ADAPTIVE AND DYNAMIC LOAD BALANCING METHODOLOGIES FOR DISTRIBUTED ENVIRONMENT

Transcription

1 ADAPTIVE AND DYNAMIC LOAD BALANCING METHODOLOGIES FOR DISTRIBUTED ENVIRONMENT PhD Summary DOCTORATE OF PHILOSOPHY IN COMPUTER SCIENCE & ENGINEERING By Sandip Kumar Goyal (09-PhD-052) Under the Supervision of Dr. Manpreet Singh Professor Department of Computer Science & Engineering M. M. University, Ambala Department of Computer Science & Engineering M. M. Engineering College M. M. University, Mullana, Ambala, Haryana, India October 2013

2 ADAPTIVE AND DYNAMIC LOAD BALANCING METHODOLOGIES FOR DISTRIBUTED ENVIRONMENT (PhD Summary) 1. DISTRIBUTED COMPUTING ENVIRONMENT Distributed computing environments have become a cost-effective and popular choice to achieve high performance and to solve large scale computational problems. A Distributed System is of a collection of autonomous computers connected through a network and distribution middleware which enables computers to coordinate their activities and to share the resources of the system so that the users perceive the system as a single, integrated computing facility. A major advantage of using distributed system is resource sharing, where processor cycle is one of the major shareable resources. A distributed scheduler is a resource management component of a distributed operating system that focuses on redistributing the load of the system among the individual devices, so that the overall performance of the system is optimized. 2. DISTRIBUTED SYSTEM CHARECTERSCTICS Today distributed system is everywhere since it provides high performance computation power to serve the increasing need of large applications. The following are the characteristics of distributed system: Large Scale Ability of distributed system to accommodate more users without affecting the performance of system is called scalability. It can be achieved by adding more nodes with fast processors. As the number of nodes increases, components of the system should not require change i.e. DS components should be designed to support scalability without major modifications. Geographical Distribution Distributed system resources may be located at distant places. Heterogeneity A distributed system hosts both software and hardware resources that can be ranging from data, files, software components or programs to 1

3 sensors, scientific instruments, display devices, personal digital organizers, computers, super-computers and networks. Resource Sharing In distributed system, computers are having the ability to use any hardware, software or data anywhere in the system. Resource Manager is used to control access of resources, concurrency among resources and also provides naming scheme. Resource sharing model can be client/server or object-based. These models are used to describe how resources are provided, how they are used and how provider and user interact with each other. Transparent Access A distributed system should be seen as a single virtual computer and any user can access resources of distributed system in a transparent way. Quality of Service (QoS) Requirements The need for dependable service is fundamental since users require assurances that they will receive predictable, sustained and often high levels of performance. Consistent Access A distributed system must be built with standard services, protocols and interfaces, thus hiding the heterogeneity of the resources while allowing its scalability. Without such standards, application development and pervasive use would not be possible. Pervasive Access The distributed system must grant access to available resources by adapting to a dynamic environment in which resource failure is a commonplace. Openness Openness is concerned with extensions and improvements of distributed system. Various components of distributed system are connected to each other through interfaces, so all these interfaces detailed information should be published. Any new component added to the system must be integrated with existing components. Due to heterogeneous components, the issue of different data representation schemes arises which have to be resolved. 2

