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Tag: Networks & Cloud Computing

  • Workflow decomposition expanded scheduling beyond native solver capacity

    What the study found

    The study found that the Series-Parallel Workflow Decomposition (SPWD) algorithm can help a hybrid quantum solver handle workflow scheduling instances that would otherwise exceed its capacity. It also found that this comes with a relatively low cost increase compared with a reference solver.

    Why the authors say this matters

    The authors suggest that SPWD may make it possible to use low-capacity solvers on larger workflow scheduling problems. They also conclude that, under a hypothetical cloud pay-as-you-go billing model, reducing solver runtime while shifting extra work locally may be beneficial.

    What the researchers tested

    The researchers introduced SPWD, a heuristic for workflow scheduling that maps a workflow to a Two-Terminal Series-Parallel graph, builds a binary decomposition tree, and prunes it into smaller subworkflows. These subworkflows are then solved independently and merged into a final schedule using the hybrid D-Wave Constrained Quadratic Model (CQM) solver. They tested the approach on real-world workflows from the WfCommons standardization initiative repository.

    What worked and what didn't

    SPWD enabled the CQM solver to solve instances beyond its native capacity. The reported cost increase was up to 17.5% compared with the reference Gurobi solver, and the optimization subroutine runtime was reduced. At the same time, the decomposition increased local computational load.

    What to keep in mind

    The available summary does not describe detailed limitations beyond the trade-off between lower solver runtime and higher local computation. The cost comparison is reported against Gurobi on the tested real-world workflows, so the result is scoped to that setting.

    • SPWD is a heuristic for decomposing workflow scheduling problems into smaller parts.
    • The approach let a hybrid D-Wave CQM solver handle instances beyond its native capacity.
    • Reported cost increase versus Gurobi was up to 17.5%.
    • The optimization subroutine ran faster, but local computational load increased.
    • The authors note possible benefit under a cloud pay-as-you-go billing model.

  • No study findings are provided in the available abstract

    What the study found

    The available abstract does not describe any specific finding or conclusion. It only identifies the article as a research article about load balancing in cloud computing.

    Why the authors say this matters

    The provided text does not include an explicit statement of relevance or impact. No author-specified significance is available in the abstract.

    What the researchers tested

    The abstract does not describe the research approach, methods, or data used. Only the title indicates that the article concerns load balancing in cloud computing, including challenges, constraints, algorithms, and future research directions.

    What worked and what didn't

    No results are reported in the provided abstract. It does not state which algorithms, constraints, or approaches performed well or poorly.

    What to keep in mind

    The available summary is extremely limited and does not include the article's substantive abstract. Any detailed interpretation would go beyond the provided text.

    • The abstract provided here does not state any specific findings.
    • The article is identified as a research article on load balancing in cloud computing.
    • The title mentions challenges, constraints, algorithms, and future research directions.
    • No methods, results, or conclusions are described in the supplied text.

  • DynamoDB and MongoDB were compared for efficiency

    What the study found

    The article compares Amazon DynamoDB and MongoDB and reports that the study aimed to identify the more efficient system. It also examined which database system better optimizes queries in one of the test scenarios.

    Why the authors say this matters

    The authors suggest the comparison is relevant because it helps determine which non-relational database system performs more efficiently. The study indicates this could be useful for understanding how the two systems behave across different deployment environments.

    What the researchers tested

    The researchers prepared identical databases in four environments: MongoDB Local, DynamoDB Local, MongoDB Atlas, and DynamoDB AWS. They used JMeter, a tool for testing and measuring software performance, to run scripts that measured the execution time of searching, adding, editing, and deleting data.

    What worked and what didn't

    The abstract says the study measured how long database operations took and checked query optimization in one scenario, but it does not state which system performed better. No specific operation-level results are provided in the available summary.

    What to keep in mind

    The available abstract does not include the actual performance results or the final comparison outcome. It also does not describe any limitations beyond the tested environments and scenarios.

    • The article compares Amazon DynamoDB with MongoDB.
    • Identical databases were tested in four environments: local and cloud versions of both systems.
    • JMeter scripts were used to measure the time for searching, adding, editing, and deleting data.
    • One scenario was used to check which system better optimizes queries.
    • The abstract does not state which database was more efficient.

  • RaaS reduced tail latency in storage-disaggregated databases

    What the study found

    The study found that Replay-as-a-Service, or RaaS, reduced long tail latency in storage-disaggregated databases. It also improved overall throughput in the tested system.

    Why the authors say this matters

    The authors say storage-disaggregated databases are widely used in the cloud because they can improve resource utilization, reduce fragmentation, and allow compute and storage to scale independently. The study suggests RaaS matters because it addresses the long tail latency problem that the authors identify as a significant limitation of this database design.

    What the researchers tested

    The researchers focused on online transaction processing (OLTP) databases, including systems such as Amazon Aurora, Microsoft Socrates, and Neon. They implemented RaaS in OpenAurora, an open-source storage-disaggregated database based on PostgreSQL, and evaluated it with SysBench.

