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REE-TM: Reliable and Energy-Efficient Traffic Management Model for Diverse Cloud Workloads

Ashutosh Kumar Singh, Deepika Saxena, Volker Lindenstruth

arXiv:2608.25747v1cs.DC

TL;DR

Heterogeneous cloud workloads complicate resource allocation and energy-efficient management. REE-TM categorizes jobs, predicts workload and traffic states, and evaluates reliability, utilization, energy, and congestion-related metrics through simulations.

  • Problem

    Diverse workloads require different compute, storage, and network resources, complicating traffic optimization while risking overprovisioning or under-utilization.

  • Method

    REE-TM categorizes heterogeneous jobs and combines TG-QNN workload analysis with QBHO-based learning to estimate capacity usage and detect probable congestion.

  • Results

    TG-QNN achieved average prediction errors of 0.0005 for memory and 0.0006 for CPU at 1-minute intervals, while REE-TM reliability ranged from 85.6% to 97.8%.

  • Takeaways & Limitations

    REE-TM provides a reliability-focused framework for managing heterogeneous cloud traffic using workload prediction, traffic-state analysis, and resource-aware job categorization.

Abstract

from arXiv · show

Diversity of workload demands lays a critical impact on efficient resource allocation and management of cloud services. The existing literature has either weakly considered or overlooked the heterogeneous feature of job requests received from wide range of internet services users. To address this context, the proposed approach named Reliable and Energy Efficient Traffic Management (REE-TM) has exploited the diversity of internet traffic in terms of variation in resource demands and expected complexity. Specifically, REE-TM incorporates categorization of heterogeneous job requests and executes them by selecting the most admissible virtual node (a software-defined instance such as a virtual machine or container) and physical node (an actual hardware server or compute host) within the cloud infrastructure. To deal with resource-contention-based resource failures and performance degradation, a novel workload estimator 'Toffoli Gate-based Quantum Neural Network' (TG-QNN) is proposed, wherein learning process or interconnection weights optimization is achieved using Quantum version of BlackHole (QBHO) algorithm. The proactively estimated workload is used to compute entropy of the upcoming internet traffic with various traffic states analysis for detection of probable resource-congestion. REE-TM is extensively evaluated through simulations using a benchmark dataset and compared with optimal and without REE-TM versions. The performance evaluation and comparison of REE-TM with measured significant metrics reveal its effectiveness in assuring higher reliability by up to 30.25% and energy-efficiency by up to 23% as compared without REE-TM.

I. INTRODUCTION

Cloud workloads vary in resource demands, traffic patterns, latency sensitivity, and priorities, making reliable and energy-efficient management difficult. REE-TM addresses this diversity through workload classification, proactive estimation, entropy-based traffic analysis, and resource-aware management.

  • Workload diversity: Diverse cloud workloads require different compute, storage, and network resources, complicating traffic optimization without overprovisioning or under-utilization.
  • Management challenges: Real-time and critical cloud services must balance varying service times, resource needs, deadlines, reliability, and energy efficiency.
  • Proposed approach: REE-TM categorizes heterogeneous jobs and selects admissible virtual and physical nodes for resource-sensitive execution.
  • Proposed approach: TG-QNN with QBHO proactively estimates workload capacity usage, while traffic-state entropy analysis detects probable congestion and supports adaptive resource decisions.
  • Evaluation: REE-TM is evaluated on a real benchmark dataset and reported to outperform state-of-the-art approaches across various performance metrics.

D. Motivation for TG-QNN

Conventional workload models are limited in capturing complex, high-dimensional cloud-traffic relationships. REE-TM therefore combines a Toffoli-gate quantum neural network with quantum optimization and entropy-based traffic analysis.

  • D. Motivation for TG-QNN: Conventional machine-learning methods may inadequately capture complex correlations in heterogeneous, high-dimensional, and variable cloud workloads.
  • TG-QNN architecture: TG-QNN uses input, hidden, and output qubit neurons, with interconnection weights represented as qubits and adjusted through Toffoli-gate operations.
  • Workload estimation: The system monitors CPU, memory, and bandwidth usage, normalizes samples, and aggregates them into fixed-interval resource snapshots for workload estimation.
  • Traffic-state analysis: TSECE computes and learns traffic entropy, including sudden variations, peaks, and downturns, to support real-time resource adaptation.

