Optimal Model for Scheduling of Computational Grid Entities

Amritava Chaudhuri Tata Consultancy Services Ltd

Kolkata, India amritava.chaudhuri@gmail.com

Debasish Jana TEOCO Software Pvt Ltd

Kolkata, India djana@alumni.uwaterloo.ca

Bijan Bihari Bhaumik Dept of Computer Sc & Engineering

Jadavpur University Kolkata, India

bbbhaumik@yahoo.com Abstract—In computational grid, effective scheduling of job and resource plays an essential role to optimize and enhance the quality of services provided to the service consumers by the service providers. In order to obtain this collaborative harmony, proper utilization and allocation of grid computing entities should be the imperative need of the hour. This paper primarily focuses on the optimal model for scheduling of grid entities among an assortment of conventional methods of job and resource scheduling and concurrent execution on multiple market based economic scheduling models. While highlighting the merits and demerits of some select scheduling models through illustrative simulations in an optimized arrangement, the paper aims to obtain collaborative stability amongst computational grid entities.

Keywords- Computational Grid; Job and Resource Scheduling; Conventional Scheduling Models; Economic Scheduling Models.

I. INTRODUCTION After the introduction of the term ‘Grid’ by Foster et al [4]

in 1998, many technological changes emerged but the concept of service orientation and web services have been well established by several researchers [23]. Grid computing [7] offers a set of software frameworks, infrastructures, and middleware services to allow seamless sharing, aggregation, and selection of resources across multiple heterogeneous control and administrative domains [5][6]. Computational grids and computational clouds have gained popularity among enterprises feeling the need of colossal computational facility. Resource as well as job scheduling and management within grid and cloud backbone are needed to achieve effective results. The computational grid topology primarily comprises of multiple entities viz. service providers, consumers and resource brokers. Service orientation is the essential bond among the service providers, consumers as well as brokers. Consumers avail service via the brokers from the providers obeying a mutually agreed upon service contract. Consumers can also have their specific brokers. The resource brokers interact with trading services for resource negotiations to meet consumer needs. The consumer may run different jobs of varying resource requirements, varied complexity and different execution times. The provider provides resources to be able to execute jobs given by the consumers. Resources demanded by service consumers may be physical (computer, communication networks, database servers, storage disks, specific scientific instruments) or logical (jobs, applications, workflows etc). Producers will have the natural propensity to maximize return

of investment (ROI) whereas the consumers will always to optimize expenses in availing the service. The broker providing the market directory plays the key role. A market directory mediates the conversations between brokers and trading services. On one hand, the current market offers various conventional scheduling models. On the other hand, there is an alternate approach to establish different economic scheduling models. We highlight the merits and demerits of some select scheduling models through illustrative simulations to gain collaborative stability amongst computational grid entities.

In this paper, emphasis has been laid primarily on the different conventional and economic models of job and resource scheduling applicable to computational grid entities. A comparison has been made between some selected techniques via simulations. Section 2 of this paper highlights key conventional job and resource scheduling models along with their advantages and drawbacks. Section 3 features the economic scheduling models. Section 4 refers to the simulations conducted in our experimental testbed and its associated results. Conclusion and future work is present in Section 5 followed by acknowledgement and references.

Figure 1. Computational Grid Topology

II. CONVENTIONAL SCHEDULING MODELS In conventional operating systems, scheduling of resources

require that complete control is obtainable over a resource

governed by policies and methodologies. In case of grid resources, resources may belong to several administrative domains with different authentication an authorization and also resources could be heterogeneous. Lack of centralized control and variation of policies creates difficulties to schedule and assign grid resource. In the context of grid, a scheduler allows consumer to choose desired resources and execution environment in such a way that the resource location can be dynamically determined. Xhafa et al [27] and Sharma et al [17] had surveyed several computational models for grid scheduling problems. In contrast to scheduling in conventional parallel and distributed systems, scheduling is more complex in computational grids. This is because of the fact that resources may join and depart a computational grid dynamically in a non-deterministic way. Prediction of waiting as well as usage time is seldom possible. Policy restrictions in terms of resource usage limit may restrict resources to be scheduled. The inherent stochastic behavior in the dynamic on-demand service model of a grid environment causes the non-deterministic utilization and usage of grid resources. Resources may be selected based on application requirements in symmetric or asymmetric matchmaking way. In symmetric matchmaking, both the provider and consumer of the resource service must agree on some agreed upon criteria based on select attributes. In asymmetric matchmaking, domain background knowledge, matchmaking rules, ontology prevail.

