Abstract— Traffic Engineering (TE) is most effective in networks where some links are heavily utilized and have little or no bandwidth available while others carry little or no traffic. It is of great importance to the recent development of mobile and wireless technologies. Without the process of TE, there is possibilities of having under-utilization and over-utilization problems along the links. It is necessary to consider the implementation that would avoid the goal of network and unguaranteed bandwidth delivery. Therefore, the operators and service providers require seamless combination of network protocols with an improved quality of service (QoS). This paper will be focusing on ResourceReservationProtocol Tunnelling Extension Multiprotocol Layer Switching (RSVP-TE MPLS) for sustainable mobile wireless networks. This will make provision of bandwidth allocation possible by implementing the configurations of the dynamic and static LSPs (Label Switching Paths). The network model designed will be used for this purpose by using simulation approach. The verification of the MPLS model will be presented. It will eventually maximize bandwidth utilization, minimize operation cost and improve QoS.
Abstract: Constrained netwrks like Wireless Sensor Networks have been identified as a promising scheme for next-generation wireless networks. These networks are capable of capturing data from the physical world without human intervention possessing applications such as IoT in various fields of life that require reliable and précised end to end delivery. However, Wireless Sensor Networks inevitably suffers from severe resource constraints and hence promising the provision of desired QoS is a challenge. In most of the applications like military, medical surveillance. Data captured are critical and hence the transmission of such data entails a minimal end to end delay. In constrained networks achieving minimal delay with effective utilization of resources are important cost factors for achieving an end to end delivery. In this Paper, a Software- defined Networking (SDN), based resourcereservationprotocol, which leverages SDN to centrally process the whole control logic and accordingly decides the amount of resources to be allocated for each data flow alleviating the processing overhead of all other nodes thus minimizing the energy consumption is proposed. The proposed algorithm is evaluated through simulation and the results obtained proved the efficiency of the proposed protocol by effectively minimizing the system’s energy consumption and end to end delay.
ABSTRACT: Today Internet Plays an important role in our day-to-day life. Basically, WAN connections are expensive in ISP’s budget and it is most important to deliver the services with QoS to its customers. Traffic Engineering is one of the solution when routers are overloaded. Generally, all routers in the network uses the shortest path. By this all routers are overloaded and traffic occurs.This results in packet delay or dropping in the network. The main objective of this project is to forward the packets using tunnels, when the traffic occurs. The headend router uses ResourceReservationprotocol to reserve the bandwidth to forward the packets. And finally the enhanced Quality of Service (QoS) is achieved by this project.
The linear topology for typical pipeline deployments requires linear placement of the WSN nodes. In its active operation, the LRRCC scheme must leveraged on ResourceReservationProtocol module (RSVP) and is achieved through the cooperation of three processes: the RSVP- Application interface process, the RSVP process, and the traffic control process for the system architecture. Again, for the sensor nodes and the sinks, RSVP is enabled to minimize the data drop in the unicast flows to the upstream sinks. Every sensor node sends messages to the sinks where the event are sensed. Because all sensors nodes (downstream and upstream) are data sources, all of them relays their data from their upstream neighbors toward the DRN and then via the DDN to the NCC. Each sensor cannot move after deployment. The sensors are controlled by the LRRCC algorithm implemented on the existence
This paper presents a new resource selection algorithm in a bidding based grid environments to minimize the total time for task completion in effective and efficient way. Our algorithm proposes the single reservation mechanism to reserve the best resource for task execution as a commitment and hence guarantees the task completion time will be as expected. The proposed Algorithm tackles the problems arise when using traditional reserved and non-reserved resource bidding selection mechanisms. Now we are in the implementation phase and we plan to conduct an experiment in grid testbed using Globus Toolkit as a middleware to evaluate the algorithm. In future we plan to upgrade this model into dynamic reservation model. Start:
Abstract. Hadoop YARN is an Apache Software Foundation’s open project that provides a resource management framework for large scale parallel data processing, such as MapReduce jobs. Fair scheduler is a dispatcher which has been widely used in YARN to assign resources fairly and equally to applications. However, there exists a problem of the Fair scheduler when the resource requisition of applications is beyond the amount that the cluster can provide. In such a case, the YARN system will be halted if all resources are occupied by ApplicationMasters, a special task of each job that negotiates resources for processing tasks and coordinates job execution. To solve this problem, we propose an automatic and dynamic admission control mechanism to prevent the ceasing situation happened when the requested amount of resources exceeds the cluster’s resource capacity, and dynamically reserve resources for processing tasks in order to obtain good performance, e.g., reducing makespans of MapReduce jobs. After collecting resource usage information of each work node, our mechanism dynamically predicts the amount of reserved resources for processing tasks and automatically controls running jobs based on the prediction. We implement the new mechanism in Hadoop YARN and evaluate it with representative MapReduce benchmarks. The experimental results show the effectiveness and robustness of this mechanism under both homogeneous and heterogeneous workloads.
