Load balancing (LB)

There is continual demand for greater computation power from computer system there is currently possible area requiring great computational speed include numerical problem of scientific and engineering problem. Multiprocessor system is very efficient at evaluate task with uniform communication and computation pattern. Complex method and Algorithm use to solve problem in parallel system in order to obtain a well balanced overall load of the system . The goal of our Algorithm to proper load balancing the multicomputer system and analysis about what amount of workload should we give to the cooperative slave .processor.

[9] Guopu.Zha, Sam Kwong (2010) “Gbest-guided Artificial bee Colony algorithm for numerical function optimization”, Elsevier 3166-3173 [10] Jing Yao, Ju-hou He(2013)” Load Balancing Strategy of Cloud Computing based on Artificial Bee Algorithm”,IJNCM, vol 3,no 1 [11] Kobra etminami, Prof.M.Naghibzadeh (2007)” A Min-Min Max-Max selective algorithm for Grid task scheduling”,IEEE,1-4244-1007-X/07 [12] Kavya S.A, M.V Panduranga Rao,S.Basavaraj Patil(2012)”Grid computing for load balancing strategies”,International journal of science and

LTE heterogeneous network. Femtocells are plug and play devices connected to the operator’s network by a broadband connection (e.g., cable or xDSL). Thus, these networks are prone to unplanned deployment in many cases (the client is free to locate the femtocell anywhere). The authors in [18], proposed a method to adjust hyster- esis margins depending on an estimation of the distance from the base station to the user equipment (UE), reduces the number of redundant handovers (HOs) while keeping the throughput of femtocells as high as possible. The work shown in [19] studied the persistent congestion problems on traffic distribution in LTE fem- tocell enterprise scenario. That work implemented and compared several traffic sharing algorithms that tune femtocell transmission power and handover margins, following a fuzzy logic controller (FLC) scheme in order to automatically adjust femtocell parameters. Neverthe- less, temporarily overloaded cells issues were not ad- dressed in that work. The study of [20] analyzed the importance of the femtocell capacity in terms of the number of active users for mobility load balancing in temporal overloaded situations. However, the user loca- tion was not considered in that study.

Monitoring as a Service (Maas) provides monitoring for security such as outward thread, susceptibility exposure, monitoring for trouble shooting, monitoring for service level agreement (SLA) compliance and quality of services (QOS) [15].Have a need of having decidedly available resource to service the request on demand where the consumers pay for the resource utilized promptly, so cloud is a pay-go model [3]. To distribute the large amount of workload over a node using the load balancing technique. In the load balancing contribute the load over the node. To distribute or divide the load among the node considering some prediction. Dynamic resource can be effectively managed on a cloud computing platform using the load balancing concept [1]. In general, load balancing algorithm can be sketchily categorized as Dynamic or Static, Centralized or Decentralized, Periodic or Non-Periodic and those with threshold or without threshold [5]. Load balancing in the cloud computing based on standard load balancing but differs from classical thinking on load-balancing architecture and implementation by using commodity servers to perform the load balancing, which provides for new opportunities and economies of scale as well as presenting its own unique set of challenges

Figure 6 shows that the number of switch supported by a controller depends on its load ρ . Cbench application is a performance measurement tool designed to compare packages processed by OpenFlow controllers. It can simulate packets from OpenFlow controllers. Mininet is an OpenSources tool for emulating SDN networks. The mapping table has been stored on the Zookeeper servers. It can support, store services with strong consistency so that the result of the choices of switch to migrate is consistent with the expected results. During the experiment were used three distributed Floodlight controllers likewise having a charge rate ρ of 0, 8. The modules for load measurement, load collection, decision and switch migration are implemented on each controller. All controllers are implemented so that as soon as they reach 80% of their maximum load, an alert is issued by the decision component of the load balancing module.

To improve the performance It should keep in mind above design concepts. That is above things should not be overridden in any case. If file migration is used to balance the load the naming scheme in metadata management should give same transparency to the file system. Load balancing can be achieved with two approaches. First one is Static Load balancing and Second one is Dynamic approach to solve load balancing problem. Static load balancing is performed in the initialization phase of computation[4].Dynamic load balancing is done during the computation process. Dynamic load balancing is further classified according to migration operation as direct and iterative load balancing [5]. In direct load balancing load is a one step process to balance the load while iterative process require more steps to achieve a load balanced state. Iterative dynamic load balancing is the method used for load balancing further classified as Diffusion Method and Dimension exchange method. In Diffusion method surplus lead is diffused from highly loaded nodes to lightly loaded nodes. In this method, the local load balanced state is achieved first and then automatically global state achieved. For hypercube topology, the dimension exchange method is used. This all the above concept is for load balancing in highly parallel distributed computing system. Load balancing in distributed file systems is done with file allocation and file migration strategies[6]. Based on the file migration approach, A dynamic and Adaptive load balancing strategy for parallel file system with large scale I/O servers is latest SALB [7] algorithm dynamic load balancing. Dynamic load balancing is achieved with the two different ways in distributed system such as centralized load balancing and Distributed load balancing

