Mohammad Saiful Islam

Mohammad Saiful Islam, Mohamed Sami Rakha, William Pourmajidi et al.

As Large-Scale Cloud Systems (LCS) become increasingly complex, effective anomaly detection is critical for ensuring system reliability and performance. However, there is a shortage of large-scale, real-world datasets available for benchmarking anomaly detection methods. To address this gap, we introduce a new high-dimensional dataset from IBM Cloud, collected over 4.5 months from the IBM Cloud Console. This dataset comprises 39,365 rows and 117,448 columns of telemetry data. Additionally, we demonstrate the application of machine learning models for anomaly detection and discuss the key challenges faced in this process. This study and the accompanying dataset provide a resource for researchers and practitioners in cloud system monitoring. It facilitates more efficient testing of anomaly detection methods in real-world data, helping to advance the development of robust solutions to maintain the health and performance of large-scale cloud infrastructures.

Mohammad Saiful Islam, William Pourmajidi, Lei Zhang et al.

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously. After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages. In this paper, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.

Mohammad Saiful Islam, Andriy Miranskyy

Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multi-dimensional time series and achieved high performance.

Mehmet Kuzu, Mohammad Saiful Islam, Murat Kantarcioglu

Nowadays, huge amount of documents are increasingly transferred to the remote servers due to the appealing features of cloud computing. On the other hand, privacy and security of the sensitive information in untrusted cloud environment is a big concern. To alleviate such concerns, encryption of sensitive data before its transfer to the cloud has become an important risk mitigation option. Encrypted storage provides protection at the expense of a significant increase in the data management complexity. For effective management, it is critical to provide efficient selective document retrieval capability on the encrypted collection. In fact, considerable amount of searchable symmetric encryption schemes have been designed in the literature to achieve this task. However, with the emergence of big data everywhere, available approaches are insufficient to address some crucial real-world problems such as scalability. In this study, we focus on practical aspects of a secure keyword search mechanism over encrypted data on a real cloud infrastructure. First, we propose a provably secure distributed index along with a parallelizable retrieval technique that can easily scale to big data. Second, we integrate authorization into the search scheme to limit the information leakage in multi-user setting where users are allowed to access only particular documents. Third, we offer efficient updates on the distributed secure index. In addition, we conduct extensive empirical analysis on a real dataset to illustrate the efficiency of the proposed practical techniques.