4 3. TYPES OF DISTRIBUTED SYSTEM Ideally, a distributed system should provide full-scale integration of heterogeneous computing resources of any type: processing units, storage units, communication units and so on. However, as the technology has not yet reached its maturity, real-world distributed system implementations are more specialized and generally focus on the integration of certain types of resources. As a result, nowadays we have different distributed system of following types: Peer To Peer System Peer to Peer is a paradigm for sharing of computing resources/services such as data files, cache storage, and disk space or processing cycles. In contrast to the conventional client/server model, Peer to Peer systems are characterized by symmetric roles among the peers, where every node in the network acts alike, and the processing and communication are widely distributed among the peers. An important goal in Peer to Peer networks is that all clients provide resources, including bandwidth, storage space and computing power. Cluster Computing Cluster computing environment consist of personal computers or workstations that are interconnected using high speed networks and are located at same location. Cluster computing can be used for load balancing as well as for high availability. A common use of cluster computing is to balance the traffic on popular web sites. Grid Computing Grid computing involves coupled and coordinated use of geographically distributed resources for purposes such as large-scale computation and distributed data analysis. Grid computing provides an environment in which network resources are virtualized to enable a utility model of computing. Cloud Computing Cloud computing is the use of computing resources (hardware and software) that are delivered as a service over a network (typically the Internet). Proponents claim that cloud computing allows enterprises to get their applications up and running faster with improved manageability 3

5 and less maintenance and enables IT to more rapidly adjust resources to meet fluctuating and unpredictable business demands. 4. CHALLENGES IN DISTRIBUTED SYSTEM The goal of distributed computing is to combine resources spanning many organizations into virtual organization that can more effectively solve important scientific, engineering, business and government problems. To achieve this goal, the following challenges must be taken into account: Transparency The main goal of a distributed system is to make the existence of multiple computers invisible (transparent) and provide a single image to its users. The eight forms of transparency identified by the International Standards Organizations References Model for Open Distributed Processing are Access transparency, Location transparency, Replication transparency, Failure transparency, Migration transparency, Concurrency transparency, Performance transparency and Scaling transparency. Reliability In general, distributed systems are expected to be more reliable than centralized system due to the existence of multiple instances of resources. For higher reliability, the fault-handling mechanisms of a distributed system must be designed properly to avoid faults, to tolerate faults and to detect and recover from the faults. Commonly used methods for dealing with these issues are fault avoidances and fault tolerance. Scalability Scalability refers to the capability of a system to adapt to increase service load. It is inevitable that a distributed system will grow with time since it is very common to add new machines or an entire sub network to the system, to take care of increased workload or organizational changes in a company. Therefore, a distributed system should be designed to easily cope with the growth of nodes and users in the significant loss of performance to users. Heterogeneity A heterogeneous distributed system consists of interconnected sets of dissimilar hardware or software systems. Because of the diversity, 4

6 designing heterogeneous distributed system is far more difficult than designing homogenous distributed system in which each system is based on the same or closely related, hardware and software. Security In order that users can trust the system and rely on it, the various resources of a computer system must be protected against destructed and unauthorized access. Enforcing security in a distributed system is more difficult than in a centralized system because of the lack of a single point of control and the use of insecure networks for data communication. 5. PROBLEM AREAS IN DISTRIBUTED COMPUTING Some of the challenging fields of distributed computing are: Job Scheduling In order to efficiently utilize available distributed resources and promptly complete tasks assigned to the distributed system, providing a suitable job scheduling strategy for the distributed computing is necessary. A distributed system has many independent resource providers with different policies. In addition to the size of such a distributed system, the diversity of those policies leads to a very complex allocation task that can not be manually handled by the user. This task does not only include the search for suitable resources but also the coordination of the actual job executions on the selected set of resources. Therefore, an efficient and flexible scheduling system is required to manage the job requests of the user. Since, distributed systems consist of heterogeneous, non-dedicated hardware with dynamic availability, and are connected via variable speed, congested links; the scheduling problem becomes very complex. The independent resource providers typically want to maintain control of their distributed resources, by use of local management systems. This increase the complexity of allocation task as those local management systems usually do not provide all system information due to their architecture or due to policy restrictions. Fault Tolerance Because of distributed system heterogeneity, scale and complexity, faults become likely. Therefore, distributed system infrastructure must have 5

