Dynamic access load balancing on the parallel secondary storage

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

In this paper, we classify the dynamic distributed load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP), and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate-based load balancing via virtual routing (RLBVR) and queue-based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) and perfect information algorithm, which were introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing abilities under varying load conditions (light, moderate, or high) and the minimization of the mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behaviors under some influencing parameters, such as load on the system and status exchange intervals. We also extend our experimental verification to large scale cluster systems such as a mesh architecture, which is widely used in real-life situations. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations

Dear SCPE Reader, We present a selection of papers which are extensions of papers presented at the 7-th International Symposium on Parallel and Distributed Computing, 1–5 July 2008, in Krakow, Poland. The motivation for publishing the selection in the SCPE Journal was, on the one hand, to present the flavour of the research reported at the conference and on the other hand to present some of the most relevant topics currently focused on the research on parallel and distributed computing in general. The selection contains only 6 papers out of about 60 presented at the conference, and thus, is far from covering all relevant topics represented at the ISPDC 2008. This is because not all of the invited authors were patient enough to accept a fairly long paper publishing process. Nevertheless, we hope that the presented papers will bring you closer to the research covered by the ISPDC conferences and will encourage you to participate in future ISPDC editions. The first paper ``The Impact of Workload Variability on Load Balancing Algorithms'' is by Marta Beltran and Antonio Guzman from King Juan Carlos University in Spain. It concerns an important topic of load balancing in cluster systems, namely adaptativity of the load balancing algorithms to changes of the workload in the system. Adequate accounting for additional load in the hosting system is of great relevance for correct optimization effects. The paper presents a thorough formal analysis of the workload variability metrics and their influence on the quality of load balancing algorithms. Four basic activities appearing in load balancing algorithms are identified, and based on them some algorithmic solutions are proposed to correctly deal with workload variability in system load balancing. The problem of dynamic load balancing algorithms robustness has been discussed. Two different robustness metrics sensitive to the applied type of opimization: local task-oriented or a global one enable selecting task remote execution or migration as load balancing operations. The proposed approach is illustrated with experiments. The second paper ``Model-Driven Engineering and Formal Validation of High-Performance Embedded Systems'' is by Abdoulaye Gamatie, Eric Rutten, Huafeng Yu, Pierre Boulet, Jean-Luc Dekeyser, from University of Lille and INRIA in France. The paper is concerned with a very advanced methodology of designing correct parallel embedded systems for intensive data-parallel computing. In their previous research, the authors of the paper designed the GASPARD embedded system design framework. It is based on the hardware/software co-design approach through model-driven engineering. The framework is based on an UML-like model specification language in which hardware and software elements are modelled using a component approach with special mechanisms for repetitive structures. This paper tries to combine the modelling framework of GASPARD with the mechanisms of synchronous languages to achieve design verifiability provided for such languages. The paper shows how GASPARD models can be translated into synchronous models based on data flow equations in order to formally check their correctness. The proposed approach is illustrated with an example of a video processing system. The third paper ``Relations Between Several Parallel Computational Models'' is by Stephan Bruda and Yuanqiao Zhang from Bishop’s University in Canada. The paper is concerned with theoretical aspects of shared memory systems described by the parallel random access machine PRAM model and aims in studying performance properties of different types of PRAM systems. The attention is focussed on analysing the computational power of two more sophisticated PRAM models (Combining CRCW and Broadcast Selective Reduction), which include data reduction in case of concurrent writes. The paper shows that these two models have equivalent computational power, which is a new result comparing the existing literature. The performance of both models applied to reconfigurable multiple bus machines was studied as a possible architectural solution for current VLSI processor implementations. It was shown that in such systems under reasonable assumptions concurrent-write does not enhance performance comparing the exclusive-write model. Another result important for the VLSI technology is that the Combining CRCW PRAM model (in which data of concurrent writes are arthmetically or logically combined before write) and the exclusive-write on directed reconfigurable busses perform in equivalent way under strong real-time requirements. The fourth paper ``Experiences with Mesh-Like Computations Using Prediction Binary Trees'' is by Gennaro Cordasco, Biagio Cosenza, Rosario de Chiara, Ugo Erra and Vittorio Scarano from the University ``degli Studi'' of Salerno and the University ``degli Studi della Biasilicata'' of Potenza in Italy. The paper concerns optimization methods for mesh-like computations in clusters of processors. The computations are perfomed assuming a phase-like program execution control using a tiling approach which reduces inter-processor communication. A temporal coherence is also assumed, which means that task sizes provide similar execution times in consecutive phases. Temporary coherent computations are structured in a Prediction Binary Tree, in which leaves represent computing tiles to be mapped to processors. A phase-by-phase semi-static load balancing is introduced to the scheduling algorithm. The scheduling algorithm is equipped with a predictor, which estimates the computation time of next phase tiles based on previous execution times and modifies the tiles to achieve balanced execution in phases. For this, two heuristics are used to leverage on data locality in processors. The proposed approach is illustrated by the example of interactive rendering with Parallel Ray Tracing algorithm. The fifth paper ``The Influence of the IBM pSeries Servers Virtualization Mechanism on Dynamic Resource Allocation in AIX 5L'' is by Maciej Mlynski from ASpartner Limited in Poland. The paper concerns a very up-to-date problem of system virtualization and presents the results of research carried on IBM pSeries servers. IBM is strongly developing the virtualization technique especially on IBM pSeries servers enabling an improved and flexible sharing of system resources between applications. The paper investigates novel facilities for dynamic resource management such as micro-partitioning and partition load manager. They enable dynamic creation of workload logical partitions of system resources and their dynamic mangement. It includes run-time resource re-alocation between logical partitions including setting of sharing specifications as well as run-time adding/removing/setting parameters of resources in the system. It remains an open question how to properly tune parameters of the operating system using the provided virtualization facilities to obtain the best efficiency for a given application program. The paper presents the results of experiments which study the effects of tuning the disk subsystem parameters under the IBM AIX 5L operating system with the use of the provided virtualization facilities on the resulting application execution performance. The results show that even small deterioration in the resource pool status requires an immediate adaptation of the operating system parameters to maintain the required performance. The sixth paper ``HeteroPBLAS: A Set of Parallel Basic Linear Algebra Subprograms Optimized for Heterogeneous Computational Clusters'' is by Ravi Reddy, Alexey Lastovetsky and Pedro Alonso from University College Dublin in Ireland and Polytechnic University of Valencia in Spain. The paper concerns the methodology for parallelization of linear algebra computations for execution in heterogeneous cluster environments. The design of the HeteroPBLAS library (Parallel Basic Linear Algebra Subprograms) for heterogeneous computational clusters is presented. The main contribution of the paper is the automation of the parallelization and optimization of the PBLAS, which is done by means of a special user interface and the underlying set of functions. An important element is here a performance model that is based on program code instrumentation, which determines parameters of the application and the executive heterogeneous platform relevant for execution performance of parallel code. The parameter values specified for or returned by execution of the performance model functions are next used for generation and optimal mapping of the parallel code of the library subroutines. The proposed approach is illustrated by experimental results of execution of optimized HeteroPBLAS programs on homogeneous and heterogeneous computing clusters. Marek Tudruj

