Threshold-based Load Balancing Research Articles

On Quantum Algorithms for Random Walks in the Nonnegative Quarter Plane

It is well known that strong connections exist between random walks (RWs) in the nonnegative quarter plane and the mathematical performance modeling, analysis and optimization of computer systems and communication networks. Examples include adaptive threshold-based load balancing in concert with affinity scheduling (e.g., [16, 10]), scheduling parallel jobs on parallel computer systems (e.g., [15]), and asymptotic scalabilty in wireless and linear loss networks (e.g., [9]).

Optimal Load Balancing in Cloud Environment of Virtual Machines

Cloud resource scheduling is gaining prominence with the increasing trends of reliance on cloud infrastructure solutions. Numerous sets of cloud resource scheduling models were evident in the literature. Cloud resource scheduling refers to the distinct set of algorithms or programs the service providers engage to maintain the service level allocation for various resources over a virtual environment. The model proposed in this manuscript schedules resources of virtual machines under potential volatility aspects, which can be applied for any priority metric chosen by the server administrators. Also, the model can be flexible for any time frame-based analysis of the load factor. The model discussed in this manuscript relies on the Bollinger Bands tool for understanding the potential volatility aspects of a Virtual Machine. The experimental study of the model compared to the contemporary load balancing model called STLB (Starvation Threshold-based Load Balancing) refers to a simple and potential model that can be more pragmatic for sustainable ways of load balancing.

Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

Self-Learning Threshold-Based Load Balancing

We consider a large-scale service system where incoming tasks have to be instantaneously dispatched to one out of many parallel server pools. The user-perceived performance degrades with the number of concurrent tasks and the dispatcher aims at maximizing the overall quality of service by balancing the load through a simple threshold policy. We demonstrate that such a policy is optimal on the fluid and diffusion scales, while only involving a small communication overhead, which is crucial for large-scale deployments. In order to set the threshold optimally, it is important, however, to learn the load of the system, which may be unknown. For that purpose, we design a control rule for tuning the threshold in an online manner. We derive conditions that guarantee that this adaptive threshold settles at the optimal value, along with estimates for the time until this happens. In addition, we provide numerical experiments that support the theoretical results and further indicate that our policy copes effectively with time-varying demand patterns. Summary of Contribution: Data centers and cloud computing platforms are the digital factories of the world, and managing resources and workloads in these systems involves operations research challenges of an unprecedented scale. Due to the massive size, complex dynamics, and wide range of time scales, the design and implementation of optimal resource-allocation strategies is prohibitively demanding from a computation and communication perspective. These resource-allocation strategies are essential for certain interactive applications, for which the available computing resources need to be distributed optimally among users in order to provide the best overall experienced performance. This is the subject of the present article, which considers the problem of distributing tasks among the various server pools of a large-scale service system, with the objective of optimizing the overall quality of service provided to users. A solution to this load-balancing problem cannot rely on maintaining complete state information at the gateway of the system, since this is computationally unfeasible, due to the magnitude and complexity of modern data centers and cloud computing platforms. Therefore, we examine a computationally light load-balancing algorithm that is yet asymptotically optimal in a regime where the size of the system approaches infinity. The analysis is based on a Markovian stochastic model, which is studied through fluid and diffusion limits in the aforementioned large-scale regime. The article analyzes the load-balancing algorithm theoretically and provides numerical experiments that support and extend the theoretical results.

Threshold load balancing with weighted tasks

We study threshold-based load balancing protocols for weighted tasks. We are given an arbitrary graph G with n nodes (resources, bins) and m≥n tasks (balls). Initially the tasks are distributed arbitrarily over the n nodes. The resources have a threshold and we are interested in the balancing time, i.e., the time it takes until the load of all resources is below or at the threshold. We distinguish between resource-based and user-based protocols. In the case of resource-based protocols, resources with a load larger than the threshold are allowed to send tasks to neighboring resources. In the case of user-based protocols, the tasks make the migration decisions and we restrict ourselves to the complete graph in the model. Any task allocated to a resource with a load above the threshold decides whether to migrate to a neighboring resource independently of the other tasks.For resource-controlled protocols, we present results for arbitrary graphs. For the user-controlled migration, we consider complete graphs and derive bounds for both above-average and tight thresholds.

