-based application, cluster schedulers have to carefully place each service of the application with respect to the resource demands and traffic demands. Firmament [5], a centralized cluster scheduler, can make high-quality placement decisions on large-scale clusters via a min-cost max-flow optimization These solutions would face difficulties for handling service-based applications, as they ignore the traffic demands when making placement decisions. The objective is to minimize the inter-machine traffic while satisfying multi-resource demands for service-based applications. The results show that our scheduler outperforms existing container cluster schedulers and representative heuristics, leading to much less overall inter-machine traffic
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