Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads

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1 Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads G. Suganthi (Member, IEEE), K. N. Vimal Shankar, Department of Computer Science and Engineering, V.S.B. Engineering College, Karur Abstract - A distributed system is a collection of independent computers that appears to its users as a single coherent system. The main goal of distributed system is to make it easy for users and applications to access remote resources and to share them in a control and efficient way. To reduce the cost of infrastructure and electrical energy, enterprise datacenters consolidate workloads on the same physical hardware. It allows integrated management of heterogeneous workloads composed of transactional applications and long-running jobs, dynamically placing the workloads in such a way as to equalize their satisfaction. It also leverages virtualization control mechanisms to perform online system reconfiguration. For enterprise datacenters and cloud computing infrastructures, the resource utilization is a critical goal even in presence of heterogeneous workloads. To provide the better heterogeneous services in the distributed environment, the service-level agreements through multi issue negotiation for transactional and batch workloads are established and the load balancing mechanism is implemented. The use of live VM migration has enabled more effective sharing of system resources in a physical server. This project helps us to maximize mixed workload performance and increases overall system resource utilization. Keywords - Virtual Machine (VM), Quality of Service (QoS), Performance management, Workload management, Resource management and cloud computing. 1. INTRODUCTION Transactional applications and batch jobs are widely used by many organizations to deliver services to their customers and partners. Due to intrinsic differences between these workloads, the physical server gets imbalanced, which contributes to resource underutilization and management complexity. Integrated performance management of mixed workloads is a challenging problem. First, the performance goals for different workloads tend to be of different types. For interactive workloads, goals are typically defined in terms of average or percentile response time or throughput over a short time interval, while goals for non interactive workloads concern the performance (e.g., completion time) of individual jobs. Second, due to the nature of their goals and the short duration of individual requests, interactive workloads lend themselves to management at short control cycles, whereas non interactive workloads typically require calculation of a schedule for an extended period of time. In addition, different types of workload require different control mechanisms for management. Transactional workloads are managed using flow control, load balancing, and application placement. Non interactive workloads need scheduling and resource control. Traditionally, these have been addressed separately. Compute clouds are commonly used by many different users that rely on the existing computing infrastructure to deploy their workloads. As a result, heterogeneous workloads run on the same physical nodes and pose extraordinary challenges on a cloud management middleware. Our technique relies on load balancing mechanisms to manage workloads. The use of live VM migration has enabled more effective sharing of system resources in a physical server. Different QoS parameters such as Maximum delay for a request, Minimum number of parallel request handled per second and Deadline for completion of each jobs request are used for finding the feasible host. Then predict queuing delay and execution time on feasible hosts. If utility ISSN: Page 1431

2 value of each path is less than threshold of utility value of each path then predict QoS and allocate it best host based on the resources needs. 2. RELATED WORK 2.1. Server Virtualization in Autonomic Management of Heterogeneous Workloads Server virtualization opens up a range of new possibilities for autonomic datacenter management, through the availability of new automation mechanisms that can be exploited to control and monitor tasks running within virtual machines. This offers not only new and more flexible control to the operator using a management console, but also more powerful and flexible autonomic control, through management software that maintains the system in a desired state in the face of changing workload and demand. This paper explores in particular the use of server virtualization technology in the autonomic management of data centers running a heterogeneous mix of workloads Managing SLAs of Heterogeneous Workloads using Dynamic Application Placement This paper deals with the problem of managing heterogeneous workloads in a virtualized data center. Consider two different workloads: transactional applications and long-running jobs. This technique permits collocation of these workload types on the same physical hardware. It dynamically modifies workload placement by leveraging control mechanisms such as suspension and migration, and strives to optimally trade off resource allocation among these workloads in spite of their differing characteristics and performance objectives. This paper extends our framework with the capability to manage long-running workloads. This is achieved by using utility functions, which permits to compare the performance of various workloads, and which are used to drive allocation decisions Efficient Resource Allocation and Power Saving in Multitierd Systems In this paper, a parameter-free algorithm for dynamic resource provisioning uses simple statistics to promptly distill information about changes in workload burstiness. This information, coupled with the application's end-to-end response times and system bottleneck characteristics, guide resource allocation that shows to be very effective under a broad variety of burstiness profiles and bottleneck scenarios Adaptive Data-aware Utility-based Scheduling in Resourceconstrained Systems This paper addresses the problem of dynamic scheduling of data-intensive multiprocessor jobs. Each job requires some number of CPUs and some amount of data that needs to be downloaded into a local storage space before starting the job. The completion of each job brings some benefit (utility) to the system, and the goal is to find the optimal scheduling policy that maximizes the average utility per unit of time obtained from all completed jobs. A co-evolutionary solution methodology is proposed, where the utility-based policies for managing local storage and for scheduling jobs onto the available CPUs mutually affect each other's environments, with both policies being adaptively tuned using the Reinforcement Learning methodology. Our simulation results demonstrate the feasibility of this approach and show that it performs better than the best heuristic scheduling policy we could find for this domain Quantifying Load Imbalance on Virtualized Enterprise Servers Virtualization has been shown to be an attractive path to increase overall system resource utilization. The use of live Virtual Machine (VM) migration has enabled more effective sharing of system resources across multiple physical servers, resulting in an increase in overall performance. The algorithm for balancing the load of virtualized enterprise servers follows a greedy approach, inductively predicting which VM migration will yield the greatest improvement of the imbalance metric in a particular step. Move VM 2 to Server 2 VM 1 VM 2 VM 3 VM 4 Physical Server 1 Physical Server 2 Virtualization Manager Fig.1. Live VM Migration Process ISSN: Page 1432