A threshold-based load balancing algorithm for grid computing systems

This paper proposes a dynamic workload’s threshold-based scheduling and load balancing algorithm for distributed computing systems. It takes into account the dynamicity and heterogeneity of available system resources, and tasks. It automatically updates the workload thresholds upon any significant change of any resource workload and balances the system’s workload using the new thresholds. Unlike most of the current scheduling and load balancing algorithms, the proposed algorithm uses the resource’s load as a workload index instead of number of tasks. The number of tasks is not a good workload index especially when the weights of tasks are different. A node can be overloaded even if it has a number of tasks less than that in an under-loaded node. A simulation model is built to evaluate the performance of the proposed algorithm. The performance of the proposed algorithm is evaluated and compared with that of Min–Min, and Ant Colony scheduling algorithms using mean task response time, average utilization of processing nodes, and load balancing level as performance metrics. The results show that the proposed algorithm reduces the task mean response time, improves resources utilization, and load balancing level compared with the other two studied algorithms in the range of system’s parameters values examined.

Threshold-Based Load Balancing for Multiple Home Agents in Mobile IP Networks

Mobile IP is an extension of the IP protocol, designed to provide seamless connectivity to mobile nodes roaming over the Internet. Under certain conditions of traffic, such as in Mobile IP networks supporting multimedia applications, overhead can cause significant delays at the mobility agents, i.e. foreign and home agents. Some multiple home agents protocol extensions have been proposed to avoid potential bottlenecks in single home agents configurations. Due to the highly unpredictable nature of the IP data traffic, efficient load balancing applied in such schemes is crucial to the overall performance improvement. In this paper, we propose a novel threshold-based dynamic load balancing policy that uses a realistic data-sharing model operational at each home agent. A discrete event computer simulator is developed to study the system performance with the proposed load balancing policy. We also introduce two new policy tuning parameters, the number of information queue slices and the retry latency, by which we control the load balancing benefit. Due to the relatively large number of simulation parameters, results are presented for typical configuration scenarios. In general, in all the results obtained, our threshold-based load balancing policy provides a significant performance improvement. The proposed architecture efficiently uses parallel processing for handling the overhead generated by the load balancing, which results in minimal performance degradation.

Threshold-based load balancing policies in heterogeneous distributed systems

This paper presents a new load balancing policy for heterogeneous distributed systems, in which the servers with heavy load can distribute their tasks to other servers. The proposed policy is based on thresholds to determine whether the current server is heavily loaded or not. The threshold is defined as the ratio of service rates between adjacent server pairs. The current load of a server is indicated by the queue length. The scheduler compares the current load with the thresholds to decide the load status, and then determines if some tasks should be migrated to other servers. For studying the performance of response time, the Markov process is used to model the behaviour of the heterogeneous distributed system under the proposed threshold policy. Additionally, the equations derived from this model are solved by using the Matrix-Geometric solution technique. Finally, the performance of the proposed policy is compared with that of the generic load balancing policy, the policy with no load balancing, and the ideal load balancing policy. The results show that the proposed policy is better than that of other policies and close to the ideal load balancing policy.

Design and performance of a threshold-based load balancing scheme in an ATM switch

We consider an ATM switch consisting of a number of output-buffered ATM switch elements operating in parallel. Such an architecture may lead to uneven distribution of cells over the parallel switch element buffers, if no modifications to the design is made. With this motivation, we propose a threshold based load balancing method to evenly distribute the cells over the parallel switch element buffers. We present the details of how the load balancing method can be implemented. Our computer simulations show that the proposed load balancing method significantly reduces the cell loss probability. The study is done with two input traffic models: random traffic and bursty traffic.