Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Lagén, Sandra; Pascual‐Iserte, Antonio; Muñoz, Olga; Vidal‐Alaball, Josep

doi:10.1109/tsp.2017.2778692

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…It must also improve resource utilization while trying to satisfy service level agreements (SLAs). Genetic algorithm, Ant Colony algorithm, linear programming, adaptive heuristics, and utility-based approaches are proposed for resource scheduling and VM migration [24][25][26][27][28][29][30][31][32][33][34]. For typical air-cooled data centers, researchers propose thermal-aware workload allocation strategy with respect to the chip temperature constraint [35] and use computational fluid dynamics (CFD) to model and validate Airflow in a Data Center [36].…”

Section: Related Workmentioning

confidence: 99%

Energy Aware Virtual Machine Scheduling in Data Centers

et al. 2019

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Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

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Section: Related Workmentioning

confidence: 99%

Energy Aware Virtual Machine Scheduling in Data Centers

et al. 2019

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“…A simple one-dimensional convex numerical optimization technique was used to resolve the objective function. Focusing on other scenarios where offloading services are provided by multiple virtual machines through backhaul links, 24 Lagen et al 25 optimized the offloading strategy at the user devices to minimize the energy consumption subject to latency constraint. The channel conditions at the air interface and the backhaul link capacities are considered in the development of the solution.…”

Section: Literature Reviewmentioning

confidence: 99%

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Dao

Jang

et al. 2018

Trans Emerging Tel Tech

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Recently, the emergence of fog computing and big internet of things (IoT) data have been considered as the main representatives identifying fifth‐generation mobile networks. In fifth generation, cloudization is extended from the core to the access tiers, referred to as fog radio access networks. Fog radio access networks provide ultralow latency offloading services to a massive number of IoT devices in their proximity. In this paper, we propose a joint energy and latency optimization (JELO) scheme for upstream IoT offloading services in fog radio access networks. The JELO scheme controls the offloaded task assignment among fog‐enabled eNodeBs (fog nodes) with strict consideration of the systematic resources and specific characteristics of individual tasks. The joint objective function aims at red optimizing the energy red consumption and offload latency for entire networks. Simulation results demonstrate that the JELO scheme outperforms existing approaches in terms of the energy consumption and load balancing while maintaining IoT service satisfaction.

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“…To solve the above multiple problems, the concept of mobile edge computing (MEC) has been proposed in recent years. MEC refers to the deployment of edge servers or computing nodes around the network [6]. Therefore, edge servers have a stronger computing capability than SMDs, and at the same time, edge servers are closer to users than remote cloud servers.…”

Section: Introductionmentioning

confidence: 99%

Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

Zhang

2020

IEEE Access

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As more and more compute-intensive and delay-sensitive applications are deployed on smart mobile devices, mobile edge computing is considered an effective way to solve the limited computing ability of smart mobile devices (SMDs). At present, latency has become the most critical indicator of the quality of service (QoS), and more and more studies focus on this aspect. Unlike previous work, our work fully takes into account the limited storage and computing ability of edge servers. To effectively reduce the delay of SMDs and improve QoS, we propose a Delay Control Strategy Joint Service Caching and Task Offloading (DCS-OCTO) in a three-tier mobile edge computing (MEC) system consist of multiuser , multiedge server and remote cloud servers. Some of the key challenges include service heterogeneity, unknown system dynamics, spatial demand coupling, and decentralized coordination. In particular, a very compelling but rarely studied issue is the dynamic service caching in the three-tier MEC system. The DCS-OCTO strategy is proposed based on Lyapunov optimization and Gibbs sampling. It works online without requiring prior information and achieves provable near-optimal performance. Finally, simulation results show that the strategy effectively reduces the overall system delay while ensuring low energy consumption.

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Energy Efficiency in Latency-Constrained Application Offloading From Mobile Clients to Multiple Virtual Machines

Cited by 13 publications

References 36 publications

Energy Aware Virtual Machine Scheduling in Data Centers

Energy Aware Virtual Machine Scheduling in Data Centers

Joint energy and latency optimization for upstream IoT offloading services in fog radio access networks

Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

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