Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

SOA中一种基于负载均衡的服务查找请求路由算法

服务查找是面向服务架构(SOA)中一个非常重要的环节,但目前的服务查找算法一般并未考虑到服务查找节点间的负载均衡,在请求频繁条件下不能满足查找效率的要求。提出一种分布式的、综合考虑节点处理能力和网络延时、适用于SOA中分布式服务注册中心的服务查找请求路由算法HaFA。该算法利用负载度实现对服务节点计算能力的度量,解决了负载度均衡后任务仍可能分配到弱计算能力节点上的问题,提高了服务注册中心计算资源的利用率;利用节点负载波动率估量下一个离散时间点的负载度,解决了网络延时期间负载波动对实现均衡造成影响的问题。实验结果表明,HaFA在分布式服务查找中能有效提高系统吞吐率,缩短结果响应的平均等待时间。     

Scalable network file systems with load balancing and fault tolerance for web services

Because of the rapid growth of the World Wide Web and the popularization of smart phones, tablets and personal computers, the number of web service users is increasing rapidly. As a result, large web services require additional disk space, and the required disk space increases with the number of web service users. Therefore, it is important to design and implement a powerful network file system for large web service providers. In this paper, we present three design issues for scalable network file systems. We use a variable number of objects within a bucket to decrease internal fragmentation in small files. We also propose a free space and access load-balancing mechanism to balance overall loading on the bucket servers. Finally, we propose a mechanism for caching frequently accessed data to lower the total disk I/O. These proposed mechanisms can effectively improve scalable network file system performance for large web services.     

Redundant movements in autonomous mobility: Experimental and theoretical analysis

Distributed load balancers exhibit thrashing where tasks are repeatedly moved between locations due to incomplete global load information. This paper shows that systems of autonomous mobile programs (AMPs) exhibit the same behaviour, and identifies two types of redundant movement (greedy effect). AMPs are unusual in that, in place of some external load management system, each AMP periodically recalculates network and program parameters and may independently move to a better execution environment. Load management emerges from the behaviour of collections of AMPs.The paper explores the extent of greedy effects by simulating collections of AMPs and proposes negotiating AMPs (NAMPs) to ameliorate the problem. We present the design of AMPs with a competitive negotiation scheme (cNAMPs), and compare their performance with AMPs by simulation. We establish new properties of balanced networks of AMPs, and use these to provide a theoretical analysis of greedy effects.     

Offline file assignments for online load balancing

We study a novel load balancing problem that arises in web search engines. The problem is a combination of an offline assignment problem, where files need to be (copied and) assigned to machines, and an online load balancing problem, where requests ask for specific files and need to be assigned to a corresponding machine, whose load is increased by this.We present simple deterministic algorithms for this problem and exhibit an interesting trade-off between the available space to make file copies and the obtainable makespan. We also give non-trivial lower bounds for a large class of deterministic algorithms and present a randomized algorithm that beats these bounds with high probability.     

Artificial life techniques for load balancing in computational grids

Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper studies several well-known artificial life techniques to gauge their suitability for solving grid load balancing problems. Due to their popularity and robustness, a genetic algorithm (GA) and tabu search (TS) are used to solve the grid load balancing problem. The effectiveness of each algorithm is shown for a number of test problems, especially when prediction information is not fully accurate. Performance comparisons with Min-min, Max-min, and Sufferage are also discussed.     

Practical load balancing for content requests in peer-to-peer networks

This paper studies the problem of balancing the demand for content in a peer-to-peer network across heterogeneous peer nodes that hold replicas of the content. Previous decentralized load balancing techniques in distributed systems base their decisions on periodic updates containing information about load or available capacity observed at the serving entities. We show that these techniques do not work well in the peer-to-peer context; either they do not address peer node heterogeneity, or they suffer from significant load oscillations which result in unutilized capacity. We propose a new decentralized algorithm, Max-Cap, based on the maximum inherent capacities of the replica nodes. We show that unlike previous algorithms, it is not tied to the timeliness or frequency of updates, and consequently requires significantly less update overhead. Yet, Max-Cap can handle the heterogeneity of a peer-to-peer environment without suffering from load oscillations. Mema Roussopoulos is an Assistant Professor of Computer Science on the Gordon McKay Endowment at Harvard University. Before joining Harvard, she was a Postdoctoral Fellow in the Computer Science Department at Stanford University. She received her PhD and Master’s degrees in Computer Science from Stanford, and her Bachelor’s degree in Computer Science from the University of Maryland at College Park. Her interests are in the areas of distributed systems, networking, and mobile and wireless computing. Mary Baker is a Senior Research Scientist at HP Labs. Her research interests include distributed systems, networks, mobile systems, security, and digital preservation. Before joining HP Labs she was on the faculty of the computer science department at Stanford University where she ran the MosquitoNet project. She received her PhD from the University of California at Berkeley.     

