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1. Automated Microservice Code-Smell Detection

   https://doi.org/10.1007/978-981-33-6385-4_20  

   Walker, A.; Das, D.; Cerny, T. (April 2021, Information Science and Applications. Lecture Notes in Electrical Engineering)

   null; null; null (Ed.)

   Microservice Architecture (MSA) is rapidly taking over modern software engineering and becoming the predominant architecture of new cloud-based applications (apps). There are many advantages to using MSA, but there are many downsides to using a more complex architecture than a typical monolithic enterprise app. Beyond the normal bad coding practices and code-smells of a typical app, MSA specific code-smells are difficult to discover within a distributed app. There are many static code analysis tools for monolithic apps, but no tool exists to offer code-smell detection for MSA-based apps. This paper proposes a new approach to detect code smells in distributed apps based on MSA. We develop an open-source tool, MSANose, which can accurately detect up to eleven different types of MSA specific code smells. We demonstrate our tool through a case study on a benchmark MSA app and verify its accuracy. Our results show that it is possible to detect code-smells within MSA apps using bytecode and or source code analysis throughout the development or before deployment to production. 

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2. Dynamically Balancing Load with Overload Control for Microservices

   https://doi.org/10.1145/3676167  

   Bhattacharya, Ratnadeep; Gao, Yuan; Wood, Timothy (December 2024, ACM Transactions on Autonomous and Adaptive Systems)

   The microservices architecture simplifies application development by breaking monolithic applications into manageable microservices. However, this distributed microservice “service mesh” leads to new challenges due to the more complex application topology. Particularly, each service component scales up and down independently creating load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this article, we propose a self-managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes. BLOC uses overload control approaches to provide feedback to the load balancers. We show that this performs significantly better in solving the incast problem in microservice architectures. A critical component of BLOC is the dynamic capacity estimation algorithm. We show that a well-tuned capacity estimate can outperform even join-the-shortest-queue, a nearly optimal algorithm, while a reasonable dynamic estimate still outperforms Least Connection, a distributed implementation of join-the-shortest-queue. Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 –4 times and the tail, 99th percentile, latency by 2 times. 

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3. Snicket: Query-Driven Distributed Tracing

   https://doi.org/10.1145/3484266.3487393  

   Berg, Jessica; Ruffy, Fabian; Nguyen, Khanh; Yang, Nicholas; Kim, Taegyun; Sivaraman, Anirudh; Netravali, Ravi; Narayana, Srinivas (November 2021, HotNets '21: Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks)

   Increasing application complexity has caused applications to be refactored into smaller components known as microservices that communicate with each other using RPCs. Distributed tracing has emerged as an important debugging tool for such microservice-based applications. Distributed tracing follows the journey of a user request from its starting point at the application's front-end, through RPC calls made by the front-end to different microservices recursively, all the way until a response is constructed and sent back to the user. To reduce storage costs, distributed tracing systems sample traces before collecting them for subsequent querying, affecting the accuracy of queries on the collected traces. We propose an alternative system, Snicket, that tightly integrates querying and collection of traces. Snicket takes as input a database-style streaming query that expresses the analysis the developer wants to perform on the trace data. This query is compiled into a distributed collection of microservice extensions that run as "bumps-in-the-wire," intercepting RPC requests and responses as they flow into and out of microservices. This collection of extensions implements the query, performing early filtering and computation on the traces to reduce the amount of stored data in a query-specific manner. We show that Snicket is expressive in the queries it can support and can update queries fast enough for interactive use. 

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4. M icro P rof : Code-level Attribution of Unnecessary Data Transfer in Microservice Applications

   https://doi.org/10.1145/3622787  

   Tariq, Syed Salauddin; Menard, Lance; Su, Pengfei; Roy, Probir (September 2023, ACM Transactions on Architecture and Code Optimization)

   The microservice architecture style has gained popularity due to its ability to fault isolation, ease of scaling applications, and developer’s agility. However, writing applications in the microservice design style has its challenges. Due to the loosely coupled nature, services communicate with others through standard communication APIs. This incurs significant overhead in the application due to communication protocol and data transformation. An inefficient service communication at the microservice application logic can further overwhelm the application. We perform a grey literature review showing that unnecessary data transfer is a real challenge in the industry. To the best of our knowledge, no effective tool is currently available to accurately identify the origins of unnecessary microservice communications that lead to significant performance overhead and provide guidance for optimization. To bridge the knowledge gap, we propose MicroProf, a dynamic program analysis tool to detect unnecessary data transfer in Java-based microservice applications. At the implementation level, MicroProfproposes novel techniques such as remote object sampling and hardware debug registers to monitor remote object usage. MicroProfreports the unnecessary data transfer at the application source code level. Furthermore, MicroProfpinpoints the opportunities for communication API optimization. MicroProfis evaluated on four well-known applications involving two real-world applications and two benchmarks, identifying five inefficient remote invocations. Guided by MicroProf, API optimization achieves an 87.5% reduction in the number of fields within REST API responses. The empirical evaluation further reveals that the optimized services experience a speedup of up to 4.59 ×. 

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5. Load balancing for interdependent IoT microservices

   Ruozhou Yu, Vishnu Kilari (May 2019, IEEE INFOCOM'2019)

   Advances in virtualization technologies and edge computing have inspired a new paradigm for Internet-of-Things (IoT) application development. By breaking a monolithic application into loosely coupled microservices, great gain can be achieved in performance, flexibility and robustness. In this paper, we study the important problem of load balancing across IoT microservice instances. A key difficulty in this problem is the interdependencies among microservices: the load on a successor microservice instance directly depends on the load distributed from its predecessor microservice instances. We propose a graph-based model for describing the load dependencies among microservices. Based on the model, we first propose a basic formulation for load balancing, which can be solved optimally in polynomial time. The basic model neglects the quality-of-service (QoS) of the IoT application. We then propose a QoS-aware load balancing model, based on a novel abstraction that captures a realization of the application’s internal logic. The QoS-aware load balancing problem is NP-hard. We propose a fully polynomialtime approximation scheme for the QoS-aware problem. We show through simulation experiments that our proposed algorithm achieves enhanced QoS compared to heuristic solutions. 

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