6,517 research outputs found
Strategies for Replica Placement in Tree Networks
In this paper, we discuss and compare several policies to place replicas in
tree networks, subject to server capacity and QoS constraints. The client
requests are known beforehand, while the number and location of the servers are
to be determined. The standard approach in the literature is to enforce that
all requests of a client be served by the closest server in the tree. We
introduce and study two new policies. In the first policy, all requests from a
given client are still processed by the same server, but this server can be
located anywhere in the path from the client to the root. In the second policy,
the requests of a given client can be processed by multiple servers. One major
contribution of this paper is to assess the impact of these new policies on the
total replication cost. Another important goal is to assess the impact of
server heterogeneity, both from a theoretical and a practical perspective. In
this paper, we establish several new complexity results, and provide several
efficient polynomial heuristics for NP-complete instances of the problem. These
heuristics are compared to an absolute lower bound provided by the formulation
of the problem in terms of the solution of an integer linear program
Optimal Replica Placement in Tree Networks with QoS and Bandwidth Constraints and the Closest Allocation Policy
This paper deals with the replica placement problem on fully homogeneous tree
networks known as the Replica Placement optimization problem. The client
requests are known beforehand, while the number and location of the servers are
to be determined. We investigate the latter problem using the Closest access
policy when adding QoS and bandwidth constraints. We propose an optimal
algorithm in two passes using dynamic programming
Balancing the Migration of Virtual Network Functions with Replications in Data Centers
The Network Function Virtualization (NFV) paradigm is enabling flexibility,
programmability and implementation of traditional network functions into
generic hardware, in form of the so-called Virtual Network Functions (VNFs).
Today, cloud service providers use Virtual Machines (VMs) for the instantiation
of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a
typical scenario of Service Function Chains (SFCs), many important objectives
need to be met simultaneously, such as server load balancing, energy efficiency
and service execution time. The well-known \emph{VNF placement} problem
requires solutions that often consider \emph{migration} of virtual machines
(VMs) to meet this objectives. Ongoing efforts, for instance, are making a
strong case for migrations to minimize energy consumption, while showing that
attention needs to be paid to the Quality of Service (QoS) due to service
interruptions caused by migrations. To balance the server allocation strategies
and QoS, we propose using \emph{replications} of VNFs to reduce migrations in
DC networks. We propose a Linear Programming (LP) model to study a trade-off
between replications, which while beneficial to QoS require additional server
resources, and migrations, which while beneficial to server load management can
adversely impact the QoS. The results show that, for a given objective, the
replications can reduce the number of migrations and can also enable a better
server and data center network load balancing
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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