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Mujahid Sultan

Mujahid Sultan appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Software EngineeringArtificial IntelligenceDatabasesMachine LearningNetworking and Internet Architecture

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Published work

5 published item(s)

preprint2026arXiv

NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents

We present NeuSymMS, an adaptive memory system that enables large language model (LLM) agents to learn, remember, and reason about users across sessions via a hybrid neuro-symbolic architecture. NeuSymMS couples neural fact extraction from unstructured dialogue with a CLIPS-based expert system that classifies, deduplicates, and reconciles facts under explicit lifecycle rules. The system represents knowledge as subject-relation-value triples stored in relational database management system, supports user/agents/agent-to-agents scoping, and implements a dual-horizon short-term/long-term memory model with access-based promotion and time-based pruning. NeuSymMS maintains continuity of memory while avoiding context-window bloat and cross-entity contamination. We argue that this architecture offers a practical path to trustworthy, auditable memory for production agentic systems and discuss its novelty relative to log retrieval, summarization, and key-value approaches.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Designing knowledge plane to optimize leaf and spine data center

In the last few decades, data center architecture evolved from the traditional client-server to access-aggregation-core architectures. Recently there is a new shift in the data center architecture due to the increasing need for low latency and high throughput between server-to-server communications, load balancing and, loop-free environment. This new architecture, known as leaf and spine architecture, provides low latency and minimum packet loss by enabling the addition and deletion of network nodes on demand. Network nodes can be added or deleted from the network based on network statistics like link speed, packet loss, latency, and throughput. With the maturity of Open Virtual Switch (OvS) and OpenFlow based Software Defined Network (SDN) controllers, network automation through programmatic extensions has become possible based on network statistics. The separation of the control plane and data plane has enabled automated management of network and Machine Learning (ML) can be applied to learn and optimize the network. In this publication, we propose the design of an ML-based approach to gather network statistics and build a knowledge plane. We demonstrate that this knowledge plane enables data center optimization using southbound APIs and SDN controllers. We describe the design components of this approach - using a network simulator and show that it can maintain the historical patterns of network statistics to predict future growth or decline. We also provide an open-source software that can be utilized in a leaf and spine data center to provide elastic capacity based on load forecasts.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Linking Stakeholders' Viewpoint Concerns and Microservices-based Architecture

Widespread adoption of agile project management, independent delivery with microservices, and automated deployment with DevOps has tremendously speedup the systems development. The real game-changer is continuous integration (CI), continuous delivery, and continuous deployment (CD). Organizations can do multiple releases a day, shortening the test, release, and deployment cycles from weeks to minutes. Maturity of container technologies like Docker and container orchestration platforms like Kubernetes has promoted microservices architecture, especially in the cloud-native developments. Various tools are available for setting up CI/CD pipelines. Organizations are moving away from monolith applications and moving towards microservices-based architectures. Organizations can quickly accumulate hundreds of such microservices accessible via application programming interfaces (APIs). The primary purpose of these modern methodologies is agility, speed, and reusability. While DevOps offers speed and time to market, agility and reusability may not be guaranteed unless microservices and API's are linked to enterprise-wide stakeholders' needs. The link between business needs and microservices/APIs is not well captured nor adequately defined. In this publication, we describe a structured method to create a logical link among APIs and microservices-based agile developments with enterprise stakeholders' needs and viewpoint concerns. This method enables capturing and documenting enterprise-wide stakeholders' needs, whether these are business owners, planners (product owners, architects), designers (developers, DevOps engineers), or the partners and subscribers of an enterprise.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Probabilistic Partitive Partitioning (PPP)

Clustering is a NP-hard problem. Thus, no optimal algorithm exists, heuristics are applied to cluster the data. Heuristics can be very resource-intensive, if not applied properly. For substantially large data sets computational efficiencies can be achieved by reducing the input space if a minimal loss of information can be achieved. Clustering algorithms, in general, face two common problems: 1) these converge to different settings with different initial conditions and; 2) the number of clusters has to be arbitrarily decided beforehand. This problem has become critical in the realm of big data. Recently, clustering algorithms have emerged which can speedup computations using parallel processing over the grid but face the aforementioned problems. Goals: Our goals are to find methods to cluster data which: 1) guarantee convergence to the same settings irrespective of the initial conditions; 2) eliminate the need to establish the number of clusters beforehand, and 3) can be applied to cluster large datasets. Methods: We introduce a method that combines probabilistic and combinatorial clustering methods to produce repeatable and compact clusters that are not sensitive to initial conditions. This method harnesses the power of k-means (a combinatorial clustering method) to cluster/partition very large dimensional datasets and uses the Gaussian Mixture Model (a probabilistic clustering method) to validate the k-means partitions. Results: We show that this method produces very compact clusters that are not sensitive to initial conditions. This method can be used to identify the most 'separable' set in a dataset which increases the 'clusterability' of a dataset. This method also eliminates the need to specify the number of clusters in advance.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Ordering stakeholder viewpoint concerns for holistic and incremental Enterprise Architecture: the W6H framework

Context: Enterprise Architecture (EA) is a discipline which has evolved to structure the business and its alignment with the IT systems. One of the popular enterprise architecture frameworks is Zachman framework (ZF). This framework focuses on describing the enterprise from six viewpoint perspectives of the stakeholders. These six perspectives are based on English language interrogatives 'what', 'where', 'who', 'when', 'why', and 'how' (thus the term W5H Journalists and police investigators use the W5H to describe an event. However, EA is not an event, creation and evolution of EA challenging. Moreover, the ordering of viewpoints is not defined in the existing EA frameworks, making data capturing process difficult. Our goals are to 1) assess if W5H is sufficient to describe modern EA and 2) explore the ordering and precedence among the viewpoint concerns. Method: we achieve our goals by bringing tools from the Linguistics, focusing on a full set of English Language interrogatives to describe viewpoint concerns and the inter-relationships and dependencies among these. Application of these tools is validated using pedagogical EA examples. Results: 1) We show that addition of the seventh interrogative 'which' to the W5H set (we denote this extended set as W6H) yields extra and necessary information enabling creation of holistic EA. 2) We discover that particular ordering of the interrogatives, established by linguists (based on semantic and lexical analysis of English language interrogatives), define starting points and the order in which viewpoints should be arranged for creating complete EA. 3) We prove that adopting W6H enables creation of EA for iterative and agile SDLCs, e.g. Scrum. Conclusions: We believe that our findings complete creation of EA using ZF by practitioners, and provide theoreticians with tools needed to improve other EA frameworks, e.g., TOGAF and DoDAF.

0 citations0 reviewsNo code linkedOpen access