7 mechanisms to deal with faults while providing efficient and reliable services to its end users. As distributed application grows to use more resources for longer periods of time, they will inevitably encounter increasing number of node failures. Furthermore, differences in node failure characteristics will widen as more different kind of resources join the distributed system. Most of the fault tolerance solutions will fail to support high performance applications in this environment as the schedulers do not consider failure characteristics in making placement decisions. In case of resource failures, the resource allocation policies can have significant impact on the performance of distributed applications. Load Balancing Many real life problems have a variable or dynamic workload depending on the data. Load balancing strategies try to distribute the workload uniformly across all computers in a distributed system. To harness the computational power of a distributed system, a load balancing policy is used. Such policies attempt to balance the load with the end result of maximizing resource utilization and hence optimizing performance. Load balancing for distributed system is complicated due to several factors. Distributed system can consist of a very large number of machines. The state of the system dynamically changes and load balancing policy should adapt its decisions to the state of the system. Another factor contributing to the complexity of load balancing is heterogeneous nature of such systems. Performance may be significantly impacted if information on task and machine heterogeneity is not taken into account by the load balancing policy. Generally, a load balancing scheme consist of three phases: information collection, decision making and data migration. During the information collection phase, load balancer gathers the information of workload distribution and the state of computing environment and detects whether there is load imbalance. The decision making phase focuses on calculating an optimal data distribution, while the data migration phase transfers the excess amount of workload from overloaded processor to under loaded ones. In general, load balancing algorithms can be classified as centralized or decentralized. In centralized policies, a central machine is dedicated as a load balancer and tasks are 6

8 submitted to the central machine. Thus, the load balancer becomes a bottleneck and a single point of failure. To avoid this, decentralized load balancing policies involve all machines in load balancing and avoid the use of a central server. Even though decentralized load balancing has advantages in terms of scalability and fault tolerance, the communication overhead incurred by frequent state information exchange between machines represent a challenge. 6. RESEARCH OBJECTIVES The objectives of this research is to improve the understanding of distributed computing environment using simulation and contributes to the advancement in the areas of job scheduling, load balancing, and resource allocation. The main objective of the present work can be stated as Adaptive And Dynamic Load Balancing Methodologies For Distributed Environment and in order to handle the above problems, the following contributions are made: An adaptive load balancing scheme for distributed system is presented. It implements load balancing using processor queue length as a performance metric. An Ant based algorithm for providing dynamic load balancing in distributed system is developed and evaluated using GridSim simulation toolkit. A decentralized dynamic load balancing algorithm using Simulated Annealing heuristic is proposed. The effectiveness of developed algorithm is tested against load conditions and in terms of scalability. An adaptive genetic based algorithm for load balancing in grid environment is proposed. The performance of proposed algorithm is compared with five other scheduling heuristics in terms of load and scalability. A framework for enhancement of fault tolerance capability of distributed systems is proposed. The proposed fault tolerant framework integrates fault tolerance and job scheduling in order to improve the percentage of successful job completion. 7

9 7. THESIS ORGANIZATION The thesis is organized as follows: Chapter 1 Introduction Chapter 2 Literature Survey Chapter 3 Adaptive Load Balancing Methodology For Grid Environment Chapter 4 Ant Based Approach For Dynamic Load Balancing In Grid Environment Chapter 5 Simulated Annealing With Load Balancing In Grid Environment Chapter 6 Genetic Based Adaptive Load Balancing Methodology For Computational Grids Chapter 7 A Framework For Enhancement of Fault Tolerance Capability of Distributed Systems Chapter 1 Introduction begins with a detailed description of distributed system, covering their specific characteristics and types, application of distributed system in engineering science and life science. In the later part, various challenges and problem areas of distributed system are investigated. Chapter 2, Literature Survey provides a detailed study of motivation for load distribution and various types of load balancing algorithms. The existing approaches to balance the load in distributed environment are also explored. In Chapter 3, An adaptive load balancing methodology for grid environment is proposed. The developed algorithm is simulated in GridSim. Simulation result shows that execution time of proposed adaptive load balancing algorithm is less as compared to the execution time with random algorithm. The performance of proposed algorithm is even better in terms of increasing the resources while keeping the number of users as fixed i.e. the system is lightly loaded. In Chapter 4, Ant based Load Balancing Algorithm is developed to allocate tasks to proper resources. In order to verify the performance of proposed algorithm, the simulation is performed. The results of the experiments are also presented and the strength of the algorithm is investigated. The simulation result concludes that the ALBA algorithm 8

