Lab Report Actor in Indonesia Jakarta –Free Word Template Download with AI
Laboratory: Advanced Distributed Systems Lab
Institution:
National Research Institute of Technology, Jakarta Branch
**Subject:** Comprehensive Performance Evaluation of Actor Model Architecture in High-Concurrency Environments within Indonesia Jakarta Urban Infrastructure.
Executive Summary
This laboratory report details the rigorous testing and evaluation of the Actor computational model, specifically focusing on its applicability to real-time data processing requirements in Indonesia Jakarta. As Indonesia Jakarta serves as the economic and technological heartbeat of Southeast Asia, the demand for robust, scalable software architectures has never been higher. This study investigates how Actor-based systems can handle massive concurrency with low latency, addressing specific challenges found in the unique digital infrastructure landscape of Indonesia Jakarta.
The rapid digitization of Indonesia Jakarta has created a pressing need for software systems that can process millions of concurrent transactions without degradation in performance. Traditional multi-threaded architectures often struggle with lock contention and thread management overheads when scaled to the levels required by modern fintech, logistics, and e-commerce platforms prevalent in Indonesia Jakarta. Consequently, this lab report aims to evaluate the Actor model as a solution to these scalability bottlenecks.
The Actor model is a mathematical model of concurrent computation that treats actors as the universal primitives of concurrent digital computation. In this context, an actor is a primitive unit of computation: it receives messages, sends messages, creates more actors, and decides what to do with each message it receives. Unlike traditional shared-memory models used in many legacy systems in Indonesia Jakarta, the Actor model relies on message passing and isolated state management.
This report specifically addresses the deployment environment of Indonesia Jakarta due to its distinct network characteristics. The latency variations inherent in the Indonesian archipelago, combined with the high-density connectivity of Java’s capital city, require architectures that are resilient to partial failures and network instability. By focusing on Indonesia Jakarta, we ensure that our findings are relevant to one of the world's most dynamic digital markets.
The primary objectives of this laboratory experiment were as follows:
- To measure the throughput and latency of an Actor-based system under varying load conditions.
- To compare the performance of the Actor model against traditional thread-per-request models in a simulated environment mimicking Indonesia Jakarta’s network topology.
- To evaluate memory efficiency and CPU utilization when scaling Actor instances to handle peak traffic typical of major urban centers like Indonesia Jakarta.
The laboratory setup involved the creation of a high-fidelity simulation environment that replicated the network conditions found in Indonesia Jakarta. We utilized Akka, a popular toolkit for building Actor-based applications, deployed on cloud servers located in data centers serving the Southeast Asian region.
3.1 Environment Configuration
The hardware configuration consisted of 8-core processors with 16GB of RAM per node to simulate mid-range enterprise servers commonly used by small-to-medium enterprises in Indonesia Jakarta. The network simulation layer introduced variable latency and packet loss rates consistent with historical data from Internet Service Providers (ISPs) across Java island, particularly in the Jakarta metropolitan area.
3.2 The Actor Implementation
We developed a custom Actor hierarchy designed to simulate a ride-hailing application, a sector highly dominant in Indonesia Jakarta. Each driver and rider was represented by an independent Actor. Messages exchanged included location pings, request for service, and confirmation notifications. The Actors communicated via asynchronous message passing, ensuring that no shared mutable state existed between them.
3.3 Benchmarking Tools
We employed Apache JMeter to generate synthetic load on the system. The load was gradually increased from 1,000 concurrent users to 50,000 concurrent users to observe how the Actor system scaled. Key metrics recorded included response time (in milliseconds), requests per second (RPS), and error rates.
The data collected from the laboratory experiments reveals significant advantages of using the Actor model in environments similar to Indonesia Jakarta.
| User Load (Concurrent Users) | Average Latency (ms) - Actor Model | Average Latency (ms) - Thread Model | CPU Utilization (%) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1,000 | 45 | = 52
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