794 research outputs found
H-FfMRA: A multi resource fully fair resources allocation algorithm in heterogeneous cloud computing
The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings
Incast mitigation in a data center storage cluster through a dynamic fair-share buffer policy
Incast is a phenomenon when multiple devices interact with only one device at a given time. Multiple storage senders overflow either the switch buffer or the single-receiver memory. This pattern causes all concurrent-senders to stop and wait for buffer/memory availability, and leads to a packet loss and retransmission—resulting in a huge latency. We present a software-defined technique tackling the many-to-one communication pattern—Incast—in a data center storage cluster. Our proposed method decouples the default TCP windowing mechanism from all storage servers, and delegates it to the software-defined storage controller. The proposed method removes the TCP saw-tooth behavior, provides a global flow awareness, and implements the dynamic fair-share buffer policy for end-to-end I/O path. It considers all I/O stages (applications, device drivers, NICs, switches/routers, file systems, I/O schedulers, main memory, and physical disks) while achieving the maximum I/O throughput. The policy, which is part of the proposed method, allocates fair-share bandwidth utilization for all storage servers. Priority queues are incorporated to handle the most important data flows. In addition, the proposed method provides better manageability and maintainability compared with traditional storage networks, where data plane and control plane reside in the same device
Tromino: Demand and DRF Aware Multi-Tenant Queue Manager for Apache Mesos Cluster
Apache Mesos, a two-level resource scheduler, provides resource sharing
across multiple users in a multi-tenant cluster environment. Computational
resources (i.e., CPU, memory, disk, etc. ) are distributed according to the
Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive
resources based on their current usage and are responsible for scheduling their
tasks within the allocation. We have observed that multiple frameworks can
cause fairness imbalance in a multiuser environment. For example, a greedy
framework consuming more than its fair share of resources can deny resource
fairness to others. The user with the least Dominant Share is considered first
by the DRF module to get its resource allocation. However, the default DRF
implementation, in Apache Mesos' Master allocation module, does not consider
the overall resource demands of the tasks in the queue for each user/framework.
This lack of awareness can result in users without any pending task receiving
more resource offers while users with a queue of pending tasks starve due to
their high dominant shares. We have developed a policy-driven queue manager,
Tromino, for an Apache Mesos cluster where tasks for individual frameworks can
be scheduled based on each framework's overall resource demands and current
resource consumption. Dominant Share and demand awareness of Tromino and
scheduling based on these attributes can reduce (1) the impact of unfairness
due to a framework specific configuration, and (2) unfair waiting time due to
higher resource demand in a pending task queue. In the best case, Tromino can
significantly reduce the average waiting time of a framework by using the
proposed Demand-DRF aware policy
Exploring the Fairness and Resource Distribution in an Apache Mesos Environment
Apache Mesos, a cluster-wide resource manager, is widely deployed in massive
scale at several Clouds and Data Centers. Mesos aims to provide high cluster
utilization via fine grained resource co-scheduling and resource fairness among
multiple users through Dominant Resource Fairness (DRF) based allocation. DRF
takes into account different resource types (CPU, Memory, Disk I/O) requested
by each application and determines the share of each cluster resource that
could be allocated to the applications. Mesos has adopted a two-level
scheduling policy: (1) DRF to allocate resources to competing frameworks and
(2) task level scheduling by each framework for the resources allocated during
the previous step. We have conducted experiments in a local Mesos cluster when
used with frameworks such as Apache Aurora, Marathon, and our own framework
Scylla, to study resource fairness and cluster utilization. Experimental
results show how informed decision regarding second level scheduling policy of
frameworks and attributes like offer holding period, offer refusal cycle and
task arrival rate can reduce unfair resource distribution. Bin-Packing
scheduling policy on Scylla with Marathon can reduce unfair allocation from
38\% to 3\%. By reducing unused free resources in offers we bring down the
unfairness from to 90\% to 28\%. We also show the effect of task arrival rate
to reduce the unfairness from 23\% to 7\%
Datacenter Traffic Control: Understanding Techniques and Trade-offs
Datacenters provide cost-effective and flexible access to scalable compute
and storage resources necessary for today's cloud computing needs. A typical
datacenter is made up of thousands of servers connected with a large network
and usually managed by one operator. To provide quality access to the variety
of applications and services hosted on datacenters and maximize performance, it
deems necessary to use datacenter networks effectively and efficiently.
Datacenter traffic is often a mix of several classes with different priorities
and requirements. This includes user-generated interactive traffic, traffic
with deadlines, and long-running traffic. To this end, custom transport
protocols and traffic management techniques have been developed to improve
datacenter network performance.
In this tutorial paper, we review the general architecture of datacenter
networks, various topologies proposed for them, their traffic properties,
general traffic control challenges in datacenters and general traffic control
objectives. The purpose of this paper is to bring out the important
characteristics of traffic control in datacenters and not to survey all
existing solutions (as it is virtually impossible due to massive body of
existing research). We hope to provide readers with a wide range of options and
factors while considering a variety of traffic control mechanisms. We discuss
various characteristics of datacenter traffic control including management
schemes, transmission control, traffic shaping, prioritization, load balancing,
multipathing, and traffic scheduling. Next, we point to several open challenges
as well as new and interesting networking paradigms. At the end of this paper,
we briefly review inter-datacenter networks that connect geographically
dispersed datacenters which have been receiving increasing attention recently
and pose interesting and novel research problems.Comment: Accepted for Publication in IEEE Communications Surveys and Tutorial
Karma: Resource Allocation for Dynamic Demands
The classical max-min fairness algorithm for resource allocation provides
many desirable properties, e.g., Pareto efficiency, strategy-proofness and
fairness. This paper builds upon the observation that max-min fairness
guarantees these properties under a strong assumption -- user demands being
static over time -- and that, for the realistic case of dynamic user demands,
max-min fairness loses one or more of these properties.
We present Karma, a generalization of max-min fairness for dynamic user
demands. The key insight in Karma is to introduce "memory" into max-min
fairness -- when allocating resources, Karma takes users' past allocations into
account: in each quantum, users donate their unused resources and are assigned
credits when other users borrow these resources; Karma carefully orchestrates
exchange of credits across users (based on their instantaneous demands, donated
resources and borrowed resources), and performs prioritized resource allocation
based on users' credits. We prove theoretically that Karma guarantees Pareto
efficiency, online strategy-proofness, and optimal fairness for dynamic user
demands (without future knowledge of user demands). Empirical evaluations over
production workloads show that these properties translate well into practice:
Karma is able to reduce disparity in performance across users to a bare minimum
while maintaining Pareto-optimal system-wide performance.Comment: Accepted for publication in USENIX OSDI 202
Optimizing Service Differentiation Scheme with Sized-based Queue Management in DiffServ Networks
In this paper we introduced Modified Sized-based Queue Management as a
dropping scheme that aims to fairly prioritize and allocate more service to
VoIP traffic over bulk data like FTP as the former one usually has small packet
size with less impact to the network congestion. In the same time, we want to
guarantee that this prioritization is fair enough for both traffic types. On
the other hand we study the total link delay over the congestive link with the
attempt to alleviate this congestion as much as possible at the by function of
early congestion notification. Our M-SQM scheme has been evaluated with NS2
experiments to measure the packets received from both and total link-delay for
different traffic. The performance evaluation results of M-SQM have been
validated and graphically compared with the performance of other three legacy
AQMs (RED, RIO, and PI). It is depicted that our M-SQM outperformed these AQMs
in providing QoS level of service differentiation.Comment: 10 pages, 9 figures, 1 table, Submitted to Journal of
Telecommunication
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