III. TRAFFIC MANAGEMENT

REE-TM classifies heterogeneous job requests according to resource requirements and communication characteristics, then manages each traffic category according to its associated quality-of-service needs.

  • Traffic classification: REE-TM demarcates incoming requests into Stragglers, Resource hogs, and Normal tasks based on physical resource requirements.
  • Traffic classification: Requests are characterized using CPU, memory, bandwidth, and input/output or data-communication requirements.
  • Traffic classification: High-memory or I/O-intensive requests are assigned to the straggler cluster, while comparatively high-capacity requests belong to the resource-hog cluster.
  • Traffic-state management: Different traffic states receive distinct execution management according to service quality requirements, with reliability and energy efficiency included in node allocation.

A. Request Assignment and Execution

REE-TM assigns classified requests to virtual nodes hosted on physical nodes according to resource capacity, processing speed, deadlines, reliability, and energy considerations. The model also computes node reliability, failure hazards, utilization, and DVFS-based energy consumption.

  • A. Request Assignment and Execution: Stragglers are assigned to nodes with sufficient CPU and memory capacity that support their required I/O operations.
  • A. Request Assignment and Execution: Resource hogs use comparatively higher-capacity physical nodes, while normal tasks use remaining nodes ordered by processing speed and priority.
  • A. Request Assignment and Execution: Each task is assigned to one virtual node on one physical node, subject to node-capacity and task-resource constraints.
  • B. Reliability Modeling: Physical-node reliability incorporates availability, fault tolerance, and failure detection, while virtual-node hazard depends on resource utilization and physical-node failure rates.
  • C. Energy Efficiency Modeling: DVFS models power consumption through CPU sleep and busy states, and total energy is calculated from resource utilization over a time interval.

IV. WORKLOAD ESTIMATION

TG-QNN proactively forecasts heterogeneous workloads from recent resource-usage samples using qubit-based neural layers and QBHO-optimized weights. Its normalized outputs are evaluated against actual workloads using MSE.

  • TG-QNN architecture: TG-QNN uses input, hidden, and output qubit neurons interconnected by qubit weights.The network contains n input neurons, p neurons per hidden layer, and q output neurons.
  • Workload forecasting: N previous workload values are transformed through a Quantum Rotation gate to forecast the approaching workload.The samples capture behavioral patterns from live resource usage.
  • TG-QNN optimization: QBHO optimizes TG-QNN interconnection weights by exploring candidate qubit networks and selecting the network with the lowest prediction error.The fitness function evaluates prediction error.
  • Neuron operation: Each non-input neuron applies qubit summation, a controlled reverse operation, and activation to produce nonlinear patterns.The Toffoli Gate supplies the controlled operation within this sequence.
  • Output evaluation: The SIG activation maps predictions to [0, 1], enabling comparison with actual outputs, while MSE evaluates prediction accuracy over m samples.Normalization makes predicted and actual output values comparable.

A. TG-QNN Optimization by QBHO

QBHO trains TG-QNN through clustered candidate qubit networks, heuristic crossover, and position updates. Event-horizon absorption and reinitialization balance convergence with population diversity.

  • Initialization: QBHO initializes qubit stars as candidate solutions and selects the best-fitness star as the Quantum Black-hole.The candidates are represented using probability amplitudes for quantum states.
  • Qubit cluster optimization: Qubit stars are organized into K clusters by weight-amplitude similarity, and each cluster contributes a local blackhole.Candidates are evaluated on training data using MSE.
  • Heuristic optimization: Heuristic crossover combines selected local blackholes to generate offspring and nominates the best candidate as the Global Best.The crossover rate is randomly generated for each gene.
  • Position Update: Position updates attract qubit stars toward cluster and global blackholes while controlling convergence speed and exploration.The distances use attraction forces associated with the two blackholes.
  • Event horizon: Candidates inside an event-horizon radius are absorbed and reinitialized to improve convergence while preserving diversity.The radius is based on a blackhole’s fitness relative to its population’s total fitness.
  • Complexity: The total QBHO time complexity is O(Z × n^2 × M × K).Here Z is the number of qubit solutions, n the input size, M the training samples, and K the clusters.

