Decentralized scalable fairshare scheduling

Östberg, Per-Olov; Espling, Daniel; Elmroth, Erik

doi:10.1016/j.future.2012.06.001

Cited by 16 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These efforts are not only centered on increasing the utilization but also to keep a more balanced usage among nodes [11], and/or delivering fairness among users, projects or virtual organizations [12]. For data centers, the efficient use of resources is mainly motivated by hardware and operational costs [13], and lately also by power consumption and environmental concerns [14], becoming a critical issue for large scale data centers.…”

Section: Data Center Utilization Problemsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

Abstract-Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time -changing the acceptable level of risk -depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50% increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.

show abstract

Section: Data Center Utilization Problemsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

“…Policies help service providers to prioritize jobs and/or resources (such as fair-share usage [19]). Forecasting and profiling can be used in order to collect more information about the jobs, their executions, their behavior within resources or users requests.…”

Section: Other Qos Mechanismsmentioning

confidence: 99%

“…In [59], the Fair Execution Time Estimation (FETE) scheduling is presented, which is based on completion time predictions and focused on minimizing risk for missed job deadlines. Finally, in [19] a Grid Fair-share Scheduling system is proposed (Aequus, before known as FSGrid) that aims to provide distributed fair balancing of resource utilization between Virtual Organizations in Grid environments. Aequus performs fair-share load balancing by influencing the order in which jobs are scheduled for execution.…”

Section: Qos Differentiationmentioning

confidence: 99%

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

Conejero

Caminero

Carrión

et al. 2014

Future Generation Computer Systems

View full text Add to dashboard Cite

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multigrid computing environments. Future generations computer systems, AbstractThe exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher re- source utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities. This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources' usage.

show abstract

“…Rigid allocation mechanisms such as accounting based limitations can restrict many cloud and grid computing use cases and [9] reduce the overall scientific productiveness. Contrary to credit or accounting based limitations, our approach motivates the users to optimize their resource usage through reduced power consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Both approaches were utilized in academic infrastructures even before the cloud era, but they both have serious downsides for academic uses. First, access rationing directly intrudes the freedom of researchers access to the infrastructure [9]. For example, when a credit system is applied for rationing, then the research of users with no credits could be postponed for indefinite time periods (i.e., until they acquire some new credits for computation).…”

Section: Introductionmentioning

confidence: 99%

An architecture to stimulate behavioral development of academic cloud users

Kecskeméti

Ostermann

Prodan

2014

Sustainable Computing: Informatics and Systems

View full text Add to dashboard Cite

Academic cloud infrastructures are constructed and maintained so they minimally constrain their users. Since they are free and do not limit usage patterns, academics developed such behavior that jeopardizes fair and flexible resource provisioning. For efficiency, related work either explicitly limits user access to resources, or introduce automatic rationing techniques. Surprisingly, the root cause (i.e., the user behavior) is disregarded by these approaches. This article compares academic cloud user behavior to its commercial equivalent. We deduce, that academics should behave like commercial cloud users to relieve resource provisioning. To encourage commercial like behavior, we propose an architectural extension to existing academic infrastructure clouds. First, every user's energy consumption and efficiency is monitored. Then, energy efficiency based leader boards are used to ignite competition between academics and reveal their worst practices. Leader boards are not sufficient to completely change user behavior. Thus, we introduce engaging options that encourage academics to delay resource requests and prefer resources more suitable for the infrastructure's internal provisioning. Finally, we evaluate our extensions via a simulation using real life academic resource request traces. We show a potential resource utilization reduction (by the factor of at most 2.6) while maintaining the unlimited nature of academic clouds.

show abstract

Decentralized scalable fairshare scheduling

Cited by 16 publications

References 6 publications

An Autonomic Approach to Risk-Aware Data Center Overbooking

An Autonomic Approach to Risk-Aware Data Center Overbooking

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

An architecture to stimulate behavioral development of academic cloud users

Contact Info

Product

Resources

About