Parallel extremal optimization in processor load balancing for distributed applications

The paper concerns parallel methods for extremal optimization (EO) applied in processor load balancing in execution of distributed programs. In these methods EO algorithms detect an optimized strategy of tasks migration leading to reduction of program execution time. We use an improved EO algorithm with guided state changes (EO-GS) that provides parallel search for next solution state during solution improvement based on some knowledge of the problem. The search is based on two-step stochastic selection using two fitness functions which account for computation and communication assessment of migration targets. Based on the improved EO-GS approach we propose and evaluate several versions of the parallelization methods of EO algorithms in the context of processor load balancing. Some of them use the crossover operation known in genetic algorithms. The quality of the proposed algorithms is evaluated by experiments with simulated load balancing in execution of distributed programs represented as macro data flow graphs. Load balancing based on so parallelized improved EO provides better convergence of the algorithm, smaller number of task migrations to be done and reduced execution time of applications.     

A new analytical method for parallel,diffusion-type load balancing

We propose a new proof technique which can be used to analyse many parallel load balancing algorithms. The technique is designed to handle concurrent load balancing actions, which are often the main obstacle in the analysis. We demonstrate the usefulness of the approach by analysing various natural diffusion-type protocols. Our results are similar to, or better than, previously existing ones, while our proofs are much easier.     

Towards a hybrid load balancing policy in grid computing system

Grid computing has become conventional in distributed systems due to technological advancements and network popularity. Grid computing facilitates distributed applications by integrating available idle network computing resources into formidable computing power. As a result, by using efficient integration and sharing of resources, this enables abundant computing resources to solve complicated problems that a single machine cannot manage. However, grid computing mines resources from accessible idle nodes and node accessibility varies with time. A node that is currently idle, may become occupied within a second of time and then be unavailable to provide resources. Accordingly, node selection must provide effective and sufficient resources over a long period to allow load assignment. This study proposes a hybrid load balancing policy to integrate static and dynamic load balancing technologies. Essentially, a static load balancing policy is applied to select effective and suitable node sets. This will lower the unbalanced load probability caused by assigning tasks to ineffective nodes. When a node reveals the possible inability to continue providing resources, the dynamic load balancing policy will determine whether the node in question is ineffective to provide load assignment. The system will then obtain a new replacement node within a short time, to maintain system execution performance.     

A hybrid policy for fault tolerant load balancing in grid computing environments

Due to the emergence of grid computing over the Internet, there is a need for a hybrid load balancing algorithm which takes into account the various characteristics of the grid computing environment. Hence, this research proposes a fault tolerant hybrid load balancing strategy namely AlgHybrid_LB, which takes into account grid architecture, computer heterogeneity, communication delay, network bandwidth, resource availability, resource unpredictability and job characteristics. AlgHybrid_LB juxtaposes the strong points of neighbor-based and cluster based load balancing algorithms. Our main objective is to arrive at job assignments that could achieve minimum response time and optimal computing node utilization. Major achievements include low complexity of proposed approach and drastic reduction of number of additional communications induced due to load balancing. A simulation of the proposed approach using Grid Simulation Toolkit (GridSim) is conducted. Experimental results show that the proposed algorithm performs very well in a large grid environment.     

Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services

The prevalence of dynamic-content web services, exemplified by search and online social networking, has motivated an increasingly wide web-facing front end. Horizontal scaling in the Cloud is favored for its elasticity, and distributed design of load balancers is highly desirable. Existing algorithms with a centralized design, such as Join-the-Shortest-Queue (JSQ), incur high communication overhead for distributed dispatchers.We propose a novel class of algorithms called Join-Idle-Queue (JIQ) for distributed load balancing in large systems. Unlike algorithms such as Power-of-Two, the JIQ algorithm incurs no communication overhead between the dispatchers and processors at job arrivals. We analyze the JIQ algorithm in the large system limit and find that it effectively results in a reduced system load, which produces 30-fold reduction in queueing overhead compared to Power-of-Two at medium to high load. An extension of the basic JIQ algorithm deals with very high loads using only local information of server load.     

CUIRRE: An open-source library for load balancing and characterizing irregular applications on GPUs

While Graphics Processing Units (GPUs) show high performance for problems with regular structures, they do not perform well for irregular tasks due to the mismatches between irregular problem structures and SIMD-like GPU architectures. In this paper, we introduce a new library, CUIRRE, for improving performance of irregular applications on GPUs. CUIRRE reduces the load imbalance of GPU threads resulting from irregular loop structures. In addition, CUIRRE can characterize irregular applications for their irregularity, thread granularity and GPU utilization. We employ this library to characterize and optimize both synthetic and real-world applications. The experimental results show that a 1.63× on average and up to 2.76× performance improvement can be achieved with the centralized task pool approach in the library at a 4.57% average overhead with static loading ratios. To avoid the cost of exhaustive searches of loading ratios, an adaptive loading ratio method is proposed to derive appropriate loading ratios for different inputs automatically at runtime. Our task pool approach outperforms other load balancing schemes such as the task stealing method and the persistent threads method. The CUIRRE library can easily be applied on many other irregular problems.     