In this paper, we classify the dynamic, decentralized load balancing algorithms for heterogenous distributed computer systems into three policies: queue adjustment policy (QAP), rate adjustment policy (RAP) and queue and rate adjustment policy (QRAP). We propose two efficient algorithms, referred to as rate based load balancing via virtual routing (RLBVR) and queue based load balancing via virtual routing (QLBVR), which belong to the above RAP and QRAP policies, respectively. We also consider algorithms estimated load information scheduling algorithm (ELISA) that was introduced in the literature, to implement QAP policy. Our focus is to analyze and understand the behaviors of these algorithms in terms of their load balancing ability under varying load conditions (light, moderate, or high) and the minimization of mean response time of jobs. We compare the above classes of algorithms by a number of rigorous simulation experiments to elicit their behavior under some influencing parameters. From these experiments, recommendations are drawn to prescribe the suitability of the algorithms under various situations.

A new Some-Read-Any-Write (SRAW) fault tolerant algorithm for redundant services is presented that allows a system to adjust failures dynamically in order to keep the availability and improve the performance. SRAW is based upon dynamic and active load balancing. By introducing dynamic and active load balancing scheme into redundant services, not only the processing speed of requests can be greatly improved, but also the load balancing can be simply and efficiently achieved. Integrated with consistency protocol in this paper, SRAW can also be applied to state services. The performance of SRAW algorithm is also analyzed, and comparisons with other fault tolerant algorithms, especially with RAWA, indicate that SRAW efficiently improves the performance of redundant services with guaranteeing system availability.

