Mohan Gurusamy

Lifetime maximization for connected target coverage in wireless sensor networks

In this paper, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink. We model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-Complete. We determine an upper bound on the network lifetime for the MCT problem and then develop a (1+w)H(M circ) approximation algorithm to solve it, where w is an arbitrarily small number, H(M circ)=1 lesilesM circ(1/i) and M circ is the maximum number of targets in the sensing area of any sensor. As the protocol cost of the approximation algorithm may be high in practice, we develop a faster heuristic algorithm based on the approximation algorithm called Communication Weighted Greedy Cover (CWGC) algorithm and present a distributed implementation of the heuristic algorithm. We study the performance of the approximation algorithm and CWGC algorithm by comparing them with the lifetime upper bound and other basic algorithms that consider the coverage and connectivity problems independently. Simulation results show that the approximation algorithm and CWGC algorithm perform much better than others in terms of the network lifetime and the performance improvement can be up to 45% than the best-known basic algorithm. The lifetime obtained by our algorithms is close to the upper bound. Compared with the approximation algorithm, the CWGC algorithm can achieve a similar performance in terms of the network lifetime with a lower protocol cost.

Provisioning fault-tolerant scheduled lightpath demands in WDM mesh networks

In this paper, we consider the problem of routing and wavelength assignment (RWA) of fault-tolerant scheduled lightpath demands (FSLDs) in all optical wavelength division multiplexing (WDM) networks under single component failure. In scheduled traffic demands, besides the source, destination, and the number of lightpath demands between a node-pair, their set-up and tear-down times are known, in this paper, we develop integer linear programming (ILP) formulations for dedicated and shared scheduled end-to-end protection schemes under single link/node failure for scheduled traffic demand with two different objective functions: 1) minimize the total capacity required for a given traffic demand while providing 100% protection for all connections; and 2) given a certain capacity, maximize the number of demands accepted while providing 100% protection for accepted connections. The ILP solutions schedule both the primary and end-to-end protection routes and assign wavelengths for the duration of the traffic demands. As the time disjointness that could exist among fault-tolerant scheduled lightpath demands is captured in our formulations, it reduces the amount of global resources required. The numerical results obtained from CPLEX indicate that dedicated scheduled (with set-up and tear-down times) protection provides significant savings (up to 33 %) in capacity utilization over dedicated conventional (without set-up and tear-down times) end-to-end protection scheme; shared scheduled protection provides considerable savings (up to 21 %) in capacity utilization over shared conventional end-to-end protection schemes. Also the numerical results indicate that shared scheduled protection achieves the best performance followed by dedicated scheduled protection scheme, and shared conventional end-to-end protection in terms of the number of requests accepted, for a given network capacity.

Load balancing using adaptive alternate routing in IP-over-WDM optical burst switching networks

In this paper, we address the issue of dynamic load balancing in wavelength division multiplexing (WDM)-based optical burst switching (OBS) networks. We propose a load balancing scheme based on adaptive alternate routing whose objective is to reduce burst loss through load balancing. The key idea of adaptive alternate routing is to reduce network congestion by adaptively distributing the load between two pre-determined link-disjoint alternative paths based on the measurement of the impact of traffic load on each of them. Through extensive simulation experiments for different traffic scenarios, we show that the proposed dynamic load balancing algorithms outperforms the shortest path routing and static alternate routing algorithms.

Maximizing Network Lifetime for Connected Target Coverage in Wireless Sensor Networks

Network lifetime is one of the critical issues in sensor networks. An effective approach to prolong the network lifetime is to schedule the active states of sensors: only a subset of the deployed sensors that can maintain both sensing coverage and network connectivity is scheduled to be active. In this paper, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity among all the active sensors and the sink. We model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-complete. We give an upper bound on lifetime of the MCT problem and develop a heuristic algorithm called communication weighted greedy cover (CWGC) algorithm to solve it. We study the performance of CWGC algorithm comparing it with other algorithms that consider the coverage and connectivity problems independently. Simulation results show that CWGC algorithm performs much better than others in terms of the network lifetime and the lifetime obtained by our algorithm is close to the upper bound

An Online Integrated Resource Allocator for Guaranteed Performance in Data Centers

