Dual, mobile and reactive objects-operating system support for load balancing parallel VR applications

A distributed computing system uses the computational powers of the nodes on a network. Using a distributed approach a complex task can be divided into simpler tasks having less computational times. A serious difficulty in concurrent programming of a distributed environment is of node prioritization and load balancing. In this paper we present a novel technique for node prioritization & also propose a Load balancing technique based on heuristics. Experimental results show that the proposed model not only does the load balancing efficiently but it also increases the optimization of the network by assigning the best available node to a task.

Cloud computing is a recent computer technology that has virtualized infrastructure to offer secure and reliable services to users in a complex environment. The main advantage of the cloud is its scalability. Vast scalability is possible because of load balancing. Due to the advancement of the cloud, huge numbers of service provisioning requests are generated from users. Cloud needs a better and more efficient load-balancing mechanism to handle all the user’s requests. This paper proposes the principal component gradient round robin load balancing (PCGRLB) technique to balance the workload in a cloud server for handling a huge workload within a minimum time. The PCGRLB technique performs two processes: Virtual machine analysis and load balancing. The PCGRLB technique is evaluated on factors such as throughput, makespan, and response time concerning several tasks. The results show that the PCGRLB technique offers an efficient solution in achieving higher load balance, efficiency, throughput, and minimized makespan and response time than the conventional techniques.

Packet switching remains one of the bottlenecks in building fast Internet routers. Load balancing and switch scheduling are two important algorithms in the effort to maximize the throughput and minimize the latency of these packet switches. A load balancing algorithm regulates the traffic to conform to the service rates while a switch scheduling algorithm allocates the service rates adaptive to the arrival patterns. Many existing load balancing and switch scheduling algorithms are very similar. We show that load balancing and switch scheduling systems are dual systems based on the linear queue dynamics approximation. This allows us to cast a load balancing problem as a scheduling problem, and vice versa. We further show an example of designing a new algorithm for load balancing using an existing scheduling algorithm based on the duality. The duality perspective also allows us to solve unknown problems. We find the entropy rate of the randomized bandwidth allocation system with linear queue dynamics based on the knowledge of the entropy rate of the randomized load balancing system. For the general case, we find both an upper and a lower bound on the entropy rate. The joint use of dual load balancing and switch scheduling algorithms leads to performance gains as we show using mean field analysis.

With the proliferation of workstation clusters connected by high-speed networks, providing efficient system support for concurrent applications engaging in nontrivial interaction has become an important problem. Two principal barriers to harnessing parallelism are: efficient mechanisms that achieve transparent dependency maintenance while preserving semantic correctness; and scheduling algorithms that match coupled processes to distributed resources while explicitly incorporating their communication costs. This paper describes a set of performance features, their properties, and implementation in a system support environment called DUNES that achieves transparent dependency maintenance-IPC, file access, memory access, process creation/termination, process relationships-under dynamic load balancing. The two principal performance features are push/pull-based active and passive end-point caching and communication-sensitive load balancing. Collectively, they mitigate the overhead introduced by the transparent dependency maintenance mechanisms. Communication-sensitive load balancing, in addition, affects the scheduling of distributed resources to application processes where both communication and computation costs are explicitly taken into account. The DUNES architecture endows commodity operating systems with distributed operating system functionality while achieving transparency with respect to their existing application base. DUNES also preserves semantic correctness with respect to single processor semantics.

Multi-Protocol Label Switching (MPLS) can efficiently support the explicit routes setup by the use of Label Switched Paths (LSPs) between an ingress Label Switched Router (LSR) and an egress LSR. Hence it is possible to distribute the network traffic among several paths to achieve load balancing, thus improving the network utilization, and minimizing the congestion. The packet-level traffic characteristics in the Internet is so complex that it is natural to do traffic engineering (TE) and control at the flow level. The emerging Multi-Protocol Label Switching (MPLS) has introduced an attractive solution to TE in IP networks. The main objective of this paper is to balance traffic at the flow level among the parallel Label Switched Paths (LSPs) in MPLS networks. We introduce a multipath load-balancing model at the flow level. In this model, each LSP is modeled as an M/G/1 processor-sharing queue. The load-balancing problem is then considered as an optimization problem. Based on the analysis of the model, we propose a heuristic but efficient mechanism that can make good use of the traffic characteristics at the flow level. Packet disorder is avoided effectively by dispatching packets belonging to one flow to the same path. This mechanism only need to be implemented in the ingress LSRs and the egress LSRs, while the intermediate LSRs only forward the packets. Apart from discussing the traffic allocation granularity, and the implementation issues in details, we have also performed extensive simulations using NS-2 with MPLS modules. The simulation results show that the load through the network is well balanced so that the network throughput is improved and the delay is decreased efficiently.

