504 by ides editor

Poster Paper Proc. of Int. Conf. on Advances of Information & Communication Technology in Health Care 2011

A Survey of Job Scheduling in Mobile Ad-hoc Grid for E-healthcare Application Sri Chusri Haryanti1 and Riri Fitri Sari2 Department of Electrical Engineering, Faculty of Engineering, University of Indonesia Indonesia, Depok, Indonesia Email: 1sri.chusri@ui.ac.id, 2riri@ui.ac.id Abstract— Grid computing has been noticed as an important element for e-healthcare system. A mobile ad-hoc grid is a form of grid computing system that is believed will play a key role in the future individualized healthcare. Job scheduling algorithm working for mobile ad hoc grid plays a vital role in improving overall system performance. Low CPU capability, battery constraint and dynamic movement of mobile nodes trigger a requirement of an adaptive and robust job scheduling algorithm for ad hoc grid which is different from job scheduling for infrastructure grid. Several algorithms have been proposed, but most of them only consider simplified characteristics of mobile ad hoc network environment and only minority of them considered scenario of potential failure. This survey aims to provide a comprehensive study of stateof-the-art of job scheduling in mobile ad hoc grid network study. Index Terms— Job Scheduling, Mobile Ad-hoc Network, Grid Computing

I. INTRODUCTION

scheme of conventional grid computing due to the difference of the environment. The challenge in designing job scheduling algorithms for mobile ad hoc grid is associated with node mobility and lack of infrastructure in the network. Considering node mobility and lack of battery power, a node can leave the grid at any time. This fact could increase posibility of failure that can be minimized by employing proper job scheduling scheme. This paper aims to provide a comprehensive study of state-of-the-art of job scheduling in mobile ad hoc grid network which can be used as a basis for further research in the area. II. GRID COMPUTING The earliest definition of grid computing was hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities [6]. A few years later, it was redefined as “coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations (VO).” [7]. Grid is a system that allows resource sharing between nodes that are connected in one network in completing specific jobs [8]. Jobs mean tasks which usually require huge computing power to complete it, so it requires a very long time if it is done only by a single computer (node). In completing the jobs properly and fasterly, it will be divided into smaller task and handed over to the existing nodes in the grid to be accomplished in the same time (a parallel manner). Finally, results of all computing task will be compiled and submitted to the node that request the job to be completed. It is worthy to note that the basic functions of a grid is resource discovery, resource access and negotiation, job scheduling and authentication [9].

The interest of using mobile communication [1, 2, 3] and grid computing system [4, 5] in healthcare has been considerably noticed. Those technologies can improve the quality and efficiency of service delivery to the clients. With the growing need for communication “wherever and whenever”, and because of the flexibility of the networks, today, most users prefer to connect to the mobile and wireless network. In the future, mobile wireless and ad hoc networks seem to be more widely used also in healthcare sector. It will become a reliable network especially in places in which infrastructure network do not exist nor is difficult to find. Mobile ad hoc grid combines grid computing system and mobile ad hoc network. It allows nodes spontaneously form an ad hoc network, forming a grid or join an existing grid. Mobile nodes are also expected to be able to contribute to A. Job Scheduling in Grid Computing the Grid dynamically and be active in the services offered by Grid architecture presented in Figure 1 is a representation other nodes in the Grid. Mobile Ad hoc grids are able to of the grid architecture described in [7]. Job scheduling exists facilitate autonomous interaction without requiring in the collective layer of Grid architecture. Job scheduling is preconfiguration or policy management. Mobile ad hoc grid defined as the mapping of jobs to specific physical resources, would be very useful for e-healthcare emergency. Mobile ad trying to minimize some cost functions determined by the hoc grid can be used for example, to collect and automatically user [10]. The scheduler has the responsibility of selecting process information about a group of people who were injured resources and selecting jobs in such a way that the user and and it can help to give appropriate relief teams and medical application requirements are met [11,12]. A system needs resources. efficient and flexible scheduling, so it can manage requests Job scheduling plays an important role in determining from users. In recent years, infrastructure of grid and overall grid system performance. Job scheduling algorithm performance of its resources have been improved, but the that is appropriate with grid environment will deliver faster performance of the scheduling system is still a continuing job completion time. We can not just use job scheduling 186 © 2011 ACEEE DOI: 02.ICTHC.2011.01. 504

