os paper.pdf

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Multi-hybrid job scheduling for fault tolerable distributed

Computing in policy constrained resource networks

Proposed Title

List Scheduling for fault tolerable distributed

Computing in policy constrained resource networks

Ejaz Ul Haq* Dr. Babar Nazir

COMSATS Institute of Information Technology, Abbottabad

Abstract:Computing Grid is a high performance computing environment that allows sharing of

geographically distributed resources across multiple administrative domains and used to solve

large scale computational demands. To achieve the promising potentials of computational

grids, job scheduling is an important issue to be considered. This paper addresses scheduling

problem of independent tasks on computational grids. A Multihybrid job Scheduling and List

Scheduling is presented to reduce overall execution time of task.

1. Introduction:

The Concept of Distributed computing is motivated by wide sharing of resources has evolved to

be mainstream technologies for enabling large-scale virtual organization. During the beginning

to mid-1990’s distributed computing was count a big area on the research projects. Researchers

working on it to develop a tool that will allow it to act like a single big computer. [1]

One of the most challenging job in Distributed Computing is Job allocation, to do this we should

develop such a system that ensure that elastically allocate computing resource to jobs despite

the unpredictable occurrence of resource failures. The purpose of a multihybrid job scheduling



scheme is to guarantee for the problem MPDP [1]. It has been reported that over 75% and 70%

of the resources have failure rates of about 20% and 40% in workload archives such as DEUG,

and UCB and SDSC [2], respectively. From these application-level traces, we found that most

resources have relatively high failure probabilities. It is also recognized that failures can

significantly affect scheduling performance and that the large number of job failures is still

caused by resource fluctuations and unavailability, as discussed in [3].

Related Work:

There are some scheduling approaches recently studied about job scheduling in distributed

computing environment. In [4] The middleware support for the Distributed Computing in term

of scheduling has not been much studied. In addition to process utilization it is important to

consider the response time of job in the performance of grid scheduling strategies. In this paper

author propose distributed scheduling algorithm that use multiple simultaneous requests at

different sites. In [5] Job Scheduling in cloud computing can be divided to two main groups;

Batch mode heuristic scheduling algorithms (BMHA) and online mode heuristic algorithms. In

BMHA jobs are queued and collected into a set when they arrive in the system, algorithm start

after a defined period of time. E.g. FCFS, Round Robin scheduling algorithm. Another Paper

discussed that Scheduling onto a Grid has three main phases [6]. Phase one is resource

discovery [7], which generates a list of potential resources. Phase two involves the publication

of information about those resources and choosing the best to send a task. In [8] phase three

the job is executed.

Since the grid resources are very heterogeneous and have different processing capabilities, the

task scheduling problem becomes more important in grids [9]. The total make span of the grid

is known as one of the most important system-oriented performance measures in which

minimizing it can help the system to seem more effective and useful [10]. Particle Swarm

Optimization (PSO) algorithm could be implemented and applied easily to solve various

function optimization problems or the problems that can be transformed to optimization

problems. Our approach is to dynamically generate an optimal schedule so as to complete the

tasks within a minimum period of time as well as utilizing all the resources.



Author used Discrete PSO (DPSO) as it has a faster convergence rate than Genetic Algorithm

(GA). Also, it has fewer primitive mathematical operators than both GA and Simulated

Annealing (SA), making applications less dependent on parameter fine-tuning. It allows us to

use the fitness function directly for the optimization problem. Moreover, using discrete

numbers, we can easily correlate particle’s position to task-resource mappings [11]. But, since

the ability of local search in PSO is weak and also the possibility of becoming trapped in the

local optimum is high, in this paper, its combination Min-min algorithm is used to improve its

performance in finding solution. The proposed Hybrid DPSO (HDPSO) is decreased make span.

In [12] author evaluated four scheduling methods with different number tasks and resources

based on total completion time.

2. MJS Scheduling Model:

A multihybrid policy decision problem (MPDP) on the primary-backup fault tolerance model and

discuss how an optimal solution can be quantified in terms of scheduleing quality. To solve

MPDP effectively, we propose a new MJS scheme that finds an optimal schedule even in a large

search space by using stochastic search operations of mGA within relatively low and acceptable

complexity to convergence. The mGA-based MJS scheme demonstrates high fault tolerance

without sacrificing the other objectives, such as makespan and load balance, unlike the static

approaches and deterministic algorithms (e.g., minmin [13]), even in the policy-constrained

DCS.

3. Problem Statement:

MJS Scheme required different batch sources from different heterogeneous systems; therefore

it required more bandwidth due to batch jobs. Also it involve complex computational algorithm

as it use different algorithm for searching, batching and then transfer to processor for

execution. These entire algorithms involve processor complexity. Execution time of the tasks

and the communication cost which is the cost to transmit messages from a task on one

processor to a succeeding task on a different processor.



4. Proposed Solution:

Let we call List scheduling technique for our proposed work solution. This techniques assign a

priority to each task to be scheduled and then sort the list of tasks in decreasing priority. As

processors become available, the task with highest priority is processed and removed from thelist. If two or more tasks have the same priority, the selection which is performed among the

candidate tasks is typically random [14]. The problem with list scheduling algorithms is that the

priority assignment may not always order the tasks for scheduling according to their relative

importance.

Figure: List Scheduling (An Idea)

The question is how it overcome the problem of MJS scheme, as in our proposed solution less

bandwidth is using as compared to MJS scheme as in this scheme we process jobs in the form

of list rather than in the form of batch jobs, Also proposed solution doesn’t involve any complex

algorithms.

