Data transformation as a means towards dynamic data storage and polyglot persistence

Legacy applications have been built around the concept of storing their data in one relational data store. However, with the current differentiation in data store technologies as a consequence of the NoSQL paradigm, new and possibly more performant storage solutions are available to all applications. The concept of dynamic storage makes sure that application data are always stored in the most optimal data store at a given time to increase application performance. Additionally, polyglot persistence aims to push this performance even further by storing each different data type of an application in the data store technology best suited for it. To get legacy applications into dynamic storage and polyglot persistence, schema and data transformations between data store technologies are needed. This usually infers application redesigns as well to support the new data stores. This paper proposes such a transformation approach through a canonical model. It is based on the Lambda architecture to ensure no application downtime is needed during the transformation process, and after the transformation, the application can continue to query in the original query language, thus requiring no application code changes

Split and Migrate: Resource-Driven Placement and Discovery of Microservices at the Edge

Microservices architectures combine the use of fine-grained and independently-scalable services with lightweight communication protocols, such as REST calls over HTTP. Microservices bring flexibility to the development and deployment of application back-ends in the cloud. Applications such as collaborative editing tools require frequent interactions between the front-end running on users\u27 machines and a back-end formed of multiple microservices. User-perceived latencies depend on their connection to microservices, but also on the interaction patterns between these services and their databases. Placing services at the edge of the network, closer to the users, is necessary to reduce user-perceived latencies. It is however difficult to decide on the placement of complete stateful microservices at one specific core or edge location without trading between a latency reduction for some users and a latency increase for the others. We present how to dynamically deploy microservices on a combination of core and edge resources to systematically reduce user-perceived latencies. Our approach enables the split of stateful microservices, and the placement of the resulting splits on appropriate core and edge sites. Koala, a decentralized and resource-driven service discovery middleware, enables REST calls to reach and use the appropriate split, with only minimal changes to a legacy microservices application. Locality awareness using network coordinates further enables to automatically migrate services split and follow the location of the users. We confirm the effectiveness of our approach with a full prototype and an application to ShareLatex, a microservices-based collaborative editing application

Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms

(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] The wide adoption of microservices architectures has introduced an unprecedented granularisation of computing that requires the coordinated execution of multiple containers with diverse lifetimes and with potentially different auto-scaling requirements. These applications are managed by means of container orchestration platforms and existing centralised approaches for auto-scaling face challenges when used for the timely adaptation of the elasticity required for the different application components. This paper studies the impact of integrating bio-inspired approaches for dynamic distributed auto-scaling on container orchestration platforms. With a focus on running self-managed containers, we compare alternative configuration options for the container life cycle. The performance of the proposed models is validated through simulations subjected to both synthetic and real-world workloads. Also, multiple scaling options are assessed with the purpose of identifying exceptional cases and improvement areas. Furthermore, a nontraditional metric for scaling measurement is introduced to substitute classic analytical approaches. We found out connections for two related worlds (biological systems and software container elasticity procedures) and we open a new research area in software containers that features potential self-guided container elasticity activities.This work was supported by the Ministerio de Economía, Industria y Competitividad, Spanish Government, for the Project BigCLOE under Grant TIN2016-79951-RHerrera, J.; Moltó, G. (2020). Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms. IEEE Access. 8:52139-52150. https://doi.org/10.1109/ACCESS.2020.2980852S5213952150

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Leveraging Distributed Tracing and Container Cloning for Replay Debugging of Microservices

Microservice architectures have gained prominence in recent years for building large-scale industrial distributed systems. However, microservice architectures make the usage of replay debugging, a powerful technique for finding root causes of faults, very challenging because of the polyglot (written in several languages) services, large accumulated state of services, and tight latency limits imposed by long hop-chains. This work attempts to provide a framework for enabling replay debugging in production microservice applications. We study 25 real-world faults in microservice systems collected from diverse sources, categorize these faults by fault symptoms, and create 15 application agnostic mutation operators for microservices. We then propose a language agnostic replay debugging framework for microservice applications that uses a distributed tracing system to record network requests and enables replay of those requests on cloned service containers running in a debug environment. A key component of this framework is an anomaly detector that uses span-level and container-level monitoring to detect fault symptoms found in our study and localizes faults to trace level so that faulty traces can be easily replayed to find the root cause. An open-source microservices application injected successively with the mutation operators is used for an evaluation that shows that our framework is upto an order of magnitude lighter-weight than language-specific recording tools such as Chrome DevTools or VisualVM and can help in finding root causes of 9 out of 15 mutations at a line or function level

Tiarrah Computing: The Next Generation of Computing

The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge