1,124 research outputs found
A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce
Nowadays many companies have available large amounts of raw, unstructured
data. Among Big Data enabling technologies, a central place is held by the
MapReduce framework and, in particular, by its open source implementation,
Apache Hadoop. For cost effectiveness considerations, a common approach entails
sharing server clusters among multiple users. The underlying infrastructure
should provide every user with a fair share of computational resources,
ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In
this paper we consider two mathematical programming problems that model the
optimal allocation of computational resources in a Hadoop 2.x cluster with the
aim to develop new capacity allocation techniques that guarantee better
performance in shared data centers. Our goal is to get a substantial reduction
of power consumption while respecting the deadlines stated in the SLAs and
avoiding penalties associated with job rejections. The core of this approach is
a distributed algorithm for runtime capacity allocation, based on Game Theory
models and techniques, that mimics the MapReduce dynamics by means of
interacting players, namely the central Resource Manager and Class Managers
Coalition Formation and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing
In this paper we propose a two-stage protocol for resource management in a
hierarchically organized cloud. The first stage exploits spatial locality for
the formation of coalitions of supply agents; the second stage, a combinatorial
auction, is based on a modified proxy-based clock algorithm and has two phases,
a clock phase and a proxy phase. The clock phase supports price discovery; in
the second phase a proxy conducts multiple rounds of a combinatorial auction
for the package of services requested by each client. The protocol strikes a
balance between low-cost services for cloud clients and a decent profit for the
service providers. We also report the results of an empirical investigation of
the combinatorial auction stage of the protocol.Comment: 14 page
Service provisioning problem in cloud and multi-cloud systems
Cloud computing is a new emerging paradigm that aims to streamline the on-demand provisioning of resources as services, providing end users with flexible and scalable services accessible through the Internet on a pay-per-use basis. Because modern cloud systems operate in an open and dynamic world characterized by continuous changes, the development of efficient resource provisioning policies for cloud-based services becomes increasingly challenging. This paper aims to study the hourly basis service provisioning problem through a generalized Nash game model. We take the perspective of Software as a Service (SaaS) providers that want to minimize the costs associated with the virtual machine instances allocated in a multiple Infrastructures as a Service (IaaS) scenario while avoiding incurring penalties for execution failures and providing quality of service guarantees. SaaS providers compete and bid for the use of infrastructural resources, whereas the IaaSs want to maximize their revenues obtained providing virtualized resources. We propose a solution algorithm based on the best-reply dynamics, which is suitable for a distributed implementation. We demonstrate the effectiveness of our approach by performing numerical tests, considering multiple workloads and system configurations. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (5% on average but up to 260% for individual SaaS providers). Furthermore, varying the number of IaaS providers means an 8%-15% cost savings can be achieved from the workload distribution on multiple IaaSs
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
Cloud computing as a fairly new commercial paradigm, widely investigated by
different researchers, already has a great range of challenges. Pricing is a
major problem in Cloud computing marketplace; as providers are competing to
attract more customers without knowing the pricing policies of each other. To
overcome this lack of knowledge, we model their competition by an
incomplete-information game. Considering the issue, this work proposes a
pricing policy related to the regret minimization algorithm and applies it to
the considered incomplete-information game. Based on the competition based
marketplace of the Cloud, providers update the distribution of their strategies
using the experienced regret. The idea of iteratively applying the algorithm
for updating probabilities of strategies causes the regret get minimized
faster. The experimental results show much more increase in profits of the
providers in comparison with other pricing policies. Besides, the efficiency of
a variety of regret minimization techniques in a simulated marketplace of Cloud
are discussed which have not been observed in the studied literature. Moreover,
return on investment of providers in considered organizations is studied and
promising results appeared
Dynamic Pricing of Applications in Cloud Marketplaces using Game Theory
The competitive nature of Cloud marketplaces as new concerns in delivery of
services makes the pricing policies a crucial task for firms. so that, pricing
strategies has recently attracted many researchers. Since game theory can
handle such competing well this concern is addressed by designing a normal form
game between providers in current research. A committee is considered in which
providers register for improving their competition based pricing policies. The
functionality of game theory is applied to design dynamic pricing policies. The
usage of the committee makes the game a complete information one, in which each
player is aware of every others payoff functions. The players enhance their
pricing policies to maximize their profits. The contribution of this paper is
the quantitative modeling of Cloud marketplaces in form of a game to provide
novel dynamic pricing strategies; the model is validated by proving the
existence and the uniqueness of Nash equilibrium of the game
- …