    What worked and what didn't

    RaaS reduced P95 tail latency by 40.1% in the experiments reported in the abstract. It also improved overall throughput by 75.9%. The abstract says the approach works by decoupling log replay from the storage engine and using idle or dedicated servers for replay.

    What to keep in mind

    The available summary does not describe detailed limitations, trade-offs, or conditions under which the results may change. The reported results are from OpenAurora and SysBench, so the abstract does not state whether the same gains would appear in other systems or workloads.

    • The paper addresses long tail latency in storage-disaggregated databases.
    • RaaS separates log replay from the storage engine and treats it as an independent service.
    • The implementation was tested in OpenAurora, an open-source PostgreSQL-based system.
    • SysBench experiments reported a 40.1% reduction in P95 tail latency.
    • The abstract reports a 75.9% improvement in overall throughput.

  • AGENT improved task allocation makespan in heterogeneous cloud systems

    What the study found

    The study found that AGENT, an adaptive genetic algorithm with elitism and nonlinear tuning, improved task allocation performance in heterogeneous cloud systems. It focused on reducing makespan, which is the total time needed to finish all scheduled tasks.

    Why the authors say this matters

    The authors conclude that better makespan in infrastructure-as-a-service scheduling may support more efficient cloud resource allocation. They also suggest that shorter virtual machine operating time may imply improved energy efficiency, although they say this should be examined with direct measurements in future work.

    What the researchers tested

    The researchers proposed AGENT, which combines size-preserving elitism, feedback-based nonlinear parameter adaptation, and a multi-task-per-virtual-machine allocation model. They evaluated it in CloudSim Plus simulations using Amazon EC2 virtual machine setups and synthetic workloads.

    What worked and what didn't

    AGENT showed makespan improvements of 3.14% to 28.89% compared with HAGA, AIGA, SGA, Max-Min, and Min-Min on synthetic workloads. The abstract says it also performed well across workloads of different sizes and produced near-optimal results, but it does not provide detailed case-by-case failures or exceptions.

    What to keep in mind

    The abstract describes simulation-based testing rather than real-world deployment. It also notes that the energy-efficiency idea is implied by reduced makespan and should be investigated in the future using direct measurements.

    • AGENT is an adaptive genetic algorithm with elitism and nonlinear parameter tuning.
    • The study targets makespan reduction in heterogeneous cloud task scheduling.
    • CloudSim Plus simulations with Amazon EC2 virtual machine setups were used for evaluation.
    • Reported makespan gains ranged from 3.14% to 28.89% versus baseline algorithms.
    • The abstract says scalability was good and results were near-optimal on different workload sizes.
  • Proactive VM consolidation reduced energy use and SLA violations

    Proactive VM consolidation reduced energy use and SLA violations

    What the study found

    The study found that a proactive virtual machine (VM) consolidation framework called DTCF outperformed the compared method in high-load scenarios. In the reported experiments, it reduced energy consumption and SLA, or service level agreement, violations.

    Why the authors say this matters

    The authors say this matters because cloud data centers need to balance lower energy use with meeting SLA requirements. The study suggests that accounting for multiple resource dimensions and proactive control may help improve both efficiency and stability.

    What the researchers tested

    The researchers tested a framework for VM consolidation in cloud data centers called Dynamic Threshold Control and Three-Dimensional Resource Coordination Optimization Framework, or DTCF. It combined a hybrid workload model called Wavelet-TCN-LSTM with a three-dimensional predictive adaptive dynamic threshold mechanism, a dynamic multi-resource coupling impact weight policy, and a noise-aware physics-constrained deep reinforcement learning placement algorithm.

    What worked and what didn't

    According to the abstract, the framework used workload features and dynamic thresholds to prevent overloads proactively. It also used resource coupling information and a placement algorithm designed to work with heterogeneous hardware and strict resource constraints. In experiments on the Google Cluster Trace, DTCF reduced energy consumption by 23.2% and SLA violations by 43.5% compared with Fuzzy-GWO in high-load scenarios; the abstract does not report detailed cases where components failed.

    What to keep in mind

    The evidence described comes from experiments on the Google Cluster Trace, so the available summary does not show how the framework performs beyond that setting. The abstract also does not provide detailed limitations, failure modes, or a breakdown of the contribution of each component.

    • DTCF is a proactive VM consolidation framework for cloud data centers.
    • The framework is designed to consider multi-dimensional resource constraints, including disk I/O.
    • In the reported experiments, DTCF reduced energy consumption by 23.2% versus Fuzzy-GWO.
    • In the reported experiments, DTCF reduced SLA violations by 43.5% in high-load scenarios.
    • The abstract says the approach uses workload prediction, dynamic thresholds, and deep reinforcement learning for placement.