In the slot reservation stage, a group of participants collaborate each other to construct a vector that contains a set of slots in it, all with same length. In this each participant is allocated with separate slots. The allocated slots will be unique. The slot reservation for each participant is done by a third server authenticator which is already present in the system. At the time of slot reservation a reservation message which consists of a pseudonym and the length of the data is created for each participant the slots that are allocated will be only known by the third server authenticator and the particular participant only.
A resource may be a logical entity, such as a distributed file system, computer cluster, or distributed computer pool . The physical entities such as printer, scanner also forms the resource. As a whole the resources includes computing resources, data resources, storage resources, service resources, network resources and web resources. All these resources are heterogeneous and dynamic in nature. And efficient utilization of resources is required for the problem solving in grid environment . The grid resources can be categorized as computing resource, data resource, storage resource, service resource, network resource, web services. These resources are of dynamic in nature as it can enter or leave the environment at any time. Similarly, the resources are heterogeneous in nature as it belongs to any environment or platform.
This paper focuses on the interpretation of the “Open” category in different types of social reservations. The word “OPEN” is a double meaning word. The word “Open” means something opens for all caste and groups. Another interpretation is someone who is not included in the reserve category, what happens in the misinterpretation of the “OPEN” category as not open in the realistic sense, but classified as another caste category within the caste hierarchy, so how does caste manipulate democracy?, How do binaries of orthodox caste thinking dominate over Indian liberal democratic imaginary? According to social reservation policy fifty percent reserve and fifty percent open means the last fifty percent is not reserve for any caste or category. This implies that this is OPEN to all people irrespective of caste or category; it is eligibly on merit basis.
In computing clouds, burstiness of a virtual machine (VM) workload widely exists in real applications, where spikes usually occur aperiodically with low frequency and short duration. This could be effectively handled through dynamically scaling up/down in a virtualization-based computing cloud; however, to minimize energy consumption, VMs are often highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the dynamic runtime resource demands of VMs in a PM, some VMs haveto be migrated to some other PMs, which may cause potential performance degradation. In this paper, we investigate the burstinessaware server consolidation problem from the perspective of resourcereservation.
3 The approach in existing QoS architectures is, where possible, to mask out change and hence present a stable service to the application (effectively providing a level of network transparency). This is achieved through combining QoS functions such as admission control, resourcereservation and maintenance. In our view, such an approach is not sustainable in a mobile environment. In such an environment, the emphasis should not be on transparency but on making information available to applications and empowering applications to make the necessary changes. Such an approach relies on the provision of QoS management functions supporting monitoring and adaptation. To complement this, the underlying system must be amenable to adaptation, including the ability to adapt to periods of disconnection. This is not to say that existing approaches to QoS management are invalidated. Rather, strategies such as admission control and resourcereservation can be used for component networks and can provide guarantees while the end system remains connected to that particular network. The architecture must, however, accommodate changes in underlying network service through the additional mechanisms we seek.
Reservation Scheduling with Priorities and Benefit Functions: The algorithm is based on the following underlying assumptions. Once a request is granted reservation, a contract for the reservation is signed between the application and the system. The reservation scheduler won’t examine the same request more than once except when a QoS violation occurred. This situation should be handled by a higher level QoS broker that engages in renegotiation to establish another reservation or a continuation of the current reservation. Based on the operating policies, the reservation scheduler may find another reservation or the application may operate under best-effort conditions. In this algorithm, each reservation request involves a single resource, i.e., no co-reservation of resources is considered here . Figure 1 shows the outline of the dynamic reservation scheduler. In this scheduler, dynamically arriving requests are collected for a predefined time interval to form a meta-request.
over the existing methods in terms of packet delivery ratio, average end-to-end delay and routing packet overhead. G. Santhi et al.  propose an agent based Multi-Constrained QoS aware multicast routing scheme based on MAODV (MC_MAODV) which uses a set of static and mobile agent. It depicts QoS multicast model with multiple constraints which may deal with bandwidth reservation, delay constraint and packet loss to multicast session. Evgeny Khorov et al.  proposed an approach of using multiple metrics simultaneously, with one of the metrics which they call optimizable, reflecting consuming network resources, and other metrics which they call restrictive, reflecting QoS requirements. If a route length goes beyond a threshold in at least one of the restrictive metrics, the route shall not be chosen for packet delivery, to escape network resources waste. So, the best route is chosen in an optimizable metric, in the class of routes allowed by restrictive metrics. The approach is applicable for both unicast and multicast traffic in MANETs. Hua Chen et al.  proposed an entropy-based genetic algorithm (GA) to support QoS multicast routing in mobile ad hoc networks (EQMGA). They construct a new metric-entropy and select the long-life path with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network. Golla Varaprasad  proposed a minimum-energy-broadcast tree. It performs based on Prim’s algorithm. The received-signal- power in the BIP model is r−∞, where r is the transmission range and ∞ is a parameter that typically takes value between 2 and 4. A brief analysis of different QoS based multicast routing and their characteristics has been as shown in table 1 [2, 13, 20, 21].
