Hamzeh Khazaei

Hoda Torabi, Hamzeh Khazaei, Marin Litoiu

With the advent of 5G networks and the rise of the Internet of Things (IoT), Content Delivery Networks (CDNs) are increasingly extending into the network edge. This shift introduces unique challenges, particularly due to the limited cache storage and the diverse request patterns at the edge. These edge environments can host traffic classes characterized by varied object-size distributions and object-access patterns. Such complexity makes it difficult for traditional caching strategies, which often rely on metrics like request frequency or time intervals, to be effective. Despite these complexities, the optimization of edge caching is crucial. Improved byte hit rates at the edge not only alleviate the load on the network backbone but also minimize operational costs and expedite content delivery to end-users. In this paper, we introduce HR-Cache, a comprehensive learning-based caching framework grounded in the principles of Hazard Rate (HR) ordering, a rule originally formulated to compute an upper bound on cache performance. HR-Cache leverages this rule to guide future object eviction decisions. It employs a lightweight machine learning model to learn from caching decisions made based on HR ordering, subsequently predicting the "cache-friendliness" of incoming requests. Objects deemed "cache-averse" are placed into cache as priority candidates for eviction. Through extensive experimentation, we demonstrate that HR-Cache not only consistently enhances byte hit rates compared to existing state-of-the-art methods but also achieves this with minimal prediction overhead. Our experimental results, using three real-world traces and one synthetic trace, indicate that HR-Cache consistently achieves 2.2-14.6% greater WAN traffic savings than LRU. It outperforms not only heuristic caching strategies but also the state-of-the-art learning-based algorithm.

Alireza Pourali, Arian Boukani, Hamzeh Khazaei

Training deep learning models, particularly Transformer-based architectures such as Large Language Models (LLMs), demands substantial computational resources and extended training periods. While optimal configuration and infrastructure selection can significantly reduce associated costs, this optimization requires preliminary analysis tools. This paper introduces PreNeT, a novel predictive framework designed to address this optimization challenge. PreNeT facilitates training optimization by integrating comprehensive computational metrics, including layer-specific parameters, arithmetic operations and memory utilization. A key feature of PreNeT is its capacity to accurately predict training duration on previously unexamined hardware infrastructures, including novel accelerator architectures. This framework employs a sophisticated approach to capture and analyze the distinct characteristics of various neural network layers, thereby enhancing existing prediction methodologies. Through proactive implementation of PreNeT, researchers and practitioners can determine optimal configurations, parameter settings, and hardware specifications to maximize cost-efficiency and minimize training duration. Experimental results demonstrate that PreNeT achieves up to 72% improvement in prediction accuracy compared to contemporary state-of-the-art frameworks.

Nima Mahmoudi, Hamzeh Khazaei

Analytical performance models are very effective in ensuring the quality of service and cost of service deployment remain desirable under different conditions and workloads. While various analytical performance models have been proposed for previous paradigms in cloud computing, serverless computing lacks such models that can provide developers with performance guarantees. Besides, most serverless computing platforms still require developers' input to specify the configuration for their deployment that could affect both the performance and cost of their deployment, without providing them with any direct and immediate feedback. In previous studies, we built such performance models for steady-state and transient analysis of scale-per-request serverless computing platforms (e.g., AWS Lambda, Azure Functions, Google Cloud Functions) that could give developers immediate feedback about the quality of service and cost of their deployments. In this work, we aim to develop analytical performance models for the latest trend in serverless computing platforms that use concurrency value and the rate of requests per second for autoscaling decisions. Examples of such serverless computing platforms are Knative and Google Cloud Run (a managed Knative service by Google). The proposed performance model can help developers and providers predict the performance and cost of deployments with different configurations which could help them tune the configuration toward the best outcome. We validate the applicability and accuracy of the proposed performance model by extensive real-world experimentation on Knative and show that our performance model is able to accurately predict the steady-state characteristics of a given workload with minimal amount of data collection.

Nima Mahmoudi, Hamzeh Khazaei

Serving machine learning inference workloads on the cloud is still a challenging task on the production level. Optimal configuration of the inference workload to meet SLA requirements while optimizing the infrastructure costs is highly complicated due to the complex interaction between batch configuration, resource configurations, and variable arrival process. Serverless computing has emerged in recent years to automate most infrastructure management tasks. Workload batching has revealed the potential to improve the response time and cost-effectiveness of machine learning serving workloads. However, it has not yet been supported out of the box by serverless computing platforms. Our experiments have shown that for various machine learning workloads, batching can hugely improve the system's efficiency by reducing the processing overhead per request. In this work, we present MLProxy, an adaptive reverse proxy to support efficient machine learning serving workloads on serverless computing systems. MLProxy supports adaptive batching to ensure SLA compliance while optimizing serverless costs. We performed rigorous experiments on Knative to demonstrate the effectiveness of MLProxy. We showed that MLProxy could reduce the cost of serverless deployment by up to 92% while reducing SLA violations by up to 99% that can be generalized across state-of-the-art model serving frameworks.

Omid Hajihassani, Omid Ardakanian, Hamzeh Khazaei

The abundance of data collected by sensors in Internet of Things (IoT) devices, and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this paper, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.

Md Monir Hossain, Nima Mahmoudi, Changyuan Lin et al.

Online resources today contain an abundant amount of code snippets for documentation, collaboration, learning, and problem-solving purposes. Their executability in a "plug and play" manner enables us to confirm their quality and use them directly in projects. But, in practice that is often not the case due to several requirements violations or incompleteness. However, it is a difficult task to investigate the executability on a large scale due to different possible errors during the execution. We have developed a scalable framework to investigate this for SOTorrent Python snippets. We found that with minor adjustments, 27.92% of snippets are executable. The executability has not changed significantly over time. The code snippets referenced in GitHub are more likely to be directly executable. But executability does not affect the chances of the answer to be selected as the accepted answer significantly. These properties help us understand and improve the interaction of users with online resources that include code snippets.

Hamzeh Khazaei, Nima Mahmoudi, Cornel Barna et al.

Microservice architecture has transformed the way developers are building and deploying applications in the nowadays cloud computing centers. This new approach provides increased scalability, flexibility, manageability, and performance while reducing the complexity of the whole software development life cycle. The increase in cloud resource utilization also benefits microservice providers. Various microservice platforms have emerged to facilitate the DevOps of containerized services by enabling continuous integration and delivery. Microservice platforms deploy application containers on virtual or physical machines provided by public/private cloud infrastructures in a seamless manner. In this paper, we study and evaluate the provisioning performance of microservice platforms by incorporating the details of all layers (i.e., both micro and macro layers) in the modelling process. To this end, we first build a microservice platform on top of Amazon EC2 cloud and then leverage it to develop a comprehensive performance model to perform what-if analysis and capacity planning for microservice platforms at scale. In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both the microservice platform and the back-end macroservice infrastructures.