Some key job scheduling techniques applicable in the context of grid are highlighted below along with their merits and demerits.

A. Grouping-based Fine-grained Job Scheduling (GFJS) The model [10] is driven by resource characteristics. It is an

amalgamation of Greedy and FCFS algorithms. In this technique, fine grained jobs are grouped together to form coarse grained and thereafter are allocated to resources according to their bandwidth and processing capabilities. Maximization of utilization of system resources and reduction in job execution time, total processing time and network latency are some of the key merits of this model [17]. However, there is an underlying overhead associated with job grouping and resource selection. Memory size constraints are also not considered.

B. Hierarchical Job Scheduling (HJS) The technique [16] [17] features two level of scheduling –

global and local. Global policy ensures scheduling through a single or separate queue by using SJF (Shortest Job First), FCFS (First Come First Serve) and FF (First Fit). Local queue uses only one queue with either one of SJF, FCFS and FF. Global scheduler ensures mapping of resource requested to available before allocation, thereby assuring effective utilization. The method provides effective load balancing and reduces overall turnaround time maximizing system utilization. The scheduling ensures maintaining multiple queues for addressing high system loads. However, starvation problem may occur if a long running job gets executed in a queue using SJF policy and may result in underutilization of grid resources. The technique is unsuitable to address the dynamic nature of grid topology.

C. Highest Response Next Scheduling (HRN) This is a classical non-preemptive job scheduling algorithm

[22]. In this technique, as the wait time becomes longer, the priority increases. Thus, a long waiting job competes with jobs with estimated shorter run times. As stated by Sharma et al [17] and also substantiated by Somasundaram et al [20], HRN considers dynamic priority of the job at hand and capability of the processors.

timeexecution estimated time waiting timeexecution estimatedPriority +=

The process with the highest priority value is chosen to execute. Process starvation is thereby prevented as there is no indefinite deferral. It thus provides more response with time, memory and CPU requirements. The approach is highly adaptive with no loss of performance. It facilitates effective resource utilization, prioritization and better job completion. The method covers the shortcomings of SJF (Shortest Job First) and FCFS (First Come First Serve). However, prioritization becomes difficult when the number of jobs increases, resulting in higher turnaround time and in effect, underutilization of CPU and memory.

D. Job Schedule Model Based on grid environment (JSMB) The method [26] proposed by Wu et al categorizes the grid

nodes as supervisor, supervisor backup, and execute. Two scheduling modules are followed: supervisor schedule module residing in supervisor node and execute schedule module residing both in the supervisor backup node and in each execute node. The maximum processor utilization and throughput scheduling algorithm proposed by Wu et al [26] addresses maximum utilization of the processor as well as throughput with minimum turnaround time. It involves high communication overhead.

E. Optimal Resource Constraint Scheduling (ORC) Somasundaram et al [19] described ORC technique as the

allocation of jobs varying according to capability of the available processors. It is an amalgamation of best-fit algorithm with Round-Robin scheduling to ensure appropriate distribution of jobs [17]. It ensures better load balancing. It can handle more number of jobs and thereby overcome the shortcomings of HRN. It reduces turnaround time and average queue waiting time and avoids starvation problem for jobs. The technique has a high communication overhead.

F. Resource CoAllocation for Scheduling Tasks with Dependencies (RCSTD) CoAllocation of resources is possible when a given set of

resources is available for simultaneous use by several tasks. Some tasks may have dependencies on some other tasks. For example, output of one task may be fed as input to another task. The data dependencies between the tasks represented by a Directed Acyclic Graph (DAG) are used to decide upon the co-allocation of the resources. The model [12][17] ensures minimum time for task execution and maximum load balancing. It provides effective cohesion and adhesion for grid clusters. Decentralization assures non occurrence of single

point failure, although, this has high communication overhead associated with grouping of jobs.

G. Scheduling Framework for Bandwidth-Aware Job Grouping (SFBAJG) The method [8] checks for computational and

communication capabilities of resources. Effective job grouping against selected resources maximizes processor utilization thereby minimizing CPU wastage. Grouping also reduces network latencies thereby reducing total processing time [17]. There is a sublime overhead associated with job grouping and resource selection. Memory size constraints and dynamic resource characteristics are not taken into account.

Highlighted below are some key resource scheduling techniques along with their merits and demerits.

