Recently, the software development industry has witnessed a growing trend toward migrating from monolithic systems to microservices. However, identifying microservice candidates from an existing monolith is a primary challenge in this migration process, often proving to be a complex and labor-intensive task. Current methods for identifying microservice candidates have major drawbacks. They fail to adequately cover the various dependencies between different system entities and their relative importance. Additionally, these methods neglect to simultaneously consider important microservice architectural characteristics, such as functional independence, data independence, and granularity. Typically, these identification methods involve graph modeling of system classes, followed by a clustering process to optimize coupling and cohesion between classes. Identifying microservices from such a graph in a large monolith requires significant time and computational power. To address these limitations, this paper proposes a method that utilizes structural, conceptual, behavioral, and database dependencies to identify microservice candidates from monolithic systems. This method simultaneously addresses key characteristics of the microservices architecture and attempts to manage the time cost of identifying microservices. The proposed method has been evaluated using four widely-used open-source projects as case studies, analyzing five metrics in total. The results show that our method outperforms existing approaches across various evaluation metrics.

In recent years, edge computing has emerged as a promising solution for deploying IoT applications that demand minimal latency. By leveraging Function as a Service (FaaS) at the edge, it is possible to achieve efficient and scalable computing capabilities. However, implementing serverless deployment at the edge presents challenges such as auto-scaling, resource management, and mitigating cold-start delays, particularly due to the limited resources available. These challenges are even more significant in workflow-based applications, where tasks are interdependent. This article introduces a dynamic approach for executing serverless workflows at the edge, consisting of three key components: initial function placement, request scheduling, and dynamic adjustment. The initial placement leverages the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to deploy function instances across edge nodes. Request scheduling, on the other hand, distributes requests among these instances using a pattern graph matching algorithm. Finally, the dynamic adjustment component periodically refines placement and scheduling strategies to adapt to changing demands, utilizing a local search technique known as simulated annealing. Evaluation results indicate that the proposed solution reduces the average makespan of workflows by up to 86% compared to state-of-the-art methods.

In a cloud environment, a cloud broker plays a vital role as an intermediary between cloud customers and providers, resolving issues and facilitating negotiations to balance customer preferences and provider profits. Over the past few years, numerous research articles have either directly or indirectly examined this area. Conducting a Systematic Mapping Study (SMS) on cloud brokerage is highly motivating as it offers a high-level overview of the research landscape, identifying trends, topics, gaps, and patterns within this dynamic and crucial field. This article presents an SMS conducted to categorize existing research, highlight underexplored areas, and map out the evolution and current state of cloud brokerage, providing valuable insights for researchers and practitioners. The SMS identified 91 relevant and reputable search spaces (journals and conferences) and 634 high-quality articles published from 2009 to 2022. Furthermore, we formulated and addressed eight significant research questions to clarify various aspects of the cloud broker field. The extracted information from the selected articles is included in a supplementary file, available online, offering valuable insights for research teams and developers interested in this domain.

Summary Edge computing is a new way of computing that uses resources at the edge of a network to solve the problem of communication delays in applications that require immediate responses. This field has received a lot of attention from the research community over the past few decades, leading to a significant increase in publications. To better understand the field, a systematic mapping study (SMS) was conducted using a three‐tier search method that involved defining quality criteria to extract relevant search spaces and studies. This resulted in the selection of 112 search spaces out of 805 and 1440 studies out of 8725. The SMS addressed 8 research questions to identify the main topics, architectures, techniques, and other important aspects of edge computing.

Abstract Bad smells are bad practices in developing software. These poor solutions significantly influence the understandability and maintainability of source code. Therefore, bad smell detection plays a vital role in the refactoring, maintaining, and measuring the quality of large and complex software systems. Researchers believe that bad smells should be precisely identified and addressed. However, bad smell detection is complicated by issues such as informal and inconsistent specifications of bad smells and high false positive rates in the detection process, all of which affect the success rate in detection. In this paper, we present a new method to detect bad smells in code by addressing the aforementioned issues. Our proposed method is a multi‐step process using software quality metrics and refactoring opportunities. In this method, after obtaining the bad smell formal specifications based on software metrics, we utilize them to achieve a set of candidates for each bad smell. Afterwards, each of the instances will be examined and compared with the corresponding refactoring situations specified for that bad smell. This examination strikes out the false positives created in the previous step. The evaluation of this method on four open‐source systems demonstrates the improved effectiveness of bad smell detection in code.

Real-time online gaming via thin clients, which is known as cloud gaming, connects players around the world and allows them to play high-quality games without much processing capacity. Successful deployment of any cloud-gaming solution requires an ultralow-delay cost-efficient design. To this end, it is required to investigate how one can minimize the cloud costs while satisfying the quality of experience (QoE) requirement of users. Here, we aim to present a resource allocation (RA) framework for cloud centers, which benefits from highly virtualized CPU/GPU resources for cost efficiency. Toward this end, we first present an accurate delay model, as the main control parameter for QoE by considering all sources of delay into account. Then, we present a delay-aware cost-minimizing RA scheme and follow a tractable approach to derive the performance bounds on the blocking probability of the system. The simulation results show that the performance of the proposed schemes outperforms the others in cost efficiency while satisfying the delay requirement of the connected users.

Abstract In order to understand the security level of an organization network, detection methods are important to tackle the probable risks of the attackers' malicious activities. Intrusion detection systems, as detection solutions of the defense in depth concept, are one of the main devices to record and analyze suspicious behaviors. Besides the benefits of these systems for security enhancement, they will bring some challenges and issues for security administrators. A large number of raw alerts generated by the intrusion detection systems clearly reflect the need for a novel proactive alert correlation framework to reduce redundant alerts, correlate security incidents, discover and model multi‐step attack scenarios, and track them. Several alert correlation frameworks have been proposed in the literature, but the majority of them address the alert correlation in the offline settings. In this paper, we propose a three‐phase alert correlation framework, which processes the generated alerts in real time, correlates the alerts with the aid of causal knowledge discovery to automatically extract causal relationships between alerts, constructs the attack scenarios using the Bayesian network concept, and predicts the next goal of the attacks using the creating attack prediction rules. Experimental results show that the scalable proposed framework is efficient enough in learning and detecting known and unknown multi‐step attack scenarios without using any predefined knowledge. The results also show that the proposed framework perfectly estimates complex attacks before they can damage the assets of the network. Copyright © 2017 John Wiley & Sons, Ltd.