70,785 research outputs found
Network File Storage With Graceful Performance Degradation
A file storage scheme is proposed for networks containing heterogeneous clients. In the scheme, the
performance measured by file-retrieval delays degrades gracefully under increasingly serious faulty
circumstances. The scheme combines coding with storage for better performance. The problem
is NP-hard for general networks; and this paper focuses on tree networks with asymmetric edges
between adjacent nodes. A polynomial-time memory-allocation algorithm is presented, which
determines how much data to store on each node, with the objective of minimizing the total
amount of data stored in the network. Then a polynomial-time data-interleaving algorithm is used
to determine which data to store on each node for satisfying the quality-of-service requirements in
the scheme. By combining the memory-allocation algorithm with the data-interleaving algorithm,
an optimal solution to realize the file storage scheme in tree networks is established
On Distributed Storage Allocations for Memory-Limited Systems
In this paper we consider distributed allocation problems with memory
constraint limits. Firstly, we propose a tractable relaxation to the problem of
optimal symmetric allocations from [1]. The approximated problem is based on
the Q-error function, and its solution approaches the solution of the initial
problem, as the number of storage nodes in the network grows. Secondly,
exploiting this relaxation, we are able to formulate and to solve the problem
for storage allocations for memory-limited DSS storing and arbitrary memory
profiles. Finally, we discuss the extension to the case of multiple data
objects, stored in the DSS.Comment: Submitted to IEEE GLOBECOM'1
Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics
The incredible increase in the volume of data emerging along with recent technological developments has made the analysis processes which use traditional approaches more difficult for many organizations. Especially applications involving subjects that require timely processing and big data such as satellite imagery, sensor data, bank operations, web servers, and social networks require efficient mechanisms for collecting, storing, processing, and analyzing these data. At this point, big data analytics, which contains data mining, machine learning, statistics, and similar techniques, comes to the help of organizations for end-to-end managing of the data. In this chapter, we introduce a novel high-performance computing system on the geo-distributed private cloud for remote sensing applications, which takes advantages of network topology, exploits utilization and workloads of CPU, storage, and memory resources in a distributed fashion, and optimizes resource allocation for realizing big data analytics efficiently
Communication-efficient Distributed Multi-resource Allocation
In several smart city applications, multiple resources must be allocated
among competing agents that are coupled through such shared resources and are
constrained --- either through limitations of communication infrastructure or
privacy considerations. We propose a distributed algorithm to solve such
distributed multi-resource allocation problems with no direct inter-agent
communication. We do so by extending a recently introduced additive-increase
multiplicative-decrease (AIMD) algorithm, which only uses very little
communication between the system and agents. Namely, a control unit broadcasts
a one-bit signal to agents whenever one of the allocated resources exceeds
capacity. Agents then respond to this signal in a probabilistic manner. In the
proposed algorithm, each agent makes decision of its resource demand locally
and an agent is unaware of the resource allocation of other agents. In
empirical results, we observe that the average allocations converge over time
to optimal allocations.Comment: To appear in IEEE International Smart Cities Conference (ISC2 2018),
Kansas City, USA, September, 2018. arXiv admin note: substantial text overlap
with arXiv:1711.0197
Scalable data abstractions for distributed parallel computations
The ability to express a program as a hierarchical composition of parts is an
essential tool in managing the complexity of software and a key abstraction
this provides is to separate the representation of data from the computation.
Many current parallel programming models use a shared memory model to provide
data abstraction but this doesn't scale well with large numbers of cores due to
non-determinism and access latency. This paper proposes a simple programming
model that allows scalable parallel programs to be expressed with distributed
representations of data and it provides the programmer with the flexibility to
employ shared or distributed styles of data-parallelism where applicable. It is
capable of an efficient implementation, and with the provision of a small set
of primitive capabilities in the hardware, it can be compiled to operate
directly on the hardware, in the same way stack-based allocation operates for
subroutines in sequential machines
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