A. Agent Based Resource Management with Alternate Solution (ABRMAS) Often high-end resources become unavailable causing the

grid users facing difficulty to complete the job at hand. Whenever resource discovery fails, ABRMAS technique [13] enables the resource provider agents providing an alternate solution by negotiating for best possible resource. This helps to reduce delay overhead in waiting for the unavailable resource and enhances the system’s efficiency. Efficient resource discovery and fallback plan are available. However, deploying agents across heterogeneous administrative domains is not always possible.

B. Agent based Resource Discovery with Negotiated Alternate Solution (ARDNAS) In ARDNAS method [14], based on specialization of

services, resource provider agents are classified into several groups. Resource request is routed to a specific agent group to increase the efficiency. This causes reduction of the overhead delay because of unavailable resources. Agent deployment across heterogeneous domains is difficult.

C. New Resource Mechanism with Negotiate Solution (NRMNS) The algorithm [25] ensures agent based resource discovery

on failure. It aids for maximum profit for resource provider and feedback capability to cope up with dynamically changing grid terrain. Resource discovery is aborted when resource broker refuses to decrease the resource cost [17].

D. Research on Novel Dynamic Resource Management (RNDRM) The technique [9] uses a heap-sort tree (HST) for

computing available computational power of the nodes as well as the entire Grid System. Node with highest computational power is considered the root node of HST. The model is scalable, robust, fault tolerant and high performing [17]. It provides dynamic status information in fast changing topology. However the job waiting time is high after employing this method, resource utilization issues may also creep up and the algorithm is also not active for job submission failure cases.

Presented next is a comparative analysis on different job and resource scheduling techniques with respect to scheduling type, algorithm, key merits and also their respective demerits.

TABLE I. COMPARATIVE ANALYSIS OF SCHEDULING MODELS

Model Attributes

Scheduling Type Technique Merits Demerits

GFJS Job Greedy and FCFS

Maximum utilization of system resources, minimum job execution time, overall processing time and network latency

Overhead associated with job grouping and resource selection, memory size constraints

HJS Job SJF, FCFS and FF

Effective load balancing, reduction in overall turnaround time, maximum system utilization

Starvation problem for long running jobs, unsuitable for dynamic topology of grids

HRN Job Non preemptive

Highly adaptive, effective resource utilization, prioritization, better job completion

Difficulty in job prioritization with large number of jobs

JSMB Job

Processor utilization and throughput scheduling

Maximum processor utilization, minimum turnaround time of jobs

High communication overhead

ORC Job Best fit and Round Robin

Better load balancing, handling more jobs, reduced turnaround time and avoidance of starvation problem

High communication overhead

RCSTD Job Resource Coallocation

Minimum job execution time, maximum load balancing

High communication overhead

SFBAJG Job Grouping Minimized CPU wastage, effective job grouping

Overhead associated with job grouping and resource selection

ABRMAS Resource Agent Based

Efficient resource discovery, reduced delay overhead, enhanced system efficiency

Difficulty in deploying agents across disparte administrative domains

ARDNAS Resource Agent Based Efficient resource discovery, reduced delay overhead

Difficulty in deploying agents across heterogeneous administrative domains

NRMNS Resource Negotiate solution

Efficient resource discovery

Refusal of resource broker can create problems

RNDRM Resource HST based computing

Scalable, robust, fault tolerant and high performing

High job waiting time, complex resource management

III. ECONOMIC SCHEDULING MODELS As per the Computational Grid topology (Fig. 1), the

economic models can effectively operate between Resource Broker, Market Directory and Trading Services. Economic models would be employed by trading services. Some of the key economic models along with their key features is highlighted below.

A. Auction Model The key players in auction model (Fig. 2) are service

provider (seller), multiple consumers (buyers) and auctioneer (mediator). The model [2] operates in one to many negotiation modes. Depending on the type of offers made (i.e. back and forth, counter offers), the auctions can be open or closed. There are various variants of auction models available in the market. English Auction (first-price open cry) is an open auction where the bid starts with a low price and all bidders increase their bids to exceed the offers made by other bidder. The auction stops when the highest bid takes place and no other bidder is willing to opt for more. Perhaps the most dynamic and flexible auction model is the Double Auction technique. Buy and Sell orders can be submitted at anytime and a deal is only finalized when there is a compatible match between a buy and a sell with respect to price and requirements.