Hash-based proximity clustering for efficient load balancing in heterogeneous DHT networks

Distributed hash table (DHT) networks based on consistent hashing functions have an inherent load uneven distribution problem. The objective of DHT load balancing is to balance the workload of the network nodes in proportion to their capacity so as to eliminate traffic bottleneck. It is challenging because of the dynamism, proximity and heterogeneity natures of DHT networks and time-varying load characteristics.     

Hints to improve automatic load balancing with LeWI for hybrid applications

The DLB (Dynamic Load Balancing) library and LeWI (LEnd When Idle) algorithm provide a runtime solution to deal with the load imbalance of parallel applications independently of the source of imbalance. DLB relies on the usage of hybrid programming models and exploits the malleability of the second level of parallelism to redistribute computation power across processes.     

Game-theoretic static load balancing for distributed systems

In this paper, we present a game theoretic approach to solve the static load balancing problem for single-class and multi-class (multi-user) jobs in a distributed system where the computers are connected by a communication network. The objective of our approach is to provide fairness to all the jobs (in a single-class system) and the users of the jobs (in a multi-user system). To provide fairness to all the jobs in the system, we use a cooperative game to model the load balancing problem. Our solution is based on the Nash Bargaining Solution (NBS) which provides a Pareto optimal solution for the distributed system and is also a fair solution. An algorithm for computing the NBS is derived for the proposed cooperative load balancing game. To provide fairness to all the users in the system, the load balancing problem is formulated as a non-cooperative game among the users who try to minimize the expected response time of their own jobs. We use the concept of Nash equilibrium as the solution of our non-cooperative game and derive a distributed algorithm for computing it. Our schemes are compared with other existing schemes using simulations with various system loads and configurations. We show that our schemes perform near the system optimal schemes and are superior to the other schemes in terms of fairness.     

Asymmetry-aware load balancing for parallel applications in single-ISA multi-core systems

Contemporary operating systems for single-ISA (instruction set architecture) multi-core systems attempt to distribute tasks equally among all the CPUs. This approach works relatively well when there is no difference in CPU capability. However, there are cases in which CPU capability differs from one another. For instance, static capability asymmetry results from the advent of new asymmetric hardware, and dynamic capability asymmetry comes from the operating system (OS) outside noise caused from networking or I/O handling. These asymmetries can make it hard for the OS scheduler to evenly distribute the tasks, resulting in less efficient load balancing. In this paper, we propose a user-level load balancer for parallel applications, called the ’capability balancer’, which recognizes the difference of CPU capability and makes subtasks share the entire CPU capability fairly. The balancer can coexist with the existing kernel-level load balancer without detrimenting the behavior of the kernel balancer. The capability balancer can fairly distribute CPU capability to tasks with very little overhead. For real workloads like the NAS Parallel Benchmark (NPB), we have accomplished speedups of up to 9.8% and 8.5% in dynamic and static asymmetries, respectively. We have also experienced speedups of 13.3% for dynamic asymmetry and 24.1% for static asymmetry in a competitive environment. The impacts of our task selection policies, FIFO (first in, first out) and cache, were compared. The use of the cache policy led to a speedup of 5.3% in overall execution time and a decrease of 4.7% in the overall cache miss count, compared with the FIFO policy, which is used by default.     

Application of load balancing based on symmetric balanced incomplete block design to random networks

In a load balancing algorithm [O. Lee, M. Anshel, I. Chung, Design of an efficient load balancing algorithm on distributed networks by employing symmetric balanced incomplete block design, IEE Proceedings - Communications 151 (6) (2004) 535-538] based on the SBIBD (Symmetric Balanced Incomplete Block Design), each node receives global workload information by only two round message exchange with traffic overhead, where v is the number of nodes. It is very efficient and works well only when v=p²+p+1 is used for a prime number p. In this paper, we generated a special incidence structure using the SBIBD and then propose a new load balancing algorithm, which executes well for an arbitrary number of nodes. To accomplish this, we add a number of links to nodes in order for each node to receive more than 80% of the workload information by two round message exchange. For performance of our algorithm, we carried out an experiment for the number of nodes, w, which was up to 5000. Traffic overhead is less than in a round and standard deviation of traffic overhead shows that each node has a mostly well-balanced amount of traffic.     

LBBSRT: An efficient SDN load balancing scheme based on server response time

The response time is the most important factor determining user experiences in the service provision model involving server clusters. However, traditional server cluster load balancing scheme are limited by the hardware conditions, and cannot completely exploit the server response times for load balancing. In order to effectively resolve the traditional load balancing schemes, we propose a load balancing scheme based on server response times by using the advantage of SDN flexibility, named LBBSRT. Using the real-time response time of each server measured by the controller for load balancing, we process user requests by obtaining an evenly balanced server loads. Simulation experiments show that our scheme exhibits a better load balancing effect and process requests with a minimum average server response times. In addition, our scheme is easy to implement, and exhibits good scalability and low cost characteristics.