3 3. INTEGRATED MANAGEMENT OF HETEROGENEOUS WORKLOADS The goal of the technique introduced in this paper is to make placement decisions that involve applications of different nature, more specifically transactional applications and long running workloads. Given the different characteristics of each workload, that makes their performance hardly comparable, it leverages RPF to produce a normalized representation of their performance. RPFs are leveraged by the placement algorithm to make placement decisions, with the goal of maximizing the relative performance delivered by all the applications in the system Measuring the Job Properties Given a set of jobs. With each job m, the following information s are associated. Resource usage profile: A resource usage profile describes the resource requirements of a job and is given at job submission time in the real system, this profile comes from the job workload profiler. The profile is estimated based on historical data. Each job m consists of a sequence of N stages, S 1,.,SN m, where each stage S k is described by the following parameters: The amount of CPU cycles consumed in this stage, α k,m. The maximum speed with which the stage may run, w max k,m. The minimum speed with which the stage must run, whenever it runs, w min k,m. The memory requirement γ k,m Designing the Virtualization Control Mechanism The SLA objective for a job is expressed in terms of its desired completion time T m, which is the time by which the job must complete. Clearly, T m should be greater than the job s desired start time, T m start which itself is greater than or equal to the time when the job was submitted. The difference between the completion time goal and the desired start time, T m - T m start, is called the relative goal, and can be understood as the maximum acceptable job runtime. Notice that job runtime will depend on allocated resources to the Virtual Machine in which the job runs. RPF that maps actual job completion time tm to a measure of satisfaction from achieving it, u (t ). If job m completes at time t, then the relative distance of its completion time from the goal is the job s actual runtime normalized to its relative goal, which is expressed by the RPF of the following form: u m (t m ) = Runtime state. At runtime, we monitor and estimate the following properties for each job: current status, which may be either running, not started, suspended, or paused; and CPU time consumed thus far, α. Relative goal factor. For the purpose of easily controlling the tightness of SLA goals in our experiments, we introduce a relative goal factor which is defined as the ratio of the relative goal of the job to its execution time at the maximum speed, Load Balancing Mechanism through Improved Heuristics A system in which all jobs can be placed simultaneously, and in which the available CPU power may be arbitrarily finely allocated among the jobs. It requires a function that maps the system s CPU power to the relative performance achievable by jobs when placed on it. Let us consider job m. Based on its properties, estimate the completion time needed to achieve relative performance u. Then calculate the average speed with which the job must proceed over its remaining lifetime to achieve u, as follows: α, w (u) = t (u) t Let u 1 = -α, u 2,,u R =1, where R is a small constant, be a set of sampling points (target relative performance values from now on). Define matrices W and V as follows: W i,m = w (u ), if u u, w (u ), otherwise, ISSN: Page 1433