Any micro service-based programme, including alumni management software, must include load balancing. The effective use of resources is made possible by load balancing, which also ensures that application performance is unaffected by unexpected increases in requests or user usage. In an alumni management application, load balancing can be achieved with a combination of techniques such as round-robin scheduling, weighted load balancing, and dynamic scheduling. These techniques help to ensure that the application remains up and running, even during peak usage times. Additionally, load balancing can be used to ensure users get the best possible experience when using the application. By distributing traffic among multiple back-end services, users can expect faster response times and more reliable performance.Load balancing in a micro services-based alumni management application is a way of distributing incoming web traffic across multiple back end servers in order to efficiently manage traffic, reduce latency, and improve overall performance. Load balancing works by distributing incoming requests across a cluster of servers, and can be done either statically or dynamically. Static load balancing assigns each request to a specific server, while dynamic load balancing uses algorithms to decide which server should handle each request. In the alumni management application, load balancing is important for ensuring that requests are handled efficiently and that the system is not overwhelmed by too many requests. This is especially important when multiple users are accessing the application simultaneously. By distributing requests across multiple servers, the application can remain responsive and reliable even under heavy load.

In a cluster environment, the identification and the classification of the requested task is a prerequisite for choosing the appropriate load balancing algorithm and improving the processing capacity of the network. As the existing load balancing strategies cannot relate tasks and load balancing algorithm intelligently, this paper present an Agent-based dynamic adaptive load balancing model. Through the identification and classification of the requested task, the model selects corresponding load balancing algorithm to reach the load balancing in cluster. This model has a distinct advantage over other load balancing schedule strategies on response time. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4568 Full Text: PDF

We study the problem of balancing the load on processors in an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on a number of different networks has been well-studied, analytical work on dynamic load balancing is limited. The difficulty lies in modeling the arrivals and departures of jobs in a clean manner. In this paper, we introduce a new framework for dynamic load balancing in which the job traffic is modeled by an adversary. Our main result is that a simple local load balancing algorithm maintains the load of the network within a stable level against a powerful adversary. Our result holds for different models of traffic patterns and processor communication.

The capacity of wireless networks can be increased via dynamic load balancing/sharing by employing overlay networks on top of the existing cellular networks. One such recently proposed system is the integrated cellular and ad hoc relay (iCAR) system, where an overlay ad hoc network is employed to use the resources efficiently by dynamically balancing the load of the hot spots in the cellular network, and to provide quality-of-service to subscribers, no matter where they are located and when the request is made. It is assumed that this overlay network operates in the 2.4-GHz Industrial, Scientific, and Medical (ISM) band and, hence, the number of available ISM-band relay channels used for load balancing will be limited due to other users' interference at a given point in time. In this paper, the impact of ISM-band interference on the performance of iCAR systems, which is a representative hybrid wireless network, is studied, and it is shown that dynamic load balancing and sharing capabilities of iCAR systems are strictly dependent on the availability of the ISM-band relay channels. In addition to quantifying the impact of the number of available relay channels on the performance of iCAR systems, a simple channel assignment scheme to reduce the performance degradation due to other users' interference is also provided. Results show that this interference avoidance technique can improve the realistic performance of iCAR-like hybrid wireless networks by 12%-23% when the interferers are uniformly distributed in the ISM-band.

Load balancing is performed to achieve the optimal use of the existing computational resources as much as possible whereby none of the resources remains idle while some other resources are being utilized. Balanced load distribution can be achieved by the immigration of the load from the source nodes which have surplus workload to the comparatively lightly loaded destination nodes. Applying load balancing during run time is called dynamic load balancing (DLB). DLB can be realized both in a direct or iterative manner according to the execution node selection. In iterative methods, the final destination node is determined through several iteration steps, while in direct methods it is selected in one step. This paper presents the randomized hydrodynamic load balancing (RHLB) method which is a hybrid method that takes advantage of both direct and iterative methods. Using random load migration as a direct method, RHLB approach intends to solve the problems derived from the exceptional instantaneous load rises, and diffuse the surplus workload to relatively free resources. Besides, using hydrodynamic approach as an iterative method, RHLB aims to consume minimum possible system resources to balance the common workload distributions. The results of the experiments designate that, RHLB outruns other iterative based methods in terms of both balance quality and the total time of the load balancing process.

