Distributed Join-the-Idle-Queue for Low Latency Cloud Services

Wang, Chunpu; Feng, Chen; Cheng, Julian

doi:10.1109/tnet.2018.2869092

Cited by 22 publications

(9 citation statements)

References 18 publications

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“…In this, response time is given prime consideration as a performance metric. The proposed approach also derived expressions in semi-closed form for the delay in performance in JIQ (Wang et al , 2018).…”

Section: Related Workmentioning

confidence: 99%

A distributed quality of service-enabled load balancing approach for cloud environment

Sharma

Kumar

Jain

2022

IJPCC

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Purpose During high demand for the virtualized resources in cloud environment, efficient task scheduling achieves the desired performance criteria by balancing the load in the system. Design/methodology/approach It is a task scheduling approach used for load balancing in cloud environment. Task scheduling in such an environment is used for the task execution on a suitable resource by considering some parameters and constraints to achieve performance. Findings The presented mechanism is an extension of the previous proposed work quality of service (QoS)-enabled join minimum loaded queue (JMLQ) (Sharma et al., 2019c). The proposed approach has been tested in the CloudSim simulator, and the results show that the proposed approach achieves better results in comparison to QoS-enabled JMLQ and its other variants in the cloud environment. Originality/value 90%

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Section: Related Workmentioning

confidence: 99%

A distributed quality of service-enabled load balancing approach for cloud environment

Sharma

Kumar

Jain

2022

IJPCC

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“…Because of the high communication cost required to query all servers for their queue lengths, the JSQ-d (also called SQ(d) or Power-of-d) policy was proposed and analyzed, assuming homogeneous servers and exponential service times [15,27]. Other policies, such as Join-Idle-Queue (JIQ), have also been proposed as low-communication alternatives to JSQ [13,28].…”

Section: Related Workmentioning

confidence: 99%

Scalable Load Balancing in the Presence of Heterogeneous Servers

Gardner¹,

Jaleel²,

Wickeham³

et al. 2020

Preprint

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Heterogeneity is becoming increasingly ubiquitous in modern large-scale computer systems. Developing good load balancing policies for systems whose resources have varying speeds is crucial in achieving low response times. Indeed, how best to dispatch jobs to servers is a classical and well-studied problem in the queueing literature. Yet the bulk of existing work on large-scale systems assumes homogeneous servers; unfortunately, policies that perform well in the homogeneous setting can cause unacceptably poor performance-or even instability-in heterogeneous systems.We adapt the "power-of-d" versions of both the Join-the-Idle-Queue and Join-the-Shortest-Queue policies to design two corresponding families of heterogeneity-aware dispatching policies, each of which is parameterized by a pair of routing probabilities. Unlike their heterogeneity-unaware counterparts, our policies use server speed information both when choosing which servers to query and when probabilistically deciding where (among the queried servers) to dispatch jobs. Both of our policy families are analytically tractable: our mean response time and queue length distribution analyses are exact as the number of servers approaches infinity, under standard assumptions. Furthermore, our policy families achieve maximal stability and outperform well-known dispatching rules-including heterogeneity-aware policies such as Shortest-Expected-Delay-with respect to mean response time.

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“…These balancing approaches are provided as a service by popular cloud providers such as Windows Azure, Amazon ELB, and HP-CLB [30]. Also, Wang et al [31] and Lu et al [32] present the Join-Idle-Queue (JIQ) balancing algorithm that assigns incoming jobs to only idle resource queues in a single-tier environment. Multiple dispatchers are employed to hold incoming jobs; each dispatcher keeps IDs of idle resources in the tier.…”

Section: Background and Related Workmentioning

confidence: 99%

QOS-Driven Job Scheduling: Multi-Tier Dependency Considerations

Suleiman¹,

Basir²

2019

9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)

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For a cloud service provider, delivering optimal system performance while fulfilling Quality of Service (QoS) obligations is critical for maintaining a viably profitable business. This goal is often hard to attain given the irregular nature of cloud computing jobs. These jobs expect high QoS on an on-demand fashion, that is on random arrival. To optimize the response to such client demands, cloud service providers organize the cloud computing environment as a multi-tier architecture. Each tier executes its designated tasks and passes the job to the next tier; in a fashion similar, but not identical, to the traditional job-shop environments. An optimization process must take place to schedule the appropriate tasks of the job on the resources of the tier, so as to meet the QoS expectations of the job. Existing approaches employ scheduling strategies that consider the performance optimization at the individual resource level and produce optimal single-tier driven schedules. Due to the sequential nature of the multi-tier environment, the impact of such schedules on the performance of other resources and tiers tend to be ignored, resulting in a less than optimal performance when measured at the multi-tier level. In this paper, we propose a multi-tier-oriented job scheduling and allocation technique. The scheduling and allocation process is formulated as a problem of assigning jobs to the resource queues of the cloud computing environment, where each resource of the environment employs a queue to hold the jobs assigned to it. The scheduling problem is NP-hard, as such a biologically inspired genetic algorithm is proposed. The computing resources across all tiers of the environment are virtualized in one resource by means of a single queue virtualization. A chromosome that mimics the sequencing and allocation of the tasks in the proposed virtual queue is proposed. System performance is optimized at this chromosome level. Chromosome manipulation rules are enforced to ensure task dependencies are met. The paper reports experimental results to demonstrate the performance of the proposed technique under various conditions and in comparison with other commonly used techniques.

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Distributed Join-the-Idle-Queue for Low Latency Cloud Services

Cited by 22 publications

References 18 publications

A distributed quality of service-enabled load balancing approach for cloud environment

A distributed quality of service-enabled load balancing approach for cloud environment

Scalable Load Balancing in the Presence of Heterogeneous Servers

QOS-Driven Job Scheduling: Multi-Tier Dependency Considerations

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