Load balancing is one of the main parts of scheduling Grid resources. One of the load balancing models on Grid resources is the hierarchical model. This model has the advantage that it requires minimal communication costs between one resource and another. The PLBA load balancing algorithm uses a hierarchical model with dynamically obtained threshold values, so that it can adjust conditions at a time, both the state of the resource, the state of the computer network, and the state of the recipient or client. PVM3 is a software system capable of optimizing heterogeneous resources, so that resources can work in parallel. Resources can also complete tasks well, even though they are very large and complex tasks. This research has implemented the PLBA load balancing algorithm, with the aim of optimizing Grid resources. This research has also developed the PLBA load balancing algorithm by changing the arguments for NPEList, so that resources can be grouped more optimally. The PLBA load balancing algorithm has been successfully developed by modifying the arguments for NPEList, so that the running time required to complete the given tasks is shorter, because resources can be grouped more optimally. This has been shown by the shorter average running time when using the modified NPEList argument (0.75 * threshold1 <= ALCi <= 1.25 * threshold1) is shorter, than using the NPEList argument in previous research (ALCi = threshold1). Comparison of the average running time has been obtained as follows : (82513.63740 : 67837.71720); (63869.92450 : 50722.17210); (858,96710 : 207,33680); (321.88000 : 126.89100); (768.54560 : 468.27190); (780.22770 : 279.43730).

Load balancing is one of the central problems that have to be solved in parallel computation. Here, the problem of distributed, dynamic load balancing for massive parallelism is addressed. A new local method, which realizes a physical analogy to equilibrating liquids in multi-dimensional tori or hypercubes, is presented. It is especially suited for communication mechanisms with low set-up to transfer ratio occurring in tightly-coupled or SIMD systems. By successive shifting single load elements to the direct neighbors, the load is automatically transferred to lightly loaded processors. Compared to former methods, the proposed Liquid model has two main advantages. First, the task of load sharing is combined with the task of load balancing, where the former has priority. This property is valuable in many applications and important for highly dynamic load distribution. Second, the Liquid model has high efficiency. Asymptotically, it needs O(D . K . Ldiff ) load transfers to reach the balanced state in a D-dimensional torus with K processors per dimension and a maximum initial load difference of Ldiff . The Liquid model clearly outperforms an earlier load balancing approach, the nearest-neighbor-averaging. Besides a survey of related research, analytical results within a formal framework are derived. These results are validated by worst-case simulations in one-and two-dimensional tori with up to two thousand processors.

Effective characterization of workload could be used to drive Capacity Planning and Performance Management in IaaS Cloud. There are different workload metrics (e.g. CPU, memory usage, throughput, response time) which could be modeled along with relationships between them. Similarly, we could model relationships across a set of workloads. Analyzing and characterizing this would enable decision making for various scenarios such as migration, re-provisioning, load balancing, resource management, initial placement. In this paper, we study workload running in IaaS cloud and categorize into patterns, based on their behavioral characteristics. We define different types of behavioral patterns and outline statistical techniques to be used in determining these patterns. We present initial results for development workload data collected in the lab.

There is a very important class of applications which is named Many-Task Computing (MTC). For a lot of MTC applications, a large number of independent tasks which differ significantly on task complexities will be generated. This brings a great challenge for grids to achieve a high performance for such MTC applications. In this paper, we describe the TCE algorithm, a scheduling algorithm based on Task Complexity Estimating which reduces the overhead by applying task bundling. We also present a task complexity model for task complexity estimating in order that after task bundling loads among computing nodes can be well balanced. The TCE algorithm greatly exceeded the other scheduling algorithms involved in performance evaluation on speedup and efficiency, and it achieved a performance close to that in the ideal condition. It is demonstrated that by applying the TCE algorithm the overhead cost can be reduced significantly and that load balance can be well guaranteed, so that grids can achieve a high performance for MTC applications.

We report a new multi-GPU capable ab initio Hartree-Fock/density functional theory implementation integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Details on the load balancing algorithms for electron repulsion integrals and exchange correlation quadrature across multiple GPUs are described. Benchmarking studies carried out on up to four GPU nodes, each containing four NVIDIA V100-SXM2 type GPUs demonstrate that our implementation is capable of achieving excellent load balancing and high parallel efficiency. For representative medium to large size protein/organic molecular systems, the observed parallel efficiencies remained above 82% for the Kohn-Sham matrix formation and above 90% for nuclear gradient calculations. The accelerations on NVIDIA A100, P100, and K80 platforms also have realized parallel efficiencies higher than 68% in all tested cases, paving the way for large-scale ab initio electronic structure calculations with QUICK.

Fourth generation networks lead to multiple distributed wireless networks, including cellular networks of different generations, frequencies and cell sizes; wireless LAN and PAN networks, and potentially satellite and other networks. Existing studies on hierarchical networks, integrated networks, and dual mode studies show how higher efficiency can be achieved in a distributed cellular super-network. Load sharing, in distributed operating systems, strives to assist overloaded resources by overflowing tasks between systems. We consider how 3G/4G wireless networks can load share traffic using vertical handovers. This study integrates and builds upon simulation studies of hierarchical and integrated networks, and in addition, considers the impact of overflowing sessions to potentially multiple components.