Poster Paper Proc. of Int. Conf. on Advances of Information & Communication Technology in Health Care 2011 challenge in research on the grid [13]. Some examples of recent job scheduling research are [14-19].

on the devices predominant in the grid and relative mobility of the service in the grid into four types: • Fixed wireless grids • Mobile or dynamic wireless grids • Sensor Network Grids • Ad hoc grids Table I provides the comparison among wireless grid computing. (Combination of tables from [25] and [26]) TABLE I. A CATEGORY OF WIRELESS GRID

Figure 1. Grid Architecture [7]

For further discussion, we use a taxonomy of scheduling in grid systems that is used in [20]. Compared with several survey papers on scheduling [10, 20, 21, 22], Krauter et al. in [20] presents a more structured and clear taxonomy of scheduling in grid systems. They divided the taxonomy based on four different criterium. Scheduler Organization Scheduler component can be organized in 3 different ways: centralized scheduling, hierarchical scheduling and decentralized scheduling. State Estimation State estimation is based on mechanism used to estimate the state that affect the implementation of historical and current data storage associated with scheduling. It consists of predictive and non-predictive estimation approach. Rescheduling The term rescheduling here means whether the existing scheduling was reviewed and whether the execution of the work was reassembled. Scheduling Policy Scheduling policies are carried out to determine the relative order of jobs and demand when rescheduling is needed. The taxonomy is focused on the degree to which the scheduling policy can be enhanced by an entity outside the grid.

Fixed wireless grids extend grid resources to wireless devices that are usually static. This type of grid has no difference with traditional wired grid except the communication medium is wireless [23]. Mobile or dynamic wireless grids make services accessible through mobile devices such as PDA and smart phones. It is based on a wireless network in which each cell consists of a number of mobile devices. Mobile devices residing in a cell of wireless networks are coordinated by a central entity that resides at the Access Point/Base Station (BS), in order to perform a task. Mobile devices can be used as both resource consumers and resource providers [27]. Sensor network grid integrates wireless sensor networks and traditional grid computing technologies [28,29]. These are formed from tiny devices that are generally dedicated to a single purpose. Based on wireless sensor network infrastructure, sensor grid adopts the technologies of data grids, computing grids and access grids to storing, processing, presenting and sharing the data collected from the sensors [23]. Ad hoc grid (mobile ad hoc grid) [Figure 2.] is actually an integration of mobile ad hoc network (MANET) with grid functionalities. Mobile ad hoc grid allows nodes spontaneously form an ad hoc network, forming a grid or join an existing grid, dynamically contribute to the Grid, and being active in the services offered by other nodes in the Grid. Mobile ad hoc grid facilitates autonomous interaction without requiring pre-configuration or policy management [30]. The

B. Grid Computing In Mobile Ad-Hoc Network In recent years, the need of wireless grids has increased [23]. To meet these needs, traditional grid technology has been expanded by integrating mobile devices in a network with wireless infrastructure, either as provider or user of resources in grid computing into the Next Generation Grids (NGG) [24]. The fundamental difference between a traditional grid and NGG is the ability of self-management, primarily related to accessibility, user-centricity and dynamic interaction between its nodes. In the context of accessibility, it means that resources in the grid system can be accessed regardless of physical ability to access device information and geographic location. A highly structured network of supercomputers and high-performance workstations that dominates traditional grid usually does not provide such capabilities. Wireless, mobile and ad hoc grid is developed to support accessibility grid [24]. S.S. Manvi in [25] classifies wireless grid computing based © 2011 ACEEE DOI: 02.ICTHC.2011.01.504