10 enhances performance in terms of resource utilization. In Chapter 5, A model for Simulated Annealing based Load Balancing across resources for computational intensive tasks in grid environment is proposed. Simulated annealing is an optimization algorithm that exploits an analogy between the annealing process and the search for the optimum in a more general system. The annealing process is a two step wherein the first one corresponds to raising the temperature to a very high level (melting temperature) and second step correspond to cooling down slowly. The input of the algorithm is an initial solution constructed by assigning resource to each task randomly. There are several steps that the simulated annealing algorithm needs to go through and during each step temperature decreases at a constant rate. The cooling schedule used is among the most popular ones where the temperature decreases exponentially. Simulation results confirmed that SALB performs better than existing schemes in terms of load and scalability. In Chapter 6, A genetic based algorithm for load balancing among resources of a computational grid is presented. From the simulation results, it is concluded that the proposed algorithm is effective in terms of various load conditions and scalability. Finally, a comparative study of five scheduling strategies based on FCFS, Max_min, Min_min, GALB, SALB is presented. In Chapter 7, A framework for enhancement of fault tolerance capability of distributed systems is proposed. The proposed fault tolerant framework integrates fault tolerance and job scheduling in distributed system and makes the use of checkpointing approach for failure recovery. The proposed framework is simulated and result shows that the proposed approach provides a superior performance in terms of number of successful job completion and reduced failure probability to the one without considering fault tolerance. Finally, Conclusion and Future Scope of Research summarizes the research. It also gives an outline of the broader impact of the thesis and provides the scope of future work. The thesis is concentrated on the Adaptive and Dynamic Load Balancing Methodologies for Distributed Environment. Adaptive and fault tolerant load balancing schemes presented in this thesis are promising; still there are significant scope for 9

11 future research. This research work improves the understanding of distributed computing environments and advances the state-of the art through its contributions. Its investigation revealed areas in distributed system where much work remains to be done. The decentralized distributed model presented in this research work raises number of challenges for further research such as validate the feasibility of proposed models in real environments: Using a realistic distributed application. Consideration of tasks with different priorities. Extend the mechanism by providing means for a domain to redirect tasks across several domains. Incorporating more sophisticated load forecasting techniques. 10

12 LIST OF PUBLICATIONS 1. Sandip Kumar Goyal, and Manpreet Singh, A Framework for the Enhancement of Fault Tolerance Capability of Distributed Systems, WILKES 100-International Conference on Computing Sciences (ICCS) [Communicated]. 2. Sandip Kumar Goyal, and Manpreet Singh, Adaptive and Dynamic Load Balancing in Grid Using Ant Colony Optimization, International Journal of Engineering and Technology (IJET), Vol. 4, No. 4, pp , Aug-Sep Sandip Kumar Goyal, and Manpreet Singh, Enhanced Genetic Algorithm Based Load Balancing in Grid, International Journal of Computer Science Issues (IJCSI), Vol. 9, Issue 3, No. 2, pp , May Sandip Kumar Goyal, Manpreet Singh, and Vishal Gupta, An Adaptive Load Balancing Algorithm for Computational Grid, Journal of Engineering & Technology (JET), Vol. 1, Issue 2, pp , Jun- Dec Sandip Kumar Goyal, R. B. Patel, and Manpreet Singh, Adaptive and Dynamic Load Balancing Methodologies For Distributed Environment: A Review, International Journal of Engineering Science and Technology (IJEST), Vol. 3, No. 3, pp , March