V. TRAFFIC STATES ENTROPY ANALYSIS

REE-TM analyzes resource-use trails and entropy to classify live traffic states and estimate forthcoming traffic entropy. These estimates support physical-node planning and congestion detection.

  • Traffic-state inputs: Resource trails compare predicted and actual CPU, memory, and bandwidth utilization, while execution-time lag is measured against a variable threshold duration.The threshold may change with workload execution demand.
  • Traffic classification: A negative lag value detects probable network congestion, whereas a nonnegative value specifies regular traffic.The lag is determined from the threshold duration and execution time.
  • Traffic states: REE-TM determines ten live internet-traffic states in the experimental evaluation using resource-trail values and execution-time lag.The states are represented as TS1 through TS10.
  • Entropy estimation: Forthcoming entropy uses previous entropy values and current traffic entropy, assigning more weight to the most recent value.The method assumes Ent > Ent−1 ≥ Ent−2 for estimation.
  • Node planning: The required physical-node count is constrained so predicted workload and forthcoming entropy remain below total cluster resource capacity.Equations also calculate active and inactive physical nodes.

VI. OPERATIONAL DESIGN AND COMPLEXITY

REE-TM receives heterogeneous job requests, categorizes them by resource and input/output demands, and allocates them within available virtual and physical nodes.

  • Request and resource setup: REE-TM initializes lists of job requests, virtual nodes, and physical nodes for cloud execution.Requests arrive from a heterogeneous internet-service consumption environment.
  • Workload categorization: At each time interval, jobs are categorized by resource usage, input/output demand, and interdependency among jobs from a common user.

VII. PERFORMANCE EVALUATION

REE-TM combines workload prediction, traffic-state entropy analysis, heterogeneous job classification, reliability estimation, and node allocation in a recurring operational workflow. Its evaluation measures reliability, energy-efficiency, resource utilization, failures, and load-distribution success.

  • Operational workflow: REE-TM initializes job-request, virtual-node, and physical-node lists, then repeats traffic management for each time interval.The operational summary allocates requests, computes reliability, estimates workload, and continues the cycle.
  • Workload estimation: TG-QNN predicts future resource usage, using QBHO-trained learning to support proactive physical-resource arrangement and error evaluation.The predicted traffic is generated for the next interval before workload entropy is computed.
  • Traffic analysis: Entropy computation compares actual and previously predicted workload, estimates traffic states, and derives entropy for upcoming workload.This module supports analysis of changing traffic conditions before allocation decisions.
  • Job management: Job requests are categorized by resource demand into stragglers, resource hogs, and normal tasks before execution.Users submit requests with execution priorities, and the categorized jobs are assigned to virtual nodes on available physical nodes.
  • Reliability and allocation: REE-TM computes each physical node’s reliability from prior-interval failures, then allocates jobs and virtual nodes using Eqs. (2-5).Reliability uses MTBF based on the number of failures observed in the previous time interval.
  • Evaluation: Evaluation measures total reliability, energy-efficiency, resource utilization, failures, and successful load distribution during periodic intervals.These measures are used to assess traffic management performance.

B. Experimental Set-up and Dataset

The experiments use a server-based cloud simulation with IBM physical-server configurations, Amazon-inspired virtual-node configurations, and classical simulation of TG-QNN equations. REE-TM is evaluated on the Google Compute Cluster dataset and workload statistics.