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Poster Paper Proc. of Int. Conf. on Advances of Information & Communication Technology in Health Care 2011 tasks and reschedule their executions. To avoid too much of rescheduling on network partitioning, future job scheduling systems are expected to be equipped with partition detection abilities. Fault-tolerant upon Data loss: Data loss can be a critical issue for job scheduling, especially when schedulers accept job submissions from applications or exchange job status with execution sites. For distributed Grid applications loss of data can result in potentially erroneous outcomes, i.e. chain reactions between scheduled successive jobs, in which some jobs in queue have to delay their own processing if the outcomes of their predecessors get lost and need to be retransmitted. Such a situation would require the scheduling system to be fault-tolerant while it is in distributed operation. Several papers [34, 39-43] have proposed and analyzed job scheduling algorithms for mobile ad hoc grid that are expected to be more suitable for the environment. In [35], job scheduling algorithm based of mobile agent approach was proposed and evaluated. Although the results are good enough related to topological changes, data loss caused by node’s fault concerning out of battery has not been considered. Shivle et al. [38] designed six different heuristic approaches of varying time complexities and evaluated all the algorithms in simulation environment. The job scheduling designed was focused on minimizing resources power. According to Hummel and Jelleschitz [39], it is most appropriate to use decentralized scheduling system in ad hoc grid, because of changes in network topology is highly dynamic. To guarantee robustness of the algorithm, they implements fault tolerant mechanisms to deal with failure in finishing jobs in mobile nodes. In [40], Gomes et al. integration of resource discovery and job scheduling are performed in a system called DICHOTOMY. In this system, although there is enough awareness of the mobility of nodes, system’s resistance to network partitions is not yet evaluated. In DICHOTOMY, the nodes are assumed heterogeneous but the algorithm does not cover rescheduling and also fault tolerance against data loss is not considered. Cilku et al. [41] proposed and evaluated five different algorithms. All job scheduling algorithms are based on ad hoc grid layer (AHGL). The algorithms have considered heterogeneity of nodes in the network. In [42], Shah et al. use centralized approach. There is a node that is responsible to manage scheduling. The consideration of using centralized approach is that the need of ad hoc grid is usually only for a relatively short time and involves only a few nodes. Haobo et al. [43] concerned with reliability and security of completing task in mobile ad hoc grid. [43] introduced the trust mechanism and designed a task scheduling algorithm which is based on the tasks trust-demand. The following section presents further explanation and description of each job scheduling algorithm.

example of the implementation of mobile ad hoc grid is in emergency response system [31, 32]. In e-healthcare, ad hoc grid may be required to collect and automatically process information about a group of people who were injured and give better allocation of relief teams and medical resources [31]. Although mobile devices have limitation comparared with wired network devices, these devices are usually equipped with additional devices such as cameras, microphones, GPS receivers and multiple sensors for specific purposes. This motivate the use of mobile ad hoc grid for e-healthcare application.

Figure 2. Mobile Ad-hoc Grid [24]

In order to find the best solution for the existing environment, recently several architecture models of mobile ad hoc grid have been proposed [33-37]. Each architecture was proposed with different background and motivation. III. JOB SCHEDULING IN MOBILE AD HOC GRID Mobile ad hoc networks environment is different from wired network, so Mobile ad hoc grid also has different characteristics from traditional grid. The following properties are worth being considered in designing a job scheduling system for mobile ad-hoc Grids [31]. Alert for Node Mobility: An important factor that influences the success rate and execution time of the scheduled tasks is the mobility of the devices. If the devices in the network are highly mobile during the lifetime of a distributed application running on the Grid, the application may be disrupted due to the reconfiguration of the network topology and previously established connections. The scheduling system should be able to continuously monitor status of the selected execution sites and replace unavailable ones to resume the Grid application. Robust in Network Partitioning: When mobility causes network partitions or disconnections, schedulers and execution sites may not be able to communicate with each other if the partitions break all connections among them. In such cases, the schedulers have to select new resources for submitted © 2011 ACEEE DOI: 02.ICTHC.2011.01.504

A. Wang et al Description: Jobs execution is done with mobile agent assistance [34]. With runtime application support, in a node participate in grid computing, mobile agent can process the 188

Poster Paper Proc. of Int. Conf. on Advances of Information & Communication Technology in Health Care 2011 jobs in two ways, LIFO (Last In First Out) and FIFO (First In First Out). Whenever a job fails, mobile agent has to find another node to execute the job. Advantages:  Enabling flexible handling of various resource sharing policies. Considering each node resource capabilities. Disadvantages: No protection of mobile agent from malicious nodes.

D. Ghomes et al. Description: In [40] the DICHOTOMY protocol is proposed. The main function of this protocol is to allow resource provisioning to be scheduled among the most resourceful nodes in the mobile grid, while mitigating the overhead of discovery messages exchanged among them. Advantages: Allowing an efficient load balancing among the nodes and lowering the average completion time of tasks. Keeping the discovery efficiency at acceptable levels in mobility scenarios. Scaling very well with respect to an increasing number of nodes, both in the total amount of energy savings due to packet transmissions and the distribution of such savings among the nodes. Disadvantages: It only concerns in mapping and schedulling job in initial execution.  It does not concern with interjob dependencies and rescheduling.