  • Data center self-assessment framework standardizes efficiency evaluation

    What the study found

    The authors present a Self-Assessment Tool (SAT) for evaluating data center thermal and energy performance. The tool uses IT and cooling data from monitoring systems, and it can also use historical data imported from external sensors.

    Why the authors say this matters

    The study suggests the tool addresses the difficulty of collecting data and calculating key performance indicators, or KPIs, within a reporting period. The authors conclude that a transparent and reproducible workflow can support independent implementation and assessment reporting.

    What the researchers tested

    The researchers developed a unified, modular, and extensible framework for data center assessment. They validated it on two pilot data centers in Denmark and Switzerland, using both real-time and historical datasets.

    What worked and what didn't

    The SAT calculates standardized thermal metrics, including RCI, RHI, RTI, RI, and LI, and energy metrics, including PUE and COP. In the pilot data centers, it automatically generated assessment reports with time-series visualizations, rack-level thermal maps, and energy-efficiency classifications based on the KPIs.

    What to keep in mind

    The abstract does not describe detailed limitations or failures. It also does not provide numerical performance comparisons, so the summary here is limited to what was stated.

    • The paper introduces a Self-Assessment Tool for data center thermal and energy evaluation.
    • The tool can use monitoring-system data or imported historical data from external sensors.
    • It calculates standardized thermal metrics and energy metrics, including PUE and COP.
    • The framework was validated on two pilot data centers in Denmark and Switzerland.
    • It automatically produced assessment reports with visualizations, thermal maps, and KPI-based classifications.

  • STCC middleware enabled adaptive Cassandra consistency evaluation

    What the study found

    The study presents a middleware-enforced consistency model called Strict Timed Causal Consistency, or STCC, for Apache Cassandra. It reports that this approach can be used as an external consistency layer for unmodified Cassandra deployments and compared with static consistency levels.

    Why the authors say this matters

    The authors say Cassandra’s built-in consistency levels are fixed and cannot adapt to changes in workload intensity or network conditions. They conclude that their middleware is meant to support adaptive consistency at runtime and reproducible study of consistency, latency, and energy trade-offs.

    What the researchers tested

    The researchers built a fully functional, open-source middleware implementing Strict Timed Causal Consistency for Cassandra. They evaluated it against the static levels ALL, QUORUM, and ONE using YCSB workloads on a 24-node Cassandra cluster spread across three data centers, with CPU frequency control and energy monitoring tools.

    What worked and what didn't

    The abstract says the system was validated on a 24-node cluster and publicly released with documentation. It also states that the framework includes automation for YCSB execution, CPU frequency control, and power monitoring, but it does not report specific comparative performance, energy, or consistency outcomes here.

    What to keep in mind

    The available summary does not provide detailed numerical results or describe which consistency setting performed best. The abstract also does not state specific limitations of STCC beyond its role as a configurable freshness-bound model.

    • The paper introduces Strict Timed Causal Consistency, a middleware layer for Cassandra.
    • The middleware works with unmodified Cassandra deployments.
    • The study compares STCC with the static levels ALL, QUORUM, and ONE.
    • Testing used YCSB workloads on a 24-node, three-data-center Cassandra cluster.
    • The abstract does not give detailed performance or energy results.

  • Hybrid deep learning scheduler reduced delay in edge-cloud simulations

    What the study found

    The study found that IntelliScheduler, a hybrid actor-critic deep reinforcement learning framework, improved task scheduling in an edge-cloud computing setting in simulation. The authors report better reward, lower training loss, lower operational cost, lower rejection rate, and better quality of experience (QoE, or overall service experience) than the comparison methods.

    Why the authors say this matters

    The authors say this matters because edge-cloud computing must coordinate edge and cloud resources efficiently when deadlines and workloads vary. The study suggests that a learning-based approach may be relevant for dynamic edge-cloud scheduling scenarios.

    What the researchers tested

    The researchers developed IntelliScheduler and a learning-based optimal task scheduling (LbOTS) algorithm. Their approach uses a runtime-aware state representation, a learning-based decision mechanism, a multi-buffer experience replay architecture, and latency-aware reward modeling, and they tested it through extensive simulation experiments under different workloads.

    What worked and what didn't

    LbOTS was reported to achieve up to 13% higher normalized reward and 67% lower training loss than PSO, MBO, and MOPSO baselines. The abstract also reports 52-66% lower operational cost, 80-90% lower rejection rate, and approximately 15-75% better QoE. The current assessment was simulation-based.

    What to keep in mind

    The reported results come from simulation experiments rather than a real deployment. The abstract does not describe detailed limitations beyond noting that the assessment is simulation-based.

    • IntelliScheduler is a hybrid actor-critic deep reinforcement learning framework for edge-cloud task scheduling.
    • The paper reports up to 13% higher normalized reward and 67% lower training loss than the listed baselines.
    • The study reports 52-66% lower operational cost and 80-90% lower rejection rate.
    • The authors say the approach produced approximately 15-75% better QoE.
    • The evaluation was conducted in simulation under different workloads.