Routing is a very complex process in the field programmable gate array (FPGA) CAD flow. The increase of both FPGA size and design complexity leads to a long routing time hindering the productivity. In this article, we propose a more effective parallel router that combines static and dynamic load balance in parallel routing for FPGAs. First, we explore hierarchical region partitioning to assign routing tasks to different cores for static load balance. Then, we coordinate message propagation and task migration at runtime so that load balance between cores can be dynamically maintained in parallel routing. Finally, we combine static and dynamic load balance in the parallel routing for a higher degree of parallelism. Our parallel router performs on the multicore distributed-memory systems and the communication between cores is through message passing interface messages. We demonstrate the effectiveness of our parallel router using large-scale Titan designs. On average, our parallel router can scale up to 32 cores to achieve about 17× speedup with slight loss of quality, compared with the latest VTR 8 router.

In the era of petascale supercomputing, the importance of load balancing is crucial. Although dynamic load balancing is widespread, it is increasingly difficult to implement effectively with thousands of processors or more, prompting a second look at static load-balancing techniques even though the optimal allocation of tasks to processors is an NP-hard problem. We propose a heuristic static load-balancing algorithm, employing fitted benchmarking data, as an alternative to dynamic load balancing. The problem of allocating CPU cores to tasks is formulated as a mixed-integer nonlinear optimization problem, which is solved by using an optimization solver. On 163,840 cores of Blue Gene/P, we achieved a parallel efficiency of 80% for an execution of the fragment molecular orbital method applied to model protein-ligand complexes quantum-mechanically. The obtained allocation is shown to outperform dynamic load balancing by at least a factor of 2, thus motivating the use of this approach on other coarse-grained applications.

Nginx is a commonly used and free open-source web server that is used as a reverse proxy server, load balancer and HTTP cache. It consumes less memory and can handle more clients with less number of processes. Nginx provides users with five predefined load balancing algorithms. However, most of these algorithms are static and some of the load balancing rules are inefficient. In order to make the load of a cluster more stable under high concurrent requests, we developed a Dynamic Load Balancing (DLB) algorithm that uses Nginx as a network security control panel to provide load balancing for a cluster of backend servers. The DLB algorithm is based on the weighted round robin module of Nginx, Logistic Regression and Maximum Likelihood Estimation (MLE) algorithm. It handles the situation of high concurrent requests and reduces the probability of omitted or under-reported incident and status. We also propose a Hybrid Load Balance Method (HLBM) that incorporates the DLB algorithm and evolutionary computing to further improve the performance. We have conducted limited experiment by using dynamic load balancing algorithm. We will complete to develop the Hybrid Load Balance method and conduct experiments for HLBM.

DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network

This paper reports on the results of a preliminary study in dynamic load balancing on an Intel Hypercube. The purpose of this research is to provide experimental data in how parallel algorithms should be constructed to obtain maximal utilization of a parallel architecture. This study is one aspect of an ongoing research project into the construction of an automated parallelization tool. This tool will take FORTRAN source as input, and construct a parallel algorithm that will produce the same results as the original serial input. The focus of this paper is on the load balancing aspect of that project. The basic idea is to reserve a certain percentage of the computation task, subdivide that percentage into arbitrarily fine tasks, and dole those small tasks out to nodes on request. Ij” the percentage is chosen correctly, then a minority of nodes should be involved in consuming the filler tasks, and the overall throughput of the job should increase as a result of the individual node efJciencies having increased. This paper will outline our approach to performing dynamic load balancing on an Intel iPSC/2. We take the view that the problem of load balancing is really a problem of dividing a “computational task” into smaller components, each of roughly equal complexity, and each an independent event. After this is done, the components of the task can be sent to a node for execution. The key to an optimally balanced load across all computational nodes is the ability to form a statistical profile of the individual components of each computational task. This statistical profile will determine an initial sequence of execution. Our experience indicates that a speedup on the order of 80% is achievable with the judicious use of profiled load balancing. During the process of execution, the initial profile will be altered according to the actual behavior exhibited by the nodes. The difference between the actual and expected performance will be used to determine how much additional time should be devoted to altering the current execution schedule. Currently, our work involves statically setting the load balancing parameters. Our load balancing system determines the execution schedule