4 V i,m = u, if u u, u, otherwise. W i,m contains the average speed with which application m should execute starting from t now to achieve relative performance u i, and cell V i,m contains the relative performance value u i if it is possible for application m to achieve this performance level u i. If relative performance u i is not achievable by application m, these cells instead contain the average speed with which the application should execute starting from t now to achieve its maximum achievable relative performance, and the value of the maximum relative performance, respectively. For a given w g, there exist two values k and k þ 1 such that: 4. Calculate current utility value of each path and select negotiable paths based on the minimum utility value 5. Select application, ai, in the ranked list of recommended actions and test feasibility of allocating the application ai, on the host, Hj. 6. If utility value of each path is less than threshold of utility value of each path then predict QoS and allocate it best host and exit 7. Else allocate the violated application to the host that has the minimum utility value 4. SIMULATION EXPERIMENTS This section presents the experimental results of the proposed algorithm for balancing the load across different types of workloads. Quality of Service negotiation approach is used for finding the best available host based on resources needs. W, w W, Allocating a CPU power to all jobs will result in a relative performance um for each job m in the range V, u V,.That corresponds to a hypothetical CPU allocation w m in the range W, w W,. At some point the algorithm needs to know the relative performance that each application will achieve (um) if it decides to allocate a CPU power of w g to all applications combined. We must find values w m and um for each application m such that (7) is satisfied and that fall within the ranges described above. The chosen values must also satisfy w = w. Fig.2. Construction of cloud architecture 3.4. Quality of Service Negotiation for Distributed Systems This algorithm is used for finding the feasible host based on the Quality of Service (QoS) parameters and it allocates the best host for the different types of workloads. The steps are: 1. Find a feasible host corresponding to resource needs 2. If a host is feasible, then predict queuing delay and execution time on feasible hosts 3. Else send QoS negotiation requests to all QoS managers Fig.3. Measuring transaction job requirements ISSN: Page 1434

5 Fig.4. Setting Performance goal Fig.5. Job assigned to cloud 5. CONCLUSION In this paper, we present a technique that allows integrated management of heterogeneous workloads composed of transactional applications and long-running jobs, dynamically placing the workloads in such a way as to equalize their satisfaction. We use relative performance functions to make the satisfaction and performance of both workloads comparable. The heuristics used by the algorithm is improved, which resulted in a significant reduction in its computational complexity. The use of QoS negotiation has enabled more effective sharing of system resources in a physical server. It maximizes mixed workload performance while providing service differentiation based on high-level performance goals. The system introduces several novel features. First, it allows heterogeneous workloads to be collocated on any server machine, thus reducing the granularity of resource allocation. Second, the resource utilization is increased. Third, the placement problems can be reduced. REFERENCES [1] David Carrera, Malgorzata Steinder, Ian Whalley, Jordi Torres,and Eduard Ayguade, Autonomic Placement of Mixed Batch and Transactional Workloads, IEEE Transactions on Parallel and Distributed Systems 23, (2012). [2] E. Arzuaga and D.R. Kaeli, Quantifying Load Imbalance on Virtualized Enterprise Servers, Proc.First Joint WOSP/SIPEW Int l Conf.Performance Eng. (WOSP/SIPEW 10), pp , [3] M. Steinder, I. Whalley, D. Carrera, I. Gaweda, and D. Chess, Server Virtualization in Autonomic Management of Heterogeneous Workloads, Proc. IEEE/IFIP 10th Symp. Integrated Management (IM 07), [4] D. Carrera, M. Steinder, I. Whalley, J. Torres, and E. Ayguade, Managing SLAs of Heterogeneous Workloads Using Dynamic Application Placement, Technical Report RC 24469, IBM Research, Jan [5] A. Caniff, L. Lu, N. Mi, L. Cherkasova, and E. Smirni, Efficient Resource Allocation and Power Saving in Multi-Tiered Systems, Proc. 19th Int l Conf. World Wide Web (WWW 10), pp , [6] D.M. David Vengerov, L. Mastroleon, and N. Bambos, Adaptive Data- Aware Utility-Based Scheduling in Resource-Constrained Systems, Technical Report TR , Sun Microsystems, Apr [7] P. Bodı k, R. Griffith, C. Sutton, A. Fox, M.I. Jordan, and D.A. Patterson, Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters, Proc. Conf. Hot Topics in Cloud Computing (HotCloud 09), [8] M. Cardosa, M.R. Korupolu, and A. Singh, Shares and Utilities Based Power Consolidation in Virtualized Server Environments, Proc. IFIP/IEEE Int l Symp. Integrated Network Management (IM 09), pp , [9] X. Wang, D. Lan, G. Wang, X. Fang, M. Ye, Y. Chen, and Q. Wang, Appliance-Based Autonomic Provisioning Framework for Virtualized Outsourcing Data Center, Proc. Fourth Int l Conf. Autonomic Computing (ICAC 07), p. 29, [10] J. Xu, M. Zhao, J. Fortes, R. Carpenter, and M. Yousif, On the Use of Fuzzy Modeling in Virtualized Data Center Management, Proc. Fourth Int l Conf. Autonomic Computing (ICAC 07), p. 25, June Fig.6. Performance comparison ISSN: Page 1435