2,037 research outputs found
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
Adaptive Dispatching of Tasks in the Cloud
The increasingly wide application of Cloud Computing enables the
consolidation of tens of thousands of applications in shared infrastructures.
Thus, meeting the quality of service requirements of so many diverse
applications in such shared resource environments has become a real challenge,
especially since the characteristics and workload of applications differ widely
and may change over time. This paper presents an experimental system that can
exploit a variety of online quality of service aware adaptive task allocation
schemes, and three such schemes are designed and compared. These are a
measurement driven algorithm that uses reinforcement learning, secondly a
"sensible" allocation algorithm that assigns jobs to sub-systems that are
observed to provide a lower response time, and then an algorithm that splits
the job arrival stream into sub-streams at rates computed from the hosts'
processing capabilities. All of these schemes are compared via measurements
among themselves and with a simple round-robin scheduler, on two experimental
test-beds with homogeneous and heterogeneous hosts having different processing
capacities.Comment: 10 pages, 9 figure
Supply chain collaboration
In the past, research in operations management focused on single-firm analysis. Its goal was to provide managers in practice with suitable tools to improve the performance of their firm by calculating optimal inventory quantities, among others. Nowadays, business decisions are dominated by the globalization of markets and increased competition among firms. Further, more and more products reach the customer through supply chains that are composed of independent firms. Following these trends, research in operations management has shifted its focus from single-firm analysis to multi-firm analysis, in particular to improving the efficiency and performance of supply chains under decentralized control. The main characteristics of such chains are that the firms in the chain are independent actors who try to optimize their individual objectives, and that the decisions taken by a firm do also affect the performance of the other parties in the supply chain. These interactions among firms’ decisions ask for alignment and coordination of actions. Therefore, game theory, the study of situations of cooperation or conflict among heterogenous actors, is very well suited to deal with these interactions. This has been recognized by researchers in the field, since there are an ever increasing number of papers that applies tools, methods and models from game theory to supply chain problems
Boosting Multi-Core Reachability Performance with Shared Hash Tables
This paper focuses on data structures for multi-core reachability, which is a
key component in model checking algorithms and other verification methods. A
cornerstone of an efficient solution is the storage of visited states. In
related work, static partitioning of the state space was combined with
thread-local storage and resulted in reasonable speedups, but left open whether
improvements are possible. In this paper, we present a scaling solution for
shared state storage which is based on a lockless hash table implementation.
The solution is specifically designed for the cache architecture of modern
CPUs. Because model checking algorithms impose loose requirements on the hash
table operations, their design can be streamlined substantially compared to
related work on lockless hash tables. Still, an implementation of the hash
table presented here has dozens of sensitive performance parameters (bucket
size, cache line size, data layout, probing sequence, etc.). We analyzed their
impact and compared the resulting speedups with related tools. Our
implementation outperforms two state-of-the-art multi-core model checkers (SPIN
and DiVinE) by a substantial margin, while placing fewer constraints on the
load balancing and search algorithms.Comment: preliminary repor
A Note on Market Power in an Emission Permits Market with Banking
In this paper, we investigate the effect of market power on the equilibrium path of an emission permits market in which firms can bank current permits for use in later periods. In particular, we study the market equilibrium for a large (potentially dominant) firm and a competitive fringe with rational expectations. Rather than providing a full description of the equilibrium solution for all combinations of permits allocations and cost structures, we provide a characterization of the equilibrium solution for a few illustrative cases. For example, we find that if the large firm enjoys a dominant position in the after-banking market, it can always extend this dominant position to the market during the banking period regardless of the allocation of the stock (bank) of permits.
ArcaDB: A Container-based Disaggregated Query Engine for Heterogenous Computational Environments
Modern enterprises rely on data management systems to collect, store, and
analyze vast amounts of data related with their operations. Nowadays, clusters
and hardware accelerators (e.g., GPUs, TPUs) have become a necessity to scale
with the data processing demands in many applications related to social media,
bioinformatics, surveillance systems, remote sensing, and medical informatics.
Given this new scenario, the architecture of data analytics engines must evolve
to take advantage of these new technological trends. In this paper, we present
ArcaDB: a disaggregated query engine that leverages container technology to
place operators at compute nodes that fit their performance profile. In ArcaDB,
a query plan is dispatched to worker nodes that have different computing
characteristics. Each operator is annotated with the preferred type of compute
node for execution, and ArcaDB ensures that the operator gets picked up by the
appropriate workers. We have implemented a prototype version of ArcaDB using
Java, Python, and Docker containers. We have also completed a preliminary
performance study of this prototype, using images and scientific data. This
study shows that ArcaDB can speed up query performance by a factor of 3.5x in
comparison with a shared-nothing, symmetric arrangement
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