arXiv subjects

Javid Taheri

Publications and source records attributed to Javid Taheri.

15 recordsLinked to original sources

arXiv2026

Latency-Aware Service Placement using Neural Combinatorial Optimisers for Edge--Cloud Systems

The growth of Internet of Things (IoT) applications and latency-sensitive services has increased the demand for efficient service placement across compute continuum platforms, such as edge--cloud systems. Modern applications are decomposed into interdependent microservices deployed over heterogeneous infrastructures, making placement under resource and network constraints an intractable NP-hard combinatorial optimisation problem. This study proposes a latency-aware Edge Placement Neural Combinatorial Optimiser (EP-NCO), a learning-based framework for service placement in compute continuum platforms. EP-NCO employs a dual-graph model to capture resource relationships and service dependencies within both computing infrastructure and application structure. Graph neural networks (GNNs) learn structural embeddings of infrastructure nodes and service components, whereas reinforcement learning policies construct feasible placements that account for execution latency, communication link delays, and bandwidth-sharing effects. Extensive simulations across multiple system scales demonstrate that EP-NCO consistently achieves high-quality placement decisions, reducing the total service response time by 46%--50% compared with metaheuristics (genetic algorithm and particle swarm optimisation) and by 25%--35% compared with controlled RL ablation baselines. Once trained, EP-NCO enables fast online inference, making it a practical solution for dynamic large-scale edge--cloud environments with hundreds of computing nodes, hosting thousands of applications, which is significantly beyond the capability of current scheduling systems.

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arXiv2026

EES-CND: Collaborative Neural Decision-Making for Drift-Aware Fault-Tolerant Edge-Cloud Service Placement

The edge-cloud paradigm improves service delivery by orchestrating resources across edge nodes and cloud data centres. These environments consist of heterogeneous, interconnected computing nodes that cooperate to deliver continuous services. However, their scale and complexity increase vulnerability to failures from hardware malfunctions, software defects, and dynamic operating conditions. These failures can disrupt system configurations and service execution, leading to reduced reliability, performance degradation, and violations of service-level objectives. Ensuring service execution requires adaptive service placement strategies across edge-cloud resources. This study introduces a fault-tolerant service placement approach (Enhanced Evolution Strategy for Collaborative Neural Decision-making, EES-CND) for edge-cloud environments. The method employs collaborative decision-making, wherein multiple lightweight neural networks jointly infer redeployment strategies during failure events. To address the system dynamics and mitigate performance drift, adaptive models are updated online using an enhanced evolution strategy. Extensive simulations show that EES-CND effectively handles performance drift and significantly outperforms existing methods in service recovery time, response time, and reliability, achieving a 44.8\% reduction in fault-tolerance cost compared to standalone models.

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arXiv2024

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems using a Multi-Objective Genetic Algorithm

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67\% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97\% infrastructure (hardware) and 95\% services (software).

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arXiv2021

PerfSim: A Performance Simulator for Cloud Native Microservice Chains

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can then simulate the performance behavior of all services over a given period and provide an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

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arXiv2019

On the Cost-Optimality Trade-off for Service Function Chain Reconfiguration

Optimal placement of Virtual Network Functions (VNFs) in virtualized data centers enhances the overall performance of Service Function Chains (SFCs) and decreases the operational costs for mobile network operators. Maintaining an optimal placement of VNFs under changing load requires a dynamic reconfiguration that includes adding or removing VNF instances, changing the resource allocation of VNFs, and re-routing corresponding service flows. However, such reconfiguration may lead to notable service disruptions and impose additional overhead on the VNF infrastructure, especially when reconfiguration entails state or VNF migration. On the other hand, not changing the existing placement may lead to high operational costs. In this paper, we investigate the trade-off between the reconfiguration of SFCs and the optimality of the resulting placement and service flow (re)routing. We model different reconfiguration costs related to the migration of stateful VNFs and solve a joint optimization problem that aims to minimize both the total cost of the VNF placement and the reconfiguration cost necessary for repairing a suboptimal placement. Numerical results show that a small number of reconfiguration operations can significantly reduce the operational cost of the VNF infrastructure; however, too much reconfiguration may not pay off should heavy costs be involved.

