2,836 research outputs found
Scheduling of data-intensive workloads in a brokered virtualized environment
Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments
Truthful Online Scheduling with Commitments
We study online mechanisms for preemptive scheduling with deadlines, with the
goal of maximizing the total value of completed jobs. This problem is
fundamental to deadline-aware cloud scheduling, but there are strong lower
bounds even for the algorithmic problem without incentive constraints. However,
these lower bounds can be circumvented under the natural assumption of deadline
slackness, i.e., that there is a guaranteed lower bound on the ratio
between a job's size and the time window in which it can be executed.
In this paper, we construct a truthful scheduling mechanism with a constant
competitive ratio, given slackness . Furthermore, we show that if is
large enough then we can construct a mechanism that also satisfies a commitment
property: it can be determined whether or not a job will finish, and the
requisite payment if so, well in advance of each job's deadline. This is
notable because, in practice, users with strict deadlines may find it
unacceptable to discover only very close to their deadline that their job has
been rejected
Simple Pricing Schemes for the Cloud
The problem of pricing the cloud has attracted much recent attention due to
the widespread use of cloud computing and cloud services. From a theoretical
perspective, several mechanisms that provide strong efficiency or fairness
guarantees and desirable incentive properties have been designed. However,
these mechanisms often rely on a rigid model, with several parameters needing
to be precisely known in order for the guarantees to hold. In this paper, we
consider a stochastic model and show that it is possible to obtain good welfare
and revenue guarantees with simple mechanisms that do not make use of the
information on some of these parameters. In particular, we prove that a
mechanism that sets the same price per time step for jobs of any length
achieves at least 50% of the welfare and revenue obtained by a mechanism that
can set different prices for jobs of different lengths, and the ratio can be
improved if we have more specific knowledge of some parameters. Similarly, a
mechanism that sets the same price for all servers even though the servers may
receive different kinds of jobs can provide a reasonable welfare and revenue
approximation compared to a mechanism that is allowed to set different prices
for different servers.Comment: To appear in the 13th Conference on Web and Internet Economics
(WINE), 2017. A preliminary version was presented at the 12th Workshop on the
Economics of Networks, Systems and Computation (NetEcon), 201
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments
This chapter presents software architectures of the big data processing
platforms. It will provide an in-depth knowledge on resource management
techniques involved while deploying big data processing systems on cloud
environment. It starts from the very basics and gradually introduce the core
components of resource management which we have divided in multiple layers. It
covers the state-of-art practices and researches done in SLA-based resource
management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
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
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