Scheduling deadline-constrained bulk data transfers to minimize network congestion

Chen, Binbin; Primet, Pascale Vicat-Blanc

doi:10.1109/ccgrid.2007.102

Cited by 37 publications

(24 citation statements)

References 14 publications

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“…Achieving efficient utilization of hardware resources has been a research goal since long in data centers, and involves techniques at different scales: single processors (such as prefetching techniques [6]), networks on chip (NoCs) (such as virtualization for NoCs [7]), routing algorithms [8], network multiplexing [9], or time sharing systems [10]. 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].…”

Section: Data Center Utilization Problemsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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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.

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Section: Data Center Utilization Problemsmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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“…For link l, denote y l as the 5 In dynamic setting, the allocation of rates to maximize deadline completion is NP-Complete [9], and D 3 chooses a greedy approach. 6 MCP was first explored in our earlier paper [11] with preliminary simulation results for only type 1 flows.…”

Section: Ms(t)−τs(t)xs(t) δS(t)−τs(t)mentioning

confidence: 99%

Scheduling Mix-flows in Commodity Datacenters with Karuna

Chen

Bai

et al. 2016

Proceedings of the 2016 ACM SIGCOMM Conference

129

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Cloud applications generate a mix of flows with and without deadlines. Scheduling such mix-flows is a key challenge; our experiments show that trivially combining existing schemes for deadline/non-deadline flows is problematic. For example, prioritizing deadline flows hurts flow completion time (FCT) for non-deadline flows, with minor improvement for deadline miss rate.We present Karuna, a first systematic solution for scheduling mix-flows. Our key insight is that deadline flows should meet their deadlines while minimally impacting the FCT of non-deadline flows. To achieve this goal, we design a novel Minimal-impact Congestion control Protocol (MCP) that handles deadline flows with as little bandwidth as possible. For non-deadline flows, we extend an existing FCT minimization scheme to schedule flows with known and unknown sizes. Karuna requires no switch modifications and is backward compatible with legacy TCP/IP stacks. Our testbed experiments and simulations show that Karuna effectively schedules mix-flows, for example, reducing the 95th percentile FCT of non-deadline flows by up to 47.78% at high load compared to pFabric, while maintaining low (<5.8%) deadline miss rate.

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“…It has been shown [5] that the optimal network congestion factor does not change if we restrict the solution space to the practical multi-interval scheduling schemes, which only use bandwidth allocation profile taking the form of a step function. A n-piece step function λ(t) defined over time window ω = [η, ψ] can be represented as:…”

Section: Problem Formulationmentioning

confidence: 99%