In the existing load balancing algorithms, there are some drawbacks like increase in waiting time, context switching and increase in response time. Because of the increase in waiting time the cost also increases. As a result turnaround time also increases. In Equally Spread Current Execution Algorithm, the main drawback is scanning the queue frequently. This results in additional computational overhead. In throttled load balancing algorithm the major drawback is that the index table is scanned again and again until or unless the particular virtual machine is available for allocating the resources. The overload is a major issue in data center. To overcome the drawbacks in load balancing algorithms, this paper proposes Enhanced Throttled Load Balancing Algorithm which supports the load balancing and can provide better results. This algorithm provides efficient throughput with less turnaround time and low waiting time.

Y. Lua et al. [11] proposed a Join-Idle-Queue load balancing algorithm for dynamically scalable web services. This algorithm provides large-scale load balancing with distributed data centers by, first load balancing idle processors across dispatchers for the availability of idle processors at each dispatcher and then, assigning jobs to processors to reduce average queue length at each processor. By removing the load balancing work from the critical path of request processing, it effectively reduces the system load, incurs no communication overhead at job arrivals and does not increase actual response time [9]. Authors in [12] have recommended load balancing in a three–level cloud computing network, by using a scheduling algorithm which combines the features of Opportunistic Load Balancing (OLB) and Load Balance Min-Min (LBMM) which can utilize better executing efficiency and maintain load balancing of the system. The objective is to select a node based for executing the complicated tasks that needs large-scale computation. The scheduling algorithm proposed in this paper is not dynamic and also there is an overhead involved in the selection of the node.

For e.g. suppose we are having a server to which large number of requests are allocated at the same time but since we are having limited number of resources (bandwidth, storage, CPU etc.), the server will down soon. This problem can be solved with the concept of load balancing in which multiple instances of that server will be made and that load will be distributed evenly among those instances.

Since, with the scene of rapid use of cloud computing resources, the demand along with provisioning of the cloud resources has to be effectively design to claim better SLA (Service Level Agreement) with zero downtime claims. Currently, there is a presence of multiple vendors offering cloud services (Amazon, Microsoft, IBM, Google, Salesforce, HP, Oracle, Citrix, EMC etc.) and there are growing numbers of clientele too. Hence, it is quite obvious that catering the massive and dynamic requirements of such exponentially growing clients will become one of the most challenging issues. And to mitigate this issue, an effective load balancing technique should be explored. The proposed paper will introduce a thorough analysis of the evolution of cloud platform right from the origination of the initial distributed computing system. The paper will mainly focus on the research issues of load balancing. The main contributions of this paper are summarized below.

load balancing and improper load balancing . in the improper load balancing we obtain the non linier structure means they cannot finish their work in same time . their head of contact line is zigzag up and down if we take the graph between load and execution time. perfect load balancing oppose to improper load balancing where line of contact head of processor is smoothly and perpendicular of time axis. Perfect load balancing is sometimes rare so we try to obtain the graph just smoothly. Cluster computing load balancing has been an active research area and therefore many different assumption and terminology are independent suggested .the amount of time the communicator spend to finish or complete the work called workload time of the communicator. The master_ slave communication modal for load balancing used both in centralised and distributed platform. Load could be storage , bandwidth etc. many researcher work on load balancing for many year target to obtain the load balance scheame with overhead as low as possible. We purpose the algorithm which work in dynamic environment to use the dynamic approach . in this algorithm we will have implement multiple job to multiple communicator. The mapping between communicator and job is one to one at the same time in starting but if any communicator finish their job earliar then it can couple to another communicator dynamically .in this way response time reduce and performance increases . our algorithm is FCPDA (fully centralised and partially distributed algorithm).

Abstract —Randomized load balancing is a cost efficient policy for job scheduling in parallel server queueing systems whereby, with every incoming job, a central dispatcher randomly polls some servers and selects the one with the smallest queue. By exactly deriving the jobs’ delay distribution in such systems, in explicit and closed form, Mitzenmacher [13] proved the so-called ‘power-of-two’ result, which states that the random polling of only two servers yields an exponential improvement in delay over randomly selecting a single server. Such a fundamental result, however, was obtained in an asymptotic regime in the total number of servers, and does do not necessarily provide accurate estimates for practical finite regimes with small or moderate number of servers. In this paper we obtain stochastic lower and upper bounds on the jobs’ average delay in non-asymptotic/finite regimes, by extending ideas for analyzing the particular case of the Join-the-Shortest-Queue (JSQ) policy. Numerical illustra- tions indicate not only that the (lower) bounds are remarkably accurate, but also that the asymptotic approximation can be misleading in scenarios with a small number of servers, and especially at very high utilizations.