B. Shivle et al. Description: The main concern of all the proposed algorithms in [38] is to get the minimum average battery consumption in executing tasks. According to [34], the best result of task scheduling evaluation is using Genetic Algorithm (GA). The method is adapted from genetic process that occurs in life cells to select final task mapping that gives the best average battery consumption. Advantages: Giving the best battery consumption for executing tasks. Disadvantages: Although GA gives the best average consumption, it does not give the minimum time for executing tasks. It has not considered the need of rescheduling due to fault in executing tasks.

E. Cilku et al. Description: Comparing the results of all algorithms simulation in [41], it can be concluded that scheduling based on task transmission and execution time, made the best scheduling by giving the jobs and tasks execution in the shortest time. This algorithm is based on transmission and execution time of each task on every node that is part of Grid ad hoc environment. Tasks are selected in the same order as they arrive in the queue. If the nodes have insufficient resources to execute the tasks then all process of scheduling is stopped. Advantages: Minimum job execution time. Disadvantage:  It does not concern with interjob dependencies and rescheduling.

C. Hummel et al. Description: Mobile node decides autonomously which job to take by matching the job’s requirement against their capabilities and coordinate among each other based on share job queues [39]. The job scheduling algorithm implements reactive and proactive fault tolerance mechanisms to guarantee robustness. The scheduling approach follows the concept of decentralized opportunistic batch scheduling. It assumes equal job priorities and no interjob dependencies. Advantages: Fair job distribution among nodes. Fault tolerance mechanisms assure robustness. Disadvantages: It does not concern with interjob dependencies and rescheduling.

F. Shah et al. Description: In [42], centralized approach is used. There is a node that is responsible to manage scheduling. The consideration of using centralized approach is that the need of ad hoc grid is usually only for a relatively short time and

TABEL II . C OMPARISON OF JOB SCHEDULING IN MOBILE AD-HOC GRID

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Poster Paper Proc. of Int. Conf. on Advances of Information & Communication Technology in Health Care 2011 involves only a few nodes. Rescheduling is driven by changes in network topology. When a resource node becomes inactive in the grid because of its movement, rescheduling of the job is performed. Scheduling algorithm also considers the interjob dependencies that exist between jobs. Inter-dependent jobs are assigned to the closest node. Advantages: Robust due to the concern for inter dependent job and rescheduling jobs. Disadvantages: Lower scalability because of using centralized approach.

REFERENCES

G. Haobo et al. Description: In [43], Haobo et al. concerned with reliability and security of completing task in mobile ad hoc grid. They introduced the trust mechanism and designed a task scheduling algorithm based on the tasks’ trust-demand Advantages: Secure and reliable job execution. Disadvantages:  It does not concern with interjob dependencies and rescheduling. Table II provides a summary of job scheduling in mobile ad hoc grid comparison [34, 39-43] based on the taxonomy as has been presented in section 2.1.3 and in relation to consideration of inter-dependent jobs. CONCLUSIONS In the future, implementation of mobile ad hoc grid in ehealthcare area seems to be necessary. Particularly in emergency situation or lack of infrastructure condition, mobile ad hoc grid could be very beneficial for e-healthcare application because it allows resource sharing between mobile nodes in completing specific jobs. Mobile ad hoc grid is still in the developing stage. The different characteristics between mobile ad hoc network and infrastructure network need more research in developing rules, standards or protocols, which is necessary in the application. Among others, job scheduling gets much attention due to its performance impact to overall system performance. Several job scheduling algorithms for mobile ad hoc grid have been proposed, but most of them only considered simplified characteristics of mobile ad hoc network environment and only minority of them considered scenario of potential failure. Among algorithms that have been proposed, Chhattan Shah et al.’s research covers nearly everything associated with taxonomic job scheduling (Section II) and important aspects that should be considered in designing job scheduling. Future research in this area should be related to the adaptive characteristic of the scheduling scheme and resilience against network partitioning. Job scheduling for mobile ad hoc grid has to take into consideration the effects of mobility of nodes. In respect to the use of mobile ad hoc grid for e-healthcare application, job scheduling scheme should also provide fast completion time and reliability of the jobs. © 2011 ACEEE DOI: 02.ICTHC.2011.01. 504

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