23,536 research outputs found
A Lagrangean Relaxation Based Scheme for Allocation of Bandwidth
In this paper we study the bandwidth packing problem in the presence of priority classes. The bandwidth packing problem is defined as the selection and routing of messages from a given list of messages with prespecified requirements on demand for bandwidth. The messages have to satisfy delay constraints and have to be routed over a network with given topology so that the revenue generated from routing these messages is maximized. Messages to be routed are classified into two priority classes. An integer programming based formulation of this problem is proposed and a Lagrangean relaxation based methodology is described for solving this problem. Several numerical experiments are conducted using a number of problem parameters such as percentage of messages, ratio of messages of lower to higher priority, capacity of links and high quality solutions to the bandwidth packing problem are generated under the different situations
A bandwidth theorem for approximate decompositions
We provide a degree condition on a regular -vertex graph which ensures
the existence of a near optimal packing of any family of bounded
degree -vertex -chromatic separable graphs into . In general, this
degree condition is best possible.
Here a graph is separable if it has a sublinear separator whose removal
results in a set of components of sublinear size. Equivalently, the
separability condition can be replaced by that of having small bandwidth. Thus
our result can be viewed as a version of the bandwidth theorem of B\"ottcher,
Schacht and Taraz in the setting of approximate decompositions.
More precisely, let be the infimum over all
ensuring an approximate -decomposition of any sufficiently large regular
-vertex graph of degree at least . Now suppose that is an
-vertex graph which is close to -regular for some and suppose that is a sequence of bounded
degree -vertex -chromatic separable graphs with . We show that there is an edge-disjoint packing of
into .
If the are bipartite, then is sufficient. In
particular, this yields an approximate version of the tree packing conjecture
in the setting of regular host graphs of high degree. Similarly, our result
implies approximate versions of the Oberwolfach problem, the Alspach problem
and the existence of resolvable designs in the setting of regular host graphs
of high degree.Comment: Final version, to appear in the Proceedings of the London
Mathematical Societ
Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments
Providing resource allocation with performance
predictability guarantees is increasingly important in cloud
platforms, especially for data-intensive applications, in 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. Existing
resource allocation solutions either assume that applications
manage their data transfer between their virtualized resources, or
that cloud providers manage their internal networking resources.
With the increased prevalence of brokerage services in cloud
platforms, there is a need for resource allocation solutions that
provides predictability guarantees in settings, in which neither
application scheduling nor cloud provider resources can be
managed/controlled by the broker. This paper addresses this
problem, as we define the Network-Constrained Packing (NCP)
problem of finding the optimal mapping of brokered resources
to applications with guaranteed performance predictability. We
prove that NCP is NP-hard, and we define two special instances
of the problem, for which exact solutions can be found efficiently.
We develop a greedy heuristic to solve the general instance of the
NCP problem , and we evaluate its efficiency using simulations
on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE
Energy Efficiency and Quality of Services in Virtualized Cloud Radio Access Network
Cloud Radio Access Network (C-RAN) is being widely studied for soft and green fifth generation of Long Term Evolution - Advanced (LTE-A). The recent technology advancement in network virtualization function (NFV) and software defined radio (SDR) has enabled virtualization of Baseband Units (BBU) and sharing of underlying general purpose processing (GPP) infrastructure. Also, new innovations in optical transport network (OTN) such as Dark Fiber provides low latency and high bandwidth channels that can support C-RAN for more than forty-kilometer radius. All these advancements make C-RAN feasible and practical. Several virtualization strategies and architectures are proposed for C-RAN and it has been established that C-RAN offers higher energy efficiency and better resource utilization than the current decentralized radio access network (D-RAN). This project studies proposed resource utilization strategy and device a method to calculate power utilization. Then proposes and analyzes a new resource management and virtual BBU placement strategy for C-RAN based on demand prediction and inter-BBU communication load. The new approach is compared with existing state of art strategies with same input scenarios and load. The trade-offs between energy efficiency and quality of services is discussed. The project concludes with comparison between different strategies based on complexity of the system, performance in terms of service availability and optimization efficiency in different scenarios
Improving the Spectral Efficiency of Nonlinear Satellite Systems through Time-Frequency Packing and Advanced Processing
We consider realistic satellite communications systems for broadband and
broadcasting applications, based on frequency-division-multiplexed linear
modulations, where spectral efficiency is one of the main figures of merit. For
these systems, we investigate their ultimate performance limits by using a
framework to compute the spectral efficiency when suboptimal receivers are
adopted and evaluating the performance improvements that can be obtained
through the adoption of the time-frequency packing technique. Our analysis
reveals that introducing controlled interference can significantly increase the
efficiency of these systems. Moreover, if a receiver which is able to account
for the interference and the nonlinear impairments is adopted, rather than a
classical predistorter at the transmitter coupled with a simpler receiver, the
benefits in terms of spectral efficiency can be even larger. Finally, we
consider practical coded schemes and show the potential advantages of the
optimized signaling formats when combined with iterative detection/decoding.Comment: 8 pages, 8 figure
VIRTUALIZED BASEBAND UNITS CONSOLIDATION IN ADVANCED LTE NETWORKS USING MOBILITY- AND POWER-AWARE ALGORITHMS
Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental results
Network-constrained packing of brokered workloads in virtualized environments
Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in 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. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798
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
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