This paper proposes the use of implicit EDF  to pro- vide real-time guarantees to the network traﬃc while using nearly all the communication medium bandwidth. The price to pay is an extra overhead required for system synchroniza- tion. Implicit EDF is a time-triggered medium access control discipline in which all nodes implement in parallel an EDF queue of all communication requests. Collisions are avoided by replicating and executing the EDF scheduler in parallel in all nodes, in a tightly synchronized way. This means that all local EDF schedulers generate precisely the same sched- ule which corresponds to implementing a single global EDF queue of ready messages. In this model, every node knows when to transmit and receive, even in the presence of hid- den nodes. The protocol uses a slotted framework in which messages are allocated an integer number of fixed duration slots.
-------------------------------------------------------------------ABSTRACT--------------------------------------------------------------- Computing infrastructure provisioning in Infrastructure as a Service (IaaS) cloud is done in the form of virtual machines. Haizea, a resource lease manager, provides four types of leases: Immediate, Best Effort (BE), Advance Reservation (AR) and Dead Line Sensitive (DLS). AR leases are most privileged leases with “AR preempts other leases” policy, since they can preempt & suspend other BE leases when demanded by consumers. This leads to two problems: 1) a set of BEs can go suspended infinite number of time & 2) ARs, at the actual time of their resource allocation, can be rejected due to presence of other ARs in schedule. This work proposes two algorithms 1) Starvation-Removal and 2) AR-to-BE Conversion to solve these problems. Experimental results of the proposed algorithms successfully demonstrate that we can stop starvation of BE leases for resources and effectively improve request acceptance rate.
In time synchronization based McMAC protocols, each node is equipped with one transceiver, and clock synchro- nization is required. An example is the Multi-channel MAC (MMAC) protocol , where time is divided into beacon intervals. All nodes in the network negotiate the data channels over a common channel during the Ad-Hoc Traffic Indication Messages (ATIM) window, and then transmit the following data packets over the selected data channels. However, the multi-channel hidden terminal problem is resolved based on time synchronization, which is a difficult task involving con- siderable overhead and complexity.
Resourcereservation algorithm is one of the key technologies  , so the design of resourcereservation is important for satellite laser network through ground OBS technology use for reference. Now scholars have put forward many algorithms, such as classical JIT (Just In Time)  , JET(Just Enough Time)  , and DRR (Differenced ResourceReservation)  which can provide QoS for high priority data, RRBS (ResourceReservation Burst Stream)  which is an onboard resourcereservation algorithm based on burst stream. These are based on C-OBS architecture. An onboard E-OBS network resourcereservation algorithm is brought out considering many factors such as satellite resource limit, remote space between satellites  , low received optical power  , optical memory limit, and high continuous transmission probability, fairness and delay and so on. Simulation results verify the algorithm can provide high success ration in high load and improve packet ration and end to end delay performance.
Consider a situation where a series of buyers enter the seller’s store until a sale is made: there is just a single unit to be sold. Buyers have no costs of waiting or negotiating, and they care only about the difference between their reservation value and the price they pay. The seller maximizes her expected profit. A key assumption is that the seller is able to commit to a strategy, and that each buyer knows this. Also, the seller does not know any given buyer’s reservation price, but knows the distribution of reservation prices over potential buyers. If the seller knew the buyer’s reservation price, she would just wait for the buyer with the highest reservation price, ask for that price and not budge.
In the case of best effort jobs the JBS uses a very simple but practical approach to minimize the waiting time. The job is dispatched to the top three sites of the candidate list. This technique is referred to as Resource Manager Selective Flooding (RMSF). As soon as one of these jobs gets active, all other jobs are canceled. This approach avoids orphan jobs and ensures that the overhead caused by an allocation of more than necessary resources is minimized. However, there are some disadvantages: For example, since the LRMS decides when a job is started, no commitments with respect to a certain start time or deadline can be given. Thus, it is not possible to plan e. g. the staging of large files. Therefore, the JBS relies on advance reservation whenever possible. B. Advance Reservation Service