Figure 2. Auction Model

Just reverse to the English Auction is the Dutch auction where bid starts with a high price (i.e. a price much greater than the expected market value) and then there is a progressive decrease in price till one of the buyers takes the item on the current price. Few other closed bid options are First-price sealed bid auction and Vickrey [2][24] (i.e. Second-price sealed bid) auction. In both the models, the bidder submits a bid without knowing the details regarding the bid submitted by other bidders. The highest bidder wins the bid either at the price quoted (i.e. for First-price option) or at the price quoted by the second highest bidder (i.e. for Second-price option).

B. Bargaining Model In this model [2], brokers do not pay access prices, rather

brokers bargain with service providers for lower price and higher usage. Negotiation continues until objective functions

are satisfied. Negotiation may result in trade off with resource quality for cheaper price. This model (Fig. 3) is employed when market supply-and-demand and service prices cannot be clearly determined

Figure 3. Barganining Model

C. Commodity Market (Flat or Supply-and-Demand Driven Pricing) Model

Figure 4. Commodity market

In this model (Fig. 4), resource owners specify prices and charge according to usage. Flat or variable pricing policy [11][15] is adopted depending on resource supply and demand [2][3]. In Flat Price scheme, price remains same irrespective of service quality. Price is unaffected by supply and demand situations. Price is a function of flat fee, usage duration, subscription, demand and supply. Resource Value is a function of resource strength, cost of physical resources, service overhead, demand, value perceived by the user, preferences

D. Posted Price Model

Figure 5. Posted price model

This is similar to Commodity Market Model with additional advertisement [2] for special offers on services (Fig. 5). It drives more users with attractive deals (holiday usage offers), raises market share. Brokers can deal with posted prices as they are cheaper than regular prices. In turn the brokers can also avoid dealing directly with the Service Providers for price. Brokers look at the Market Directory where the Service Providers post their special offers and conditions and determine if any of the posted matches its requirements.

E. Tender/Contract-Net Model

Figure 6. Tender/Contract Net Model

In this model (Fig. 6), a manager [2] [18] is a user or resource broker that requires a task to be solved. A contractor is a resource that is capable to solve the task. The brokers have option of availing services from other Service Providers, in case, an existing Service provider is unable to deliver. If a more capable Service Provider remains occupied at the time for deciding the bid result, a trade off is made to avail another Service Provider of lesser capability. It is also discretionary on

the part of the Resource Broker Manager to inform stakeholder contractors that an award has already been made.

IV. SIMULATIONS AND RESULTS A number of simulation experiments have been conducted

to evaluate the performance associated with deal finalization for two existing economic scheduling models viz. Auction Model and Tender/Contract Net Model. We have used GridSim [1] for conducting necessary simulations in this regard.

Figure 7. Plot keeping number of auctioneers, brokers fixed

Figure 8. Plot keeping varying number of auctioneers and resource brokers

In the Auction model, buyers collaborate with sellers via auctioneers for finalizing a deal, similarly in the Tender/Contract model, consumers engage Resource Brokers/Mangers for deciding and awarding a bid to Service Providers or contractors. The simulation experiments have been conducted with multiple key players (viz. buyers, sellers, auctioneers, consumers, managers and contractors) interacting through multiple bids along with their responses in a collaborative computational grid platform.

The simulation results in Fig. 7 (AB implies auctioneers or brokers, and SC implies sellers or consumers) shows that with increasing number of buyers and contractors in a collaborative grid environment, the average deal finalization time increases while keeping the number of auctioneers and resource broker managers constant and increasing the number of sellers and

consumers. The situation causes a delay in finalizing a deal and allocating proper resources in time. However, the situation radically changes when the number of auctioneers and resource manager brokers are varied while keeping the number of sellers and consumers fixed for increasing number of buyers and contractors. The result shows remarkable improvement in average deal finalization time with increasing number of auctioneers and resource manager brokers as highlighted in Fig. 8. The increase in number of auctioneers/brokers helps to overcome the limitation for effective resource allocation and utilization.

V. CONCLUSION AND FUTURE WORK By amalgamating the features of Auction model and

Tender/Contract Net model, the mediating layer comprising of Auctioneer and Resource Broker Manager could be optimized in such a fashion such that a single mediating entity can perform the role of an auctioneer or resource broker based on the need of the hour. On top of this, involving more number of such mediating decision making entities can also enhance the deal/bid finalization process. Also effectively controlling the request and response mechanism for these entities could address the limitation associated with broadcasting the results associated with a bid or deal to all stakeholders. Our current research is focused towards achieving these goals for maintaining collaborative harmony amongst computational grid entities.

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