Abstract Distributing workloads across multiple computing resources are one of the major challenges in a cloud computing environment. This paper is being discussed over the basic obstacles of load balancing in cloud environment. The paper looks beyond the problems faced by the cloud system to overcome those through probable improvised techniques. This is a paper over solving the problems exist in the present days by logically analyzing and presenting in an algorithmic format. This approach is mainly focused on an effective job queue making strategy which is suitably allocated the various jobs to CPUs based on their priority or without priority. It also deals with some of the major problems of load balancing in cloud environment like, timeout. Finally, it shows how this approach is fitted in famous AWS and GAE cloud architecture partially. This article will provide the readership an overview of various load balancing problems in cloud environment while also simulating further interest to pursue more advanced research in it.

Load balancing in a network can be achieved if the routing protocol used is a load aware routing protocol (a routing protocol that includes a load balancing scheme in its route discovery). This research proposes a modification to the popular AODV routing protocol to make it perform the route discovery process while keeping load balancing in mind. The proposed routing protocol is the AODV routing protocol enhanced with load balancing capability. The routing protocol selects a route to the destination based on the current load of the intermediate nodes and selects a gateway from the available network gateway nodes based on its current load. To achieve this load balancing solution, a maximum load will be defined (based on the number of packets buffered in the queue) on each node beyond which it will not allow any incoming flow through it. The new traffic flow must find another alternative route to the destination. In WMNs for broadband access most of the traffic flows in the network are from/to internet through a gateway node, this technique also balance the load across the gateway nodes by allowing the gateway with a load greater than the defined maximum to reject the flow and then use another available gateway.

In recent years, many papers have illustrated the potential of geographical load balancing to provide significant cost savings for data centers, e.g., [32, 42, 45, 46, 48, 53] and the references therein. The goal of the current thesis is different. Our goal is to explore the social impact of geographical load balancing systems. In particular, geographical load balancing aims to reduce energy costs, but this can come at the expense of increased total energy usage: by routing to a data center farther from the request source to use cheaper energy, the data center may need to complete the job faster, and so use more service capacity, and thus energy, than if the request was served closer to the source.

Mithun Dsouza et. al. in [10] described that “Cloud computing has become popular due to its attractive features. The load on the cloud is increasing tremendously with the development of new applications. Load balancing is an important part of cloud computing environment which ensures that all devices or processors perform same amount of work in equal amount of time. In this paper we are mentioned about different techniques in load, we aim to provide a structured and comprehensive overview of the research on load balancing algorithms in cloud computing. This paper surveys the state of the art load balancing tools and techniques over the period of 2004-2016.”

[7] Mr. M. Ajit, Ms. G. Vidya, “VM Level Load Balancing in Cloud Environment”, Computing, Communications and Networking Technologies (ICCCNT),2013 Fourth International Conference, July 2013, Pages:1-5. [8] Ektemal Al-Rayis, Heba Kurdi, “Performance Analysis of

Wireless sensor actor networks (WSANs) consist of a large amount of sensor nodes with low cost and little actor nodes with better processing capabilities. The actor nodes tend to get partitioned due to low actor density in case of economic considerations. So, the communication among actors requires sensor nodes to relay his data to the destination actor which lead to the bottleneck in communication. A high-throughput disjoint multi-path (HTDM) routing scheme is proposed in this paper which allows actor simultaneously forwards data through multiple disjoint paths to achieve high throughput. HTDM routing scheme can quickly establish multiple disjoint routing paths, which can increase the throughput among actors a lot, and the routing path can be adjusted dynamically according to the energy status of their nodes, which can achieve load balancing. Simulation results show that, compared with related routing schemes, HTDM routing scheme can achieve higher throughput performance and distribute transmission loads more evenly to most of the nodes in the network.

Grid computing is an extension of distributed computing that incorporates coordinating and sharing of computational power, data storage and network resources across dynamic and geographically dispersed organizations. Rapid growth in use of computers has increased the number of applications which uses the shared hardware and software resources (e.g. memory, processor, files etc.) and ultimately increased the amount of submitted tasks across internet. Problem can be solved if we distribute the applications across different computer, in such a manner that it reduces the task response time and the overhead on a single computer. Proper distribution of applications across different available resources is termed as Load Balancing [4]. The Computational Grid category represents a system that has a higher aggregate computational capacity available for single applications. These can be additionally subdivided into circulated supercomputing and high throughput classes relying upon how the total limit is used. A circulated supercomputing Grid executes the application in parallel on different machines to decrease the finish time of a task.

[24] Y. Zhao, and W. Huang, “Adaptive Distributed Load Balancing Algorithm based on Live Migration of Virtual Machines in Cloud”, Proceedings of 5th IEEE International Joint Conference on INC, IMS and IDC, Seoul, Republic of Korea, August 2009, pages 170- 175.