As bandwidth is shared in a best-effort way in todays data centers, traffic generated between a set of VMs (virtual machines) affect the traffic between another set of VMs (possibly belonging to another tenant) sharing the same physical links, leading to unpredictable performance of applications running on these VMs. This article addresses the problem of allocation of not only server resources (computational and storage) but also network bandwidth, to provide performance guarantees in multi-tenant data centers. Bandwidth being a critical shared-resource, we formulate the problem as an optimization problem that minimizes bandwidth demand between clusters of VMs of a tenant; and we prove it as NP-hard. We develop fast online heuristics as an integrated resource allocator (IRA) that decides on the admission of dynamically arriving requests, and allocates resources for the accepted ones. We also present a modified version of IRA, called B-IRA that bounds the cost of bandwidth allocation, while exploring smaller search space for solution. We demonstrate that, IRA accommodates significantly higher number of requests in comparison to a load-balancing resource allocator (LBRA) that does not consider reducing bandwidth between clusters of VMs. IRA also outperforms B-IRA when traffic demands of VMs in an input are not localized.

Connected K-target coverage problem in wireless sensor networks with different observation scenarios

In this paper, we consider the problem of scheduling sensor activities to maximize network lifetime while maintaining both discrete K-target coverage and network connectivity. In K-target coverage, it is required that each target should be simultaneously observed by at least K sensors. The data generated by the sensors will be transmitted to the sink node via single or multiple hop communications. As maintaining discrete target coverage cannot guarantee the network connectivity, we consider both target coverage and connectivity issues. Further, by adopting a more realistic energy consumption model, we consider the sensor activity scheduling problem and routing problem jointly. We study the problem with two observation scenarios depending on whether a sensor can distinguish the targets in its sensing area or not. For the first scenario, a more general scenario where each sensor can simultaneously observe multiple targets is considered and we develop a polynomial-time algorithm which can achieve optimal solution based on linear programming and integer theorem. For the second scenario, we show that the problem is NP-complete and develop an approximation algorithm for solving it. As the protocol cost of the optimal solution and the approximation algorithm may be high in practice, we develop a low-cost heuristic algorithm which can be implemented in a distributed fashion for both scenarios. We demonstrate the effectiveness of the heuristic algorithm through extensive simulations.

Scheduling and Routing of Sliding Scheduled Lightpath Demands in WDM Optical Networks

We develop a time conflict resolving window division algorithm which places a given set of sliding scheduled lightpath demands within their allowed interval and two routing and wavelength assignment (RWA) algorithms, and study their performance.

Efficient multi-layer operational strategies for survivable IP-over-WDM networks

This paper addresses the problem of achieving a balance in satisfying a network service providers requirements: maintaining an acceptable call acceptance rate, satisfying protection requirements of requests, and controlling the signaling overhead in case of a component failure, in IP-over-WDM networks in a dynamic traffic arrival scenario. Satisfying all these aspects is a difficult task especially when the traffic pattern is dynamic in nature and a single layer protection approach is followed. In this work, first we propose a multi-layer protection scheme for achieving a better and acceptable tradeoff between blocking performance and signaling overhead in IP-over-WDM networks. We define various operational settings in the proposed scheme and investigate their impacts on the performance of the scheme. An important feature of this scheme is that, these settings allow a network service provider to select a suitable operational strategy for achieving the desired tradeoff based on networks policy and traffic demand. Then we propose an adaptive protection approach for dynamic traffic in consideration of providing better protection to requests as much as possible while considering blocking performance and signaling overhead. Through simulation experiments we evaluate the performance of the proposals and demonstrate their effectiveness

Towards Flexible Guarantees in Clouds: Adaptive Bandwidth Allocation and Pricing

This article focuses on the problem of bandwidth allocation to users of Cloud data centers. An interesting approach is to use advance bandwidth reservation. Such systems usually assume all requests demand either bandwidth-guarantee (BG) or time-guarantee (TG), but not both. Hence the solutions are tailored for one type of requests. A BG request demands guarantee on bandwidth; whereas a TG request demands guarantee on time for transfer of data of specified volume. We define a new model that allows users to not only submit both kinds of requests, but also specify flexible demands. We tie up the problem of bandwidth allocation with differential pricing, that gives discounts to users based on the flexibility in their requests. We propose a two-phase, adaptive and flexible bandwidth allocator (A-FBA) that, in one phase admits and allocates minimal bandwidth to dynamically arriving user requests, and in another phase, allocates additional bandwidth for accepted requests maximizing revenue. The problem formulated in first phase is NP-hard, while the second phase can be solved in polynomial time. We show that, in comparison to a traditional deterministic model, the A-FBA not only increases the number of accepted requests significantly, but also does so by generating higher revenues.

Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.