4.1 Introduction to Proposed Solution:

Executing large-scale applications in distributed computing infrastructures (DCI), for example

modern Cloud environments, involves optimization of several conflicting objectives such as

makespan, reliability, energy, or economic cost. Despite this trend, scheduling in

heterogeneous DCIs has been traditionally approached as a single or bi-criteria optimization

problem. In this paper, we propose a generic multi-objective optimization framework



supported by a list scheduling heuristic for scientific workflows in heterogeneous DCIs. The

algorithm approximates the optimal solution by considering user-specified constraints on

objectives in a dual strategy: maximizing the distance to the user's constraints for dominant

solutions and minimizing it otherwise. We instantiate the framework and algorithm for a four-

objective case study comprising makespan, economic cost, energy consumption, and reliability

as optimization goals. We implemented our method as part of the ASKALON environment

(Fahringer et al., 2007) for Grid and Cloud computing and demonstrate through extensive real

and synthetic simulation experiments that our algorithm outperforms related bi-criteria

heuristics while meeting the user constraints most of the time.

Figure: Flow Chart of List Scheduling

4.2 The List Scheduling Algorithm:

Here we describe our implementation of list scheduling. Priorities are assigned to each node in

the graph. There are several different heuristics that can be used to assign priorities. A common



and effective strategy is to use the latency weighted depth of the node [15]. The depth of a

node n is the length (number of nodes) of the longest path in the from n to some leaf (including

n and the leaf.) The latency weighted depth is computed the same way, but the nodes along the

path are weighted using the latency of the operation the node represents. The following

formula summarizes the priority computation for a node n:

priority(n) = max ( l leaves p paths(n,...,l) Summation( pi=n latency(pi) !)

Algorithm:

Cycle=0

Ready-list=root nodes

Inflight-list=empty list

While(ready-list or inflight-list not empty and an issue slot is avalaible)

For op=(all nodes in read-list in descending priority order)

Remove op from ready-list and add to inflight-list

Add op to schedule at time cycle

If(op has an outgoing anti-edge)

Add all targets of op anti-edge that are ready-list

End if

End for Cycle=cycle+1

For op=(all nodes in inflight-list)

If (op finishes out time cycle)

Remove op from inflight-list

Check nodeswaiting for op and add to ready-list

If all operands available

End if End for

End while



References:

[1] Pinky Rosemarry, Ravinder Singh, Payal Singhal and Dilip Sisodia

Grouping based Job Scheduling algorithm using priority queue and hybrid algorithm in Grid

Computing

[2] Derrick Kondo, Bahman Javadi, Alexandru Iosup, Dick Epema, The failure trace archive:

enabling comparative analysis of failures in diverse distributed systems, in: Proc. the 10th

IEEE/ACM 1172 International Conference on Cluster, Cloud and Grid Computing

[3] Yulai Yuan, Yongwei Wu, Qiuping Wang, Guangwen Yang, Weimin Zheng,

Job failures in high performance computing systems: a large scale empirical study, Comput.

Math. Appl. 63 (2012) 365–377.

[4] Vijay Subramani Rajkumar Kettimuthu Srividya Srinivasan P. Sadayappan

Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests

[5] Shamsollah Ghanbari,*,Mohamed Othman

A Priority based Job Scheduling Algorithm in Cloud Computing

[6] Schopf, J.M.

A General Architecture for Scheduling on the Grid. Special Issue on Grid Computing, J.

Parallel and Distributed Computing 2002.

[7] Naghibzadeh, M.—Bagheri, E.

A New Approach to Resource Discovery and Dissemination for Pervasive Computing

Environments Based on Mobile Agents. Electrical and Computer Engineering, Vol. 14, 2006,

No. 6.

[8] Antonio Javier Sanchez Santiago, Antonio Jesus

A MULTI-CRITERIA META-FUZZY-SCHEDULER FOR INDEPENDENT TASKS IN GRID COMPUTIN

[9] N. Fujimoto and K. Hagihara,

A comparison among grid scheduling algorithms for independent coarse-grained tasks”,

International Symposium on Applications and the Internet Workshops (SAINTW’04), (2004),

[10] R. Entezari-Maleki and A. Movaghar,

A probabilistic task scheduling method for grid environments”, Future Generation



Computer Systems, vol. 28, (2012), pp. 513–524, Elsevier.

[11] S. Pandey, L. Wu, S. Guru and R. Buyya,

A particle swarm optimization (PSO)-based heuristic for scheduling workflow applications

in cloud computing environments”, (2009).

[12] Maryam Karimi1,* and Homayoon Motameni2

Tasks Scheduling in Computational Grid using a Hybrid Discrete Particle Swarm

Optimization

[13] Tracy D. Braun, Howard Jay Siege, Noah Beck, Comparison of eleven static heuristics for

mapping a class of independent tasks onto heterogeneous distributed computing systems,

[14] Dr.G.Padmavathi, Mrs.S.R.Vijayalakshmi, “A Performance Study of GA and LSH in

Multiprocessor Job Scheduling”,

[15] Phillip B. Gibbons and Steven S. Muchnick. Efficient instruction scheduling for a pipelined

architecture. SIGPLAN Notices, 21(7):11–16, July 1986. Proceedings of the ACM SIGPLAN

Symposium on Compiler Construction

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