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arXiv2013

Data-Intensive Workload Consolidation on Hadoop Distributed File System

Workload consolidation, sharing physical resources among multiple workloads, is a promising technique to save cost and energy in cluster computing systems. This paper highlights a few challenges of workload consolidation for Hadoop as one of the current state-of-the-art data-intensive cluster computing system. Through a systematic step-by-step procedure, we investigate challenges for efficient server consolidation in Hadoop environments. To this end, we first investigate the inter-relationship between last level cache (LLC) contention and throughput degradation for consolidated workloads on a single physical server employing Hadoop distributed file system (HDFS). We then investigate the general case of consolidation on multiple physical servers so that their throughput never falls below a desired/predefined utilization level. We use our empirical results to model consolidation as a classic two-dimensional bin packing problem and then design a computationally efficient greedy algorithm to achieve minimum throughput degradation on multiple servers. Results are very promising and show that our greedy approach is able to achieve near optimal solution in all experimented cases.

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arXiv2013

Statistical Regression to Predict Total Cumulative CPU Usage of MapReduce Jobs

Recently, businesses have started using MapReduce as a popular computation framework for processing large amount of data, such as spam detection, and different data mining tasks, in both public and private clouds. Two of the challenging questions in such environments are (1) choosing suitable values for MapReduce configuration parameters e.g., number of mappers, number of reducers, and DFS block size, and (2) predicting the amount of resources that a user should lease from the service provider. Currently, the tasks of both choosing configuration parameters and estimating required resources are solely the users responsibilities. In this paper, we present an approach to provision the total CPU usage in clock cycles of jobs in MapReduce environment. For a MapReduce job, a profile of total CPU usage in clock cycles is built from the job past executions with different values of two configuration parameters e.g., number of mappers, and number of reducers. Then, a polynomial regression is used to model the relation between these configuration parameters and total CPU usage in clock cycles of the job. We also briefly study the influence of input data scaling on measured total CPU usage in clock cycles. This derived model along with the scaling result can then be used to provision the total CPU usage in clock cycles of the same jobs with different input data size. We validate the accuracy of our models using three realistic applications (WordCount, Exim MainLog parsing, and TeraSort). Results show that the predicted total CPU usage in clock cycles of generated resource provisioning options are less than 8% of the measured total CPU usage in clock cycles in our 20-node virtual Hadoop cluster.

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arXiv2013

Pattern Matching for Self- Tuning of MapReduce Jobs

In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a correlation analysis is then applied to DTWs outcomes to produce feasible similarity patterns. Three real applications (WordCount, Exim Mainlog parsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms.

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arXiv2013

A Study on Using Uncertain Time Series Matching Algorithms in MapReduce Applications

In this paper, we study CPU utilization time patterns of several Map-Reduce applications. After extracting running patterns of several applications, the patterns with their statistical information are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications along with its statistical information are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW) is utilized for such comparison; a statistical analysis is then applied to DTWs' outcomes to select the most suitable candidates. Moreover, under a hypothesis, another algorithm is proposed to classify applications under similar CPU utilization patterns. Three widely used text processing applications (WordCount, Distributed Grep, and Terasort) and another application (Exim Mainlog parsing) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on 5-node Map-Reduce platform

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arXiv2012

Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption

In this chapter we focus on slack reclamation and propose a new slack reclamation technique, Multiple Frequency Selection DVFS (MFS-DVFS). The key idea is to execute each task with a linear combination of more than one frequency such that this combination results in using the lowest energy by covering the whole slack time of the task. We have tested our algorithm with both random and real-world application task graphs and compared with the results in previous researches in [9] and [12-13]. The experimental results show that our approach can achieve energy almost identical to the optimum energy saving.

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arXiv2012

On Modelling and Prediction of Total CPU Usage for Applications in MapReduce Environments

Recently, businesses have started using MapReduce as a popular computation framework for processing large amount of data, such as spam detection, and different data mining tasks, in both public and private clouds. Two of the challenging questions in such environments are (1) choosing suitable values for MapReduce configuration parameters -e.g., number of mappers, number of reducers, and DFS block size-, and (2) predicting the amount of resources that a user should lease from the service provider. Currently, the tasks of both choosing configuration parameters and estimating required resources are solely the users' responsibilities. In this paper, we present an approach to provision the total CPU usage in clock cycles of jobs in MapReduce environment. For a MapReduce job, a profile of total CPU usage in clock cycles is built from the job past executions with different values of two configuration parameters e.g., number of mappers, and number of reducers. Then, a polynomial regression is used to model the relation between these configuration parameters and total CPU usage in clock cycles of the job. We also briefly study the influence of input data scaling on measured total CPU usage in clock cycles. This derived model along with the scaling result can then be used to provision the total CPU usage in clock cycles of the same jobs with different input data size. We validate the accuracy of our models using three realistic applications (WordCount, Exim MainLog parsing, and TeraSort). Results show that the predicted total CPU usage in clock cycles of generated resource provisioning options are less than 8% of the measured total CPU usage in clock cycles in our 20-node virtual Hadoop cluster.

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arXiv2012

Network Load Analysis and Provisioning of MapReduce Applications

In this paper, we study the dependency between configuration parameters and network load of fixed-size MapReduce applications in shuffle phase and then propose an analytical method to model this dependency. Our approach consists of three key phases: profiling, modeling, and prediction. In the first stage, an application is run several times with different sets of MapReduce configuration parameters (here number of mappers and number of reducers) to profile the network load of the application in the shuffle phase on a given cluster. Then, the relation between these parameters and the network load is modeled by multivariate linear regression. For evaluation, three applications (WordCount, Exim Mainlog parsing, and TeraSort) are utilized to evaluate our technique on a 4-node MapReduce private cluster.

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arXiv2012

On Modeling Dependency between MapReduce Configuration Parameters and Total Execution Time

In this paper, we propose an analytical method to model the dependency between configuration parameters and total execution time of Map-Reduce applications. Our approach has three key phases: profiling, modeling, and prediction. In profiling, an application is run several times with different sets of MapReduce configuration parameters to profile the execution time of the application on a given platform. Then in modeling, the relation between these parameters and total execution time is modeled by multivariate linear regression. Among the possible configuration parameters, two main parameters have been used in this study: the number of Mappers, and the number of Reducers. For evaluation, two standard applications (WordCount, and Exim Mainlog parsing) are utilized to evaluate our technique on a 4-node MapReduce platform.

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arXiv2012

SparseDTW: A Novel Approach to Speed up Dynamic Time Warping

We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series. The more the similarity between the time series the less space required to compute the DTW between them. To the best of our knowledge, all other techniques to speedup DTW, impose apriori constraints and do not exploit similarity characteristics that may be present in the data. We conduct experiments and demonstrate that SparseDTW outperforms previous approaches.

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arXiv2012

Some Observations on Optimal Frequency Selection in DVFS-based Energy Consumption Minimization

In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. Majority of these algorithms involve two passes: schedule generation and slack reclamation. The former pass involves the redistribution of tasks among DVFS-enabled processors based on a given cost function that includes makespan and energy consumption; and, while the latter pass is typically achieved by executing individual tasks with slacks at a lower processor frequency. In this paper, a new slack reclamation algorithm is proposed by approaching the energy reduction problem from a different angle. Firstly, the problem of task slack reclamation by using combinations of processors' frequencies is formulated. Secondly, several proofs are provided to show that (1) if the working frequency set of processor is assumed to be continues, the optimal energy will be always achieved by using only one frequency, (2) for real processors with a discrete set of working frequencies, the optimal energy is always achieved by using at most two frequencies, and (3) these two frequencies are adjacent/neighbouring when processor energy consumption is a convex function of frequency. Thirdly, a novel algorithm to find the best combination of frequencies to result the optimal energy is presented. The presented algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 3000 randomly generated task graphs, and 600 task graphs for two popular applications (Gauss-Jordan and LU decomposition). The results show the superiority of the proposed algorithm in comparison with other techniques.

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