  • Simulation environment: Experiments run on a server with two Intel Xeon Silver 4114 CPUs, 40 cores, 2.20 GHz clock speed, Ubuntu 16.04 LTS, and 128 GB memory.The configuration provides the execution environment for the simulation experiments.
  • Cloud configuration: The data-centre simulation includes three IBM-based physical-server types and four Amazon-inspired virtual-node configurations.Physical resources include power consumption, MIPS, RAM, and memory features.
  • TG-QNN evaluation: TG-QNN is evaluated through classical simulation using equations governing qubit-vector processing in Eqs. (19)-(25).The equations simulate the Toffoli-gate-based processing used by the model.
  • Experimental parameters: The experimental setup specifies parameters for TG-QNN optimization and job-request classification, while Table VI summarizes evaluated-workload characteristics.The cited tables organize optimization, classification, and workload-statistics settings.
  • Dataset: The Google Compute Cluster dataset contains 672,300 jobs executed on 12,500 servers over 29 days, with CPU, memory, disk-I/O-request, and resource-usage information.VM CPU and memory utilization percentages are derived from task-level usage recorded every five minutes over 24 hours.

C. Results

REE-TM achieves accurate workload forecasting and strong reliability, energy, and load-distribution performance across varying cloud workloads. Its results remain close to optimal while improving over approaches without proactive estimation.

  • Overall performance: REE-TM maintained 85.6%–97.8% reliability, 42.6%–49.9% resource utilization, and up to 99.9% load-allocation success over 200 minutes.Overloads ranged from 1.95% to 8.20%, while active physical nodes scaled from 9 for 100 requests to 70 for 1000 requests.
  • Workload analysis: TG-QNN prediction errors averaged 0.0005 for memory and 0.0006 for CPU at 1-minute intervals, increasing to 0.004 and 0.006 at 1-hour intervals.The reported comparisons show closer actual–predicted alignment for shorter intervals.
  • Workload analysis: TG-QNN achieved the lowest MSE and MAE at 5-minute and 60-minute windows, with MSE 0.0005 and 0.0024, respectively.At 10 minutes, ATTN slightly outperformed TG-QNN.
  • Optimization analysis: QBHO achieved 98.06% average accuracy with space complexity O(Zn^2), while also showing faster and more stable convergence than TADE, SaDE, and FT-ERM.The convergence comparison covered GCD-CPU and GCD-Mem settings.
  • Reliability: REE-TM improved reliability by up to 26.04% versus W-REE-TM∗ and 30.25% versus W-REE-TM∗∗.Its reliability stayed within 1–6% of OPTIMAL, while the baselines lacked proactive workload estimation.
  • Energy consumption: REE-TM reduced energy consumption by 20–23% versus W-REE-TM∗ and W-REE-TM∗∗, while using 1–5% more energy than OPTIMAL.The reported mechanism links lower energy use to workload estimation and fewer active physical nodes.
  • Load distribution: REE-TM’s success rates reached 100% for up to 600 requests and exceeded W-REE-TM∗ and W-REE-TM∗∗ by up to 38% and 74%.Beyond 600 requests, REE-TM trailed OPTIMAL by only 1–2%; active nodes were also reduced by up to 11.11% and 15.79% versus the two baselines.
  • Comparative analysis: Across state-of-the-art methods, REE-TM reported 96%–99.5% workload-estimation accuracy, up to 95.7% reliability, and 10.092 KWH energy consumption.Its resource utilization was 49.7%, lower than SRE-HM’s 74.9%.

3) Ablation Study:

The ablation study examines how TG-QNN, QBHO, and TSECE contribute incrementally to REE-TM’s performance. The framework combines heterogeneous-job categorization, reliability-aware node assignment, and optimized workload estimation.

  • Ablation Study: The TGQNN+TM base model achieved 66.67% reliability, 80.98% success rate, and MSE 0.0075 for 1000 service requests.These values establish the ablation baseline before adding QBHO and TSECE.
  • Ablation Study: Adding QBHO to TGQNN+TM increased reliability to 73.78% and reduced MSE to 0.0003.The reported change reflects the optimizer’s contribution to refining load allocation.
  • Ablation Study: REE-TM categorizes jobs as stragglers, resource hogs, or normal and assigns them to suitable virtual nodes hosted on periodically assessed physical servers.TG-QNN estimates traffic bursts and resource needs while calculating traffic entropy for congestion management.
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