Cloud computing is emerging as a very promising technology for computing and storage services. However, the multi-resources load balancing over heterogeneous cluster or cloud is a NP-hard problem. To obtain an optimized solution, in this paper, we propose a heuristic algorithm named Minimum Longest Queue Finish Time (MLFT). In the proposed scheme, we first divide the high computation task into multiple sub-tasks, and re-organize all the tasks into multiple task queues to shorten the entire finish time of all the tasks submitted to the cluster and launched in parallel according to load balancing. In the task division process, an adaptive segmentation algorithm is proposed according to the complexity and maximum segmentation granularity of the input task. Based on the proposed algorithm, an efficient parallel video transcoding framework with cloud computing is presented. Experimental results show that the proposed algorithm outperforms the existing algorithms significantly on the entire finish time of the tasks and approaches to the optimal solution closely.

In mobile communication system, the traffic is time-varying and unpredictable, so there will be some hot-spot cells. It means that, the number of service request is much higher than an acceptable level in some cells, which may result in call-blocking with a large probability. But in other cells of system there are still resources available to serve more users. How to avoid the imbalance to effectively improve the system's resource utilization is what load balancing need to solve. In this paper, a new kind of event-trigger mechanism using cell load status information exchange between eNBs via X2 interface is presented. Additionally, based on it, a novel load balancing algorithm, named TSLB(two-staged load balancing), for LTE systems is put forward. Using this algorithm, eNBs can start corresponding load balancing procedure accurately according to the cell load status in real-time without any manual intervention. Simulation results show that the method is valid and it can increase the system throughput significantly as well as decrease the network delay.

Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. The need for enhanced radio resource allocation schemes, improved user mobility and increased throughput, driven by a rising demand for data, has necessitated the development of diverse algorithms that optimize output values based on varied input parameters. In this context, we identify the main topics related to cellular networks and machine learning algorithms in order to pinpoint areas where the optimization of parameters is crucial. Furthermore, the wide range of available algorithms often leads to confusion and disorder during classification processes. It is crucial to note that next-generation networks are expected to require reduced latency times, especially for sensitive applications such as Industry 4.0. Research Question: An analysis of the existing literature on mobile network load balancing methods was conducted to identify systems that operate using semi-automatic, automatic and hybrid algorithms. Our research question is as follows: What are the automatic, semi-automatic and hybrid load balancing algorithms that can be applied to next-generation mobile networks? Contribution: This paper aims to present a comprehensive analysis and classification of the algorithms used in this area of study; in order to identify the most suitable for load balancing optimization in next-generation mobile networks, we have organized the classification into three categories, automatic, semi-automatic and hybrid, which will allow for a clear and concise idea of both theoretical and field studies that relate these three types of algorithms with next-generation networks. Figures and tables illustrate the number of algorithms classified by type. In addition, the most important articles related to this topic from five different scientific databases are summarized. Methodology: For this research, we employed the PRISMA method to conduct a systematic literature review of the aforementioned study areas. Findings: The results show that, despite the scarce literature on the subject, the use of load balancing algorithms significantly influences the deployment and performance of next-generation mobile networks. This study highlights the critical role that algorithm selection should play in 5G network optimization, in particular to address latency reduction, dynamic resource allocation and scalability in dense user environments, key challenges for applications such as industrial automation and real-time communications. Our classification framework provides a basis for operators to evaluate algorithmic trade-offs in scenarios such as network fragmentation or edge computing. To fill existing gaps, we propose further research on AI-driven hybrid models that integrate real-time data analytics with predictive algorithms, enabling proactive load management in ultra-reliable 5G/6G architectures. Given this background, it is crucial to conduct further research on the effects of technologies used for load balancing optimization. This line of research is worthy of consideration.

The Space-Air-Ground integrated network (SAGIN) is the development trend of the future communication networks, and it can cope with various complex environments and tasks in the future communication environment. When sudden public events and natural disasters occurs, ground communication facilities may be damaged and cannot meet the communication needs in emergency scenarios. The Space-Air-Ground integrated three-dimensional and heterogeneous network enables flexible deployment and rapid networking. In this paper, we study the routing algorithm in a software defined SAGIN to optimize the load balancing in the communication network. First, considering the characteristics of dynamic topology changes in real time, we propose a new Software Defined Network (SDN) model for the SAGIN, which promotes a high network flexibility. Basing on such model, we present a new load balancing dynamic routing algorithm, namely Load Balancing Algorithm Based on Multi-dimensional Resources and Energy (LBMRE-OLSR). The multi-dimensional resources and energy consumption are considered in the new routing algorithm. The simulation experiments show that the proposed algorithm efficiently reduce end-to-end delays and packet loss rate.