arXiv subjects

Vaibhav Jha

Publications and source records attributed to Vaibhav Jha.

4 recordsLinked to original sources

arXiv2026

FAME: QoS-aware Async Service Orchestration for Agentic Workflows

Agentic workflows built on Large Language Models (LLMs) increasingly rely on external tools, with Model Context Protocol (MCP) services emerging as a common interface for tool discovery and invocation. These workflows are stateful, bursty and can block for minutes to hours while external tools execute. Always-on virtual machines and managed agent runtimes simplify deployment, but can over-provision capacity, bill idle wait time or encounter execution limits for long-running tool calls. We present FAME, a service-oriented middleware that optimizes MCP-enabled agentic workflows through modular service orchestration, explicit state management, MCP service input/output reduction and timeout-safe async invocation for long-running external services. FAME realizes these composite services using FaaS-based agent roles, persists workflow state through agent memory injection, and reduces MCP service overhead using S3 handle passing and tool-output caching. For long-running external MCP tools, FAME checkpoints agent and orchestrator state, suspends execution and resumes from callbacks without billing idle waits. Across 26 short-running service tasks over 10 MCP servers, FAME improves service-level QoS by reducing infrastructure cost by 8-12x relative to virtual machines and 43-106x relative to managed runtimes, while reducing latency by up to 17x, input tokens by up to 88% and total cost by up to 66%. On two long-running MLZero workloads from MLE-Bench, FAME completes all iterations while synchronous AWS Step Functions time out, and is 3.64-4.24x cheaper than AWS Durable Functions at the agent level and 2.67-8.23x cheaper at the orchestrator level.

cs.DC↗

arXiv2025

AgentX: Towards Orchestrating Robust Agentic Workflow Patterns with FaaS-hosted MCP Services

Generative Artificial Intelligence (GenAI) has rapidly transformed various fields including code generation, text summarization, image generation and so on. Agentic AI is a recent evolution that further advances this by coupling the decision making and generative capabilities of LLMs with actions that can be performed using tools. While seemingly powerful, Agentic systems often struggle when faced with numerous tools, complex multi-step tasks,and long-context management to track history and avoid hallucinations. Workflow patterns such as Chain-of-Thought (CoT) and ReAct help address this. Here, we define a novel agentic workflow pattern, AgentX, composed of stage designer, planner, and executor agents that is competitive or better than the state-of-the-art agentic patterns. We also leverage Model Context Protocol (MCP) tools, and propose two alternative approaches for deploying MCP servers as cloud Functions as a Service (FaaS). We empirically evaluate the success rate, latency and cost for AgentX and two contemporary agentic patterns, ReAct and Magentic One, using these the FaaS and local MCP server alternatives for three practical applications. This highlights the opportunities and challenges of designing and deploying agentic workflows.

cs.DC↗

arXiv2014

Estimation of Optimized Energy and Latency Constraints for Task Allocation in 3d Network on Chip

In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation schemes which affect the performance of the system. In this paper we test the pre-existing proposed algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark and has been compared with the existing mapping algorithm spiral and crinkle and has shown better reduction in the communication energy consumption and shows improvement in the performance of the system. On performing experimental analysis of proposed algorithm results shows that average reduction in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%. Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.

cs.OH↗

arXiv2014

Energy and Latency Aware Application Mapping Algorithm & Optimization for Homogeneous 3D Network on Chip

Energy efficiency is one of the most critical issue in design of System on Chip. In Network On Chip (NoC) based system, energy consumption is influenced dramatically by mapping of Intellectual Property (IP) which affect the performance of the system. In this paper we test the antecedently extant proposed algorithms and introduced a new energy proficient algorithm stand for 3D NoC architecture. In addition a hybrid method has also been implemented using bioinspired optimization (particle swarm optimization) technique. The proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark and has been compared with the existing algorithm (spiral and crinkle) and has shown better reduction in the communication energy consumption and shows improvement in the performance of the system. Comparing our work with spiral and crinkle, experimental result shows that the average reduction in communication energy consumption is 19% with spiral and 17% with crinkle mapping algorithms, while reduction in communication cost is 24% and 21% whereas reduction in latency is of 24% and 22% with spiral and crinkle. Optimizing our work and the existing methods using bio-inspired technique and having the comparison among them an average energy reduction